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"$:/Acknowledgements": {
"title": "$:/Acknowledgements",
"type": "text/vnd.tiddlywiki",
"text": "TiddlyWiki incorporates code from these fine OpenSource projects:\n\n* [[The Stanford Javascript Crypto Library|http://bitwiseshiftleft.github.io/sjcl/]]\n* [[The Jasmine JavaScript Test Framework|http://pivotal.github.io/jasmine/]]\n* [[Normalize.css by Nicolas Gallagher|http://necolas.github.io/normalize.css/]]\n\nAnd media from these projects:\n\n* World flag icons from [[Wikipedia|http://commons.wikimedia.org/wiki/Category:SVG_flags_by_country]]\n"
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"$:/core/copyright.txt": {
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"text": "TiddlyWiki created by Jeremy Ruston, (jeremy [at] jermolene [dot] com)\n\nCopyright (c) 2004-2007, Jeremy Ruston\nCopyright (c) 2007-2017, UnaMesa Association\nAll rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n* Redistributions of source code must retain the above copyright notice, this\n list of conditions and the following disclaimer.\n\n* Redistributions in binary form must reproduce the above copyright notice,\n this list of conditions and the following disclaimer in the documentation\n and/or other materials provided with the distribution.\n\n* Neither the name of the copyright holder nor the names of its\n contributors may be used to endorse or promote products derived from\n this software without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 'AS IS'\nAND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\nIMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\nFOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\nDAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\nSERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\nCAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\nOR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\nOF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE."
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"text": "<svg class=\"tc-image-underline tc-image-button\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 128 128\">\n <g fill-rule=\"evenodd\">\n <path d=\"M7,117.421488 L121.247934,117.421488 L121.247934,128 L7,128 L7,117.421488 Z M104.871212,98.8958333 L104.871212,0 L88.6117424,0 L88.6117424,55.8560606 C88.6117424,60.3194668 88.0060035,64.432115 86.7945076,68.1941288 C85.5830116,71.9561425 83.7657949,75.239885 81.342803,78.0454545 C78.9198111,80.8510241 75.8911167,83.0189317 72.2566288,84.5492424 C68.6221409,86.0795531 64.3182067,86.844697 59.344697,86.844697 C53.0959284,86.844697 48.1862552,85.0593613 44.6155303,81.4886364 C41.0448054,77.9179114 39.2594697,73.0720003 39.2594697,66.9507576 L39.2594697,0 L23,0 L23,65.0378788 C23,70.3939662 23.5419769,75.2717583 24.625947,79.6714015 C25.709917,84.0710447 27.5908957,87.864883 30.2689394,91.0530303 C32.9469831,94.2411776 36.4538925,96.6960141 40.7897727,98.4176136 C45.125653,100.139213 50.545422,101 57.0492424,101 C64.3182182,101 70.630655,99.5653553 75.9867424,96.6960227 C81.3428298,93.8266902 85.742407,89.33147 89.1856061,83.2102273 L89.5681818,83.2102273 L89.5681818,98.8958333 L104.871212,98.8958333 Z\"></path>\n </g>\n</svg>"
},
"$:/core/images/unfold-all-button": {
"title": "$:/core/images/unfold-all-button",
"tags": "$:/tags/Image",
"text": "<svg class=\"tc-image-unfold-all tc-image-button\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 128 128\">\n <g fill-rule=\"evenodd\">\n <rect x=\"0\" y=\"0\" width=\"128\" height=\"16\" rx=\"8\"></rect>\n <rect x=\"0\" y=\"64\" width=\"128\" height=\"16\" rx=\"8\"></rect>\n <path d=\"M85.598226,8.34884273 C84.1490432,6.89863875 82.1463102,6 79.9340286,6 L47.9482224,6 C43.5292967,6 39.9411255,9.581722 39.9411255,14 C39.9411255,18.4092877 43.5260249,22 47.9482224,22 L71.9411255,22 L71.9411255,45.9929031 C71.9411255,50.4118288 75.5228475,54 79.9411255,54 C84.3504132,54 87.9411255,50.4151006 87.9411255,45.9929031 L87.9411255,14.0070969 C87.9411255,11.7964515 87.0447363,9.79371715 85.5956548,8.34412458 Z\" transform=\"translate(63.941125, 30.000000) scale(1, -1) rotate(-45.000000) translate(-63.941125, -30.000000) \"></path>\n <path d=\"M85.6571005,72.2899682 C84.2079177,70.8397642 82.2051847,69.9411255 79.9929031,69.9411255 L48.0070969,69.9411255 C43.5881712,69.9411255 40,73.5228475 40,77.9411255 C40,82.3504132 43.5848994,85.9411255 48.0070969,85.9411255 L72,85.9411255 L72,109.934029 C72,114.352954 75.581722,117.941125 80,117.941125 C84.4092877,117.941125 88,114.356226 88,109.934029 L88,77.9482224 C88,75.737577 87.1036108,73.7348426 85.6545293,72.2852501 Z\" transform=\"translate(64.000000, 93.941125) scale(1, -1) rotate(-45.000000) translate(-64.000000, -93.941125) \"></path>\n </g>\n</svg>"
},
"$:/core/images/unfold-button": {
"title": "$:/core/images/unfold-button",
"tags": "$:/tags/Image",
"text": "<svg class=\"tc-image-unfold tc-image-button\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 128 128\">\n <g fill-rule=\"evenodd\">\n <rect x=\"0\" y=\"0\" width=\"128\" height=\"16\" rx=\"8\"></rect>\n <path d=\"M85.598226,11.3488427 C84.1490432,9.89863875 82.1463102,9 79.9340286,9 L47.9482224,9 C43.5292967,9 39.9411255,12.581722 39.9411255,17 C39.9411255,21.4092877 43.5260249,25 47.9482224,25 L71.9411255,25 L71.9411255,48.9929031 C71.9411255,53.4118288 75.5228475,57 79.9411255,57 C84.3504132,57 87.9411255,53.4151006 87.9411255,48.9929031 L87.9411255,17.0070969 C87.9411255,14.7964515 87.0447363,12.7937171 85.5956548,11.3441246 Z\" transform=\"translate(63.941125, 33.000000) scale(1, -1) rotate(-45.000000) translate(-63.941125, -33.000000) \"></path>\n <path d=\"M85.6571005,53.4077172 C84.2079177,51.9575133 82.2051847,51.0588745 79.9929031,51.0588745 L48.0070969,51.0588745 C43.5881712,51.0588745 40,54.6405965 40,59.0588745 C40,63.4681622 43.5848994,67.0588745 48.0070969,67.0588745 L72,67.0588745 L72,91.0517776 C72,95.4707033 75.581722,99.0588745 80,99.0588745 C84.4092877,99.0588745 88,95.4739751 88,91.0517776 L88,59.0659714 C88,56.855326 87.1036108,54.8525917 85.6545293,53.4029991 Z\" transform=\"translate(64.000000, 75.058875) scale(1, -1) rotate(-45.000000) translate(-64.000000, -75.058875) \"></path>\n </g>\n</svg>"
},
"$:/core/images/unlocked-padlock": {
"title": "$:/core/images/unlocked-padlock",
"tags": "$:/tags/Image",
"text": "<svg class=\"tc-image-unlocked-padlock tc-image-button\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 128 128\">\n <g fill-rule=\"evenodd\">\n <path d=\"M48.6266053,64 L105,64 L105,96.0097716 C105,113.673909 90.6736461,128 73.001193,128 L55.998807,128 C38.3179793,128 24,113.677487 24,96.0097716 L24,64 L30.136303,64 C19.6806213,51.3490406 2.77158986,28.2115132 25.8366966,8.85759246 C50.4723026,-11.8141335 71.6711028,13.2108337 81.613302,25.0594855 C91.5555012,36.9081373 78.9368488,47.4964439 69.1559674,34.9513593 C59.375086,22.4062748 47.9893192,10.8049522 35.9485154,20.9083862 C23.9077117,31.0118202 34.192312,43.2685325 44.7624679,55.8655518 C47.229397,58.805523 48.403443,61.5979188 48.6266053,64 Z M67.7315279,92.3641717 C70.8232551,91.0923621 73,88.0503841 73,84.5 C73,79.8055796 69.1944204,76 64.5,76 C59.8055796,76 56,79.8055796 56,84.5 C56,87.947435 58.0523387,90.9155206 61.0018621,92.2491029 L55.9067479,115.020857 L72.8008958,115.020857 L67.7315279,92.3641717 L67.7315279,92.3641717 Z\"></path>\n </g>\n</svg>"
},
"$:/core/images/up-arrow": {
"created": "20150316000544368",
"modified": "20150316000831867",
"tags": "$:/tags/Image",
"title": "$:/core/images/up-arrow",
"text": "<svg class=\"tc-image-up-arrow tc-image-button\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 128 128\">\n<path transform=\"rotate(-135, 63.8945, 64.1752)\" d=\"m109.07576,109.35336c-1.43248,1.43361 -3.41136,2.32182 -5.59717,2.32182l-79.16816,0c-4.36519,0 -7.91592,-3.5444 -7.91592,-7.91666c0,-4.36337 3.54408,-7.91667 7.91592,-7.91667l71.25075,0l0,-71.25074c0,-4.3652 3.54442,-7.91592 7.91667,-7.91592c4.36336,0 7.91667,3.54408 7.91667,7.91592l0,79.16815c0,2.1825 -0.88602,4.16136 -2.3185,5.59467l-0.00027,-0.00056l0.00001,-0.00001z\" />\n</svg>\n \n"
},
"$:/core/images/video": {
"title": "$:/core/images/video",
"tags": "$:/tags/Image",
"text": "<svg class=\"tc-image-video tc-image-button\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 128 128\">\n <g fill-rule=\"evenodd\">\n <path d=\"M64,12 C29.0909091,12 8.72727273,14.9166667 5.81818182,17.8333333 C2.90909091,20.75 1.93784382e-15,41.1666667 0,64.5 C1.93784382e-15,87.8333333 2.90909091,108.25 5.81818182,111.166667 C8.72727273,114.083333 29.0909091,117 64,117 C98.9090909,117 119.272727,114.083333 122.181818,111.166667 C125.090909,108.25 128,87.8333333 128,64.5 C128,41.1666667 125.090909,20.75 122.181818,17.8333333 C119.272727,14.9166667 98.9090909,12 64,12 Z M54.9161194,44.6182253 C51.102648,42.0759111 48.0112186,43.7391738 48.0112186,48.3159447 L48.0112186,79.6840553 C48.0112186,84.2685636 51.109784,85.9193316 54.9161194,83.3817747 L77.0838806,68.6032672 C80.897352,66.0609529 80.890216,61.9342897 77.0838806,59.3967328 L54.9161194,44.6182253 Z\"></path>\n </g>\n</svg>"
},
"$:/core/images/warning": {
"title": "$:/core/images/warning",
"tags": "$:/tags/Image",
"text": "<svg class=\"tc-image-warning tc-image-button\" width=\"22pt\" height=\"22pt\" viewBox=\"0 0 128 128\">\n <g fill-rule=\"evenodd\">\n <path d=\"M57.0717968,11 C60.1509982,5.66666667 67.8490018,5.66666667 70.9282032,11 L126.353829,107 C129.433031,112.333333 125.584029,119 119.425626,119 L8.57437416,119 C2.41597129,119 -1.43303051,112.333333 1.64617093,107 L57.0717968,11 Z M64,37 C59.581722,37 56,40.5820489 56,44.9935776 L56,73.0064224 C56,77.4211534 59.5907123,81 64,81 C68.418278,81 72,77.4179511 72,73.0064224 L72,44.9935776 C72,40.5788466 68.4092877,37 64,37 Z M64,104 C68.418278,104 72,100.418278 72,96 C72,91.581722 68.418278,88 64,88 C59.581722,88 56,91.581722 56,96 C56,100.418278 59.581722,104 64,104 Z\"></path>\n </g>\n</svg>"
},
"$:/language/Buttons/AdvancedSearch/Caption": {
"title": "$:/language/Buttons/AdvancedSearch/Caption",
"text": "advanced search"
},
"$:/language/Buttons/AdvancedSearch/Hint": {
"title": "$:/language/Buttons/AdvancedSearch/Hint",
"text": "Advanced search"
},
"$:/language/Buttons/Cancel/Caption": {
"title": "$:/language/Buttons/Cancel/Caption",
"text": "cancel"
},
"$:/language/Buttons/Cancel/Hint": {
"title": "$:/language/Buttons/Cancel/Hint",
"text": "Discard changes to this tiddler"
},
"$:/language/Buttons/Clone/Caption": {
"title": "$:/language/Buttons/Clone/Caption",
"text": "clone"
},
"$:/language/Buttons/Clone/Hint": {
"title": "$:/language/Buttons/Clone/Hint",
"text": "Clone this tiddler"
},
"$:/language/Buttons/Close/Caption": {
"title": "$:/language/Buttons/Close/Caption",
"text": "close"
},
"$:/language/Buttons/Close/Hint": {
"title": "$:/language/Buttons/Close/Hint",
"text": "Close this tiddler"
},
"$:/language/Buttons/CloseAll/Caption": {
"title": "$:/language/Buttons/CloseAll/Caption",
"text": "close all"
},
"$:/language/Buttons/CloseAll/Hint": {
"title": "$:/language/Buttons/CloseAll/Hint",
"text": "Close all tiddlers"
},
"$:/language/Buttons/CloseOthers/Caption": {
"title": "$:/language/Buttons/CloseOthers/Caption",
"text": "close others"
},
"$:/language/Buttons/CloseOthers/Hint": {
"title": "$:/language/Buttons/CloseOthers/Hint",
"text": "Close other tiddlers"
},
"$:/language/Buttons/ControlPanel/Caption": {
"title": "$:/language/Buttons/ControlPanel/Caption",
"text": "control panel"
},
"$:/language/Buttons/ControlPanel/Hint": {
"title": "$:/language/Buttons/ControlPanel/Hint",
"text": "Open control panel"
},
"$:/language/Buttons/Delete/Caption": {
"title": "$:/language/Buttons/Delete/Caption",
"text": "delete"
},
"$:/language/Buttons/Delete/Hint": {
"title": "$:/language/Buttons/Delete/Hint",
"text": "Delete this tiddler"
},
"$:/language/Buttons/Edit/Caption": {
"title": "$:/language/Buttons/Edit/Caption",
"text": "edit"
},
"$:/language/Buttons/Edit/Hint": {
"title": "$:/language/Buttons/Edit/Hint",
"text": "Edit this tiddler"
},
"$:/language/Buttons/Encryption/Caption": {
"title": "$:/language/Buttons/Encryption/Caption",
"text": "encryption"
},
"$:/language/Buttons/Encryption/Hint": {
"title": "$:/language/Buttons/Encryption/Hint",
"text": "Set or clear a password for saving this wiki"
},
"$:/language/Buttons/Encryption/ClearPassword/Caption": {
"title": "$:/language/Buttons/Encryption/ClearPassword/Caption",
"text": "clear password"
},
"$:/language/Buttons/Encryption/ClearPassword/Hint": {
"title": "$:/language/Buttons/Encryption/ClearPassword/Hint",
"text": "Clear the password and save this wiki without encryption"
},
"$:/language/Buttons/Encryption/SetPassword/Caption": {
"title": "$:/language/Buttons/Encryption/SetPassword/Caption",
"text": "set password"
},
"$:/language/Buttons/Encryption/SetPassword/Hint": {
"title": "$:/language/Buttons/Encryption/SetPassword/Hint",
"text": "Set a password for saving this wiki with encryption"
},
"$:/language/Buttons/ExportPage/Caption": {
"title": "$:/language/Buttons/ExportPage/Caption",
"text": "export all"
},
"$:/language/Buttons/ExportPage/Hint": {
"title": "$:/language/Buttons/ExportPage/Hint",
"text": "Export all tiddlers"
},
"$:/language/Buttons/ExportTiddler/Caption": {
"title": "$:/language/Buttons/ExportTiddler/Caption",
"text": "export tiddler"
},
"$:/language/Buttons/ExportTiddler/Hint": {
"title": "$:/language/Buttons/ExportTiddler/Hint",
"text": "Export tiddler"
},
"$:/language/Buttons/ExportTiddlers/Caption": {
"title": "$:/language/Buttons/ExportTiddlers/Caption",
"text": "export tiddlers"
},
"$:/language/Buttons/ExportTiddlers/Hint": {
"title": "$:/language/Buttons/ExportTiddlers/Hint",
"text": "Export tiddlers"
},
"$:/language/Buttons/Fold/Caption": {
"title": "$:/language/Buttons/Fold/Caption",
"text": "fold tiddler"
},
"$:/language/Buttons/Fold/Hint": {
"title": "$:/language/Buttons/Fold/Hint",
"text": "Fold the body of this tiddler"
},
"$:/language/Buttons/Fold/FoldBar/Caption": {
"title": "$:/language/Buttons/Fold/FoldBar/Caption",
"text": "fold-bar"
},
"$:/language/Buttons/Fold/FoldBar/Hint": {
"title": "$:/language/Buttons/Fold/FoldBar/Hint",
"text": "Optional bars to fold and unfold tiddlers"
},
"$:/language/Buttons/Unfold/Caption": {
"title": "$:/language/Buttons/Unfold/Caption",
"text": "unfold tiddler"
},
"$:/language/Buttons/Unfold/Hint": {
"title": "$:/language/Buttons/Unfold/Hint",
"text": "Unfold the body of this tiddler"
},
"$:/language/Buttons/FoldOthers/Caption": {
"title": "$:/language/Buttons/FoldOthers/Caption",
"text": "fold other tiddlers"
},
"$:/language/Buttons/FoldOthers/Hint": {
"title": "$:/language/Buttons/FoldOthers/Hint",
"text": "Fold the bodies of other opened tiddlers"
},
"$:/language/Buttons/FoldAll/Caption": {
"title": "$:/language/Buttons/FoldAll/Caption",
"text": "fold all tiddlers"
},
"$:/language/Buttons/FoldAll/Hint": {
"title": "$:/language/Buttons/FoldAll/Hint",
"text": "Fold the bodies of all opened tiddlers"
},
"$:/language/Buttons/UnfoldAll/Caption": {
"title": "$:/language/Buttons/UnfoldAll/Caption",
"text": "unfold all tiddlers"
},
"$:/language/Buttons/UnfoldAll/Hint": {
"title": "$:/language/Buttons/UnfoldAll/Hint",
"text": "Unfold the bodies of all opened tiddlers"
},
"$:/language/Buttons/FullScreen/Caption": {
"title": "$:/language/Buttons/FullScreen/Caption",
"text": "full-screen"
},
"$:/language/Buttons/FullScreen/Hint": {
"title": "$:/language/Buttons/FullScreen/Hint",
"text": "Enter or leave full-screen mode"
},
"$:/language/Buttons/Help/Caption": {
"title": "$:/language/Buttons/Help/Caption",
"text": "help"
},
"$:/language/Buttons/Help/Hint": {
"title": "$:/language/Buttons/Help/Hint",
"text": "Show help panel"
},
"$:/language/Buttons/Import/Caption": {
"title": "$:/language/Buttons/Import/Caption",
"text": "import"
},
"$:/language/Buttons/Import/Hint": {
"title": "$:/language/Buttons/Import/Hint",
"text": "Import many types of file including text, image, TiddlyWiki or JSON"
},
"$:/language/Buttons/Info/Caption": {
"title": "$:/language/Buttons/Info/Caption",
"text": "info"
},
"$:/language/Buttons/Info/Hint": {
"title": "$:/language/Buttons/Info/Hint",
"text": "Show information for this tiddler"
},
"$:/language/Buttons/Home/Caption": {
"title": "$:/language/Buttons/Home/Caption",
"text": "home"
},
"$:/language/Buttons/Home/Hint": {
"title": "$:/language/Buttons/Home/Hint",
"text": "Open the default tiddlers"
},
"$:/language/Buttons/Language/Caption": {
"title": "$:/language/Buttons/Language/Caption",
"text": "language"
},
"$:/language/Buttons/Language/Hint": {
"title": "$:/language/Buttons/Language/Hint",
"text": "Choose the user interface language"
},
"$:/language/Buttons/Manager/Caption": {
"title": "$:/language/Buttons/Manager/Caption",
"text": "tiddler manager"
},
"$:/language/Buttons/Manager/Hint": {
"title": "$:/language/Buttons/Manager/Hint",
"text": "Open tiddler manager"
},
"$:/language/Buttons/More/Caption": {
"title": "$:/language/Buttons/More/Caption",
"text": "more"
},
"$:/language/Buttons/More/Hint": {
"title": "$:/language/Buttons/More/Hint",
"text": "More actions"
},
"$:/language/Buttons/NewHere/Caption": {
"title": "$:/language/Buttons/NewHere/Caption",
"text": "new here"
},
"$:/language/Buttons/NewHere/Hint": {
"title": "$:/language/Buttons/NewHere/Hint",
"text": "Create a new tiddler tagged with this one"
},
"$:/language/Buttons/NewJournal/Caption": {
"title": "$:/language/Buttons/NewJournal/Caption",
"text": "new journal"
},
"$:/language/Buttons/NewJournal/Hint": {
"title": "$:/language/Buttons/NewJournal/Hint",
"text": "Create a new journal tiddler"
},
"$:/language/Buttons/NewJournalHere/Caption": {
"title": "$:/language/Buttons/NewJournalHere/Caption",
"text": "new journal here"
},
"$:/language/Buttons/NewJournalHere/Hint": {
"title": "$:/language/Buttons/NewJournalHere/Hint",
"text": "Create a new journal tiddler tagged with this one"
},
"$:/language/Buttons/NewImage/Caption": {
"title": "$:/language/Buttons/NewImage/Caption",
"text": "new image"
},
"$:/language/Buttons/NewImage/Hint": {
"title": "$:/language/Buttons/NewImage/Hint",
"text": "Create a new image tiddler"
},
"$:/language/Buttons/NewMarkdown/Caption": {
"title": "$:/language/Buttons/NewMarkdown/Caption",
"text": "new Markdown tiddler"
},
"$:/language/Buttons/NewMarkdown/Hint": {
"title": "$:/language/Buttons/NewMarkdown/Hint",
"text": "Create a new Markdown tiddler"
},
"$:/language/Buttons/NewTiddler/Caption": {
"title": "$:/language/Buttons/NewTiddler/Caption",
"text": "new tiddler"
},
"$:/language/Buttons/NewTiddler/Hint": {
"title": "$:/language/Buttons/NewTiddler/Hint",
"text": "Create a new tiddler"
},
"$:/language/Buttons/OpenWindow/Caption": {
"title": "$:/language/Buttons/OpenWindow/Caption",
"text": "open in new window"
},
"$:/language/Buttons/OpenWindow/Hint": {
"title": "$:/language/Buttons/OpenWindow/Hint",
"text": "Open tiddler in new window"
},
"$:/language/Buttons/Palette/Caption": {
"title": "$:/language/Buttons/Palette/Caption",
"text": "palette"
},
"$:/language/Buttons/Palette/Hint": {
"title": "$:/language/Buttons/Palette/Hint",
"text": "Choose the colour palette"
},
"$:/language/Buttons/Permalink/Caption": {
"title": "$:/language/Buttons/Permalink/Caption",
"text": "permalink"
},
"$:/language/Buttons/Permalink/Hint": {
"title": "$:/language/Buttons/Permalink/Hint",
"text": "Set browser address bar to a direct link to this tiddler"
},
"$:/language/Buttons/Permaview/Caption": {
"title": "$:/language/Buttons/Permaview/Caption",
"text": "permaview"
},
"$:/language/Buttons/Permaview/Hint": {
"title": "$:/language/Buttons/Permaview/Hint",
"text": "Set browser address bar to a direct link to all the tiddlers in this story"
},
"$:/language/Buttons/Print/Caption": {
"title": "$:/language/Buttons/Print/Caption",
"text": "print page"
},
"$:/language/Buttons/Print/Hint": {
"title": "$:/language/Buttons/Print/Hint",
"text": "Print the current page"
},
"$:/language/Buttons/Refresh/Caption": {
"title": "$:/language/Buttons/Refresh/Caption",
"text": "refresh"
},
"$:/language/Buttons/Refresh/Hint": {
"title": "$:/language/Buttons/Refresh/Hint",
"text": "Perform a full refresh of the wiki"
},
"$:/language/Buttons/Save/Caption": {
"title": "$:/language/Buttons/Save/Caption",
"text": "ok"
},
"$:/language/Buttons/Save/Hint": {
"title": "$:/language/Buttons/Save/Hint",
"text": "Confirm changes to this tiddler"
},
"$:/language/Buttons/SaveWiki/Caption": {
"title": "$:/language/Buttons/SaveWiki/Caption",
"text": "save changes"
},
"$:/language/Buttons/SaveWiki/Hint": {
"title": "$:/language/Buttons/SaveWiki/Hint",
"text": "Save changes"
},
"$:/language/Buttons/StoryView/Caption": {
"title": "$:/language/Buttons/StoryView/Caption",
"text": "storyview"
},
"$:/language/Buttons/StoryView/Hint": {
"title": "$:/language/Buttons/StoryView/Hint",
"text": "Choose the story visualisation"
},
"$:/language/Buttons/HideSideBar/Caption": {
"title": "$:/language/Buttons/HideSideBar/Caption",
"text": "hide sidebar"
},
"$:/language/Buttons/HideSideBar/Hint": {
"title": "$:/language/Buttons/HideSideBar/Hint",
"text": "Hide sidebar"
},
"$:/language/Buttons/ShowSideBar/Caption": {
"title": "$:/language/Buttons/ShowSideBar/Caption",
"text": "show sidebar"
},
"$:/language/Buttons/ShowSideBar/Hint": {
"title": "$:/language/Buttons/ShowSideBar/Hint",
"text": "Show sidebar"
},
"$:/language/Buttons/TagManager/Caption": {
"title": "$:/language/Buttons/TagManager/Caption",
"text": "tag manager"
},
"$:/language/Buttons/TagManager/Hint": {
"title": "$:/language/Buttons/TagManager/Hint",
"text": "Open tag manager"
},
"$:/language/Buttons/Timestamp/Caption": {
"title": "$:/language/Buttons/Timestamp/Caption",
"text": "timestamps"
},
"$:/language/Buttons/Timestamp/Hint": {
"title": "$:/language/Buttons/Timestamp/Hint",
"text": "Choose whether modifications update timestamps"
},
"$:/language/Buttons/Timestamp/On/Caption": {
"title": "$:/language/Buttons/Timestamp/On/Caption",
"text": "timestamps are on"
},
"$:/language/Buttons/Timestamp/On/Hint": {
"title": "$:/language/Buttons/Timestamp/On/Hint",
"text": "Update timestamps when tiddlers are modified"
},
"$:/language/Buttons/Timestamp/Off/Caption": {
"title": "$:/language/Buttons/Timestamp/Off/Caption",
"text": "timestamps are off"
},
"$:/language/Buttons/Timestamp/Off/Hint": {
"title": "$:/language/Buttons/Timestamp/Off/Hint",
"text": "Don't update timestamps when tiddlers are modified"
},
"$:/language/Buttons/Theme/Caption": {
"title": "$:/language/Buttons/Theme/Caption",
"text": "theme"
},
"$:/language/Buttons/Theme/Hint": {
"title": "$:/language/Buttons/Theme/Hint",
"text": "Choose the display theme"
},
"$:/language/Buttons/Bold/Caption": {
"title": "$:/language/Buttons/Bold/Caption",
"text": "bold"
},
"$:/language/Buttons/Bold/Hint": {
"title": "$:/language/Buttons/Bold/Hint",
"text": "Apply bold formatting to selection"
},
"$:/language/Buttons/Clear/Caption": {
"title": "$:/language/Buttons/Clear/Caption",
"text": "clear"
},
"$:/language/Buttons/Clear/Hint": {
"title": "$:/language/Buttons/Clear/Hint",
"text": "Clear image to solid colour"
},
"$:/language/Buttons/EditorHeight/Caption": {
"title": "$:/language/Buttons/EditorHeight/Caption",
"text": "editor height"
},
"$:/language/Buttons/EditorHeight/Caption/Auto": {
"title": "$:/language/Buttons/EditorHeight/Caption/Auto",
"text": "Automatically adjust height to fit content"
},
"$:/language/Buttons/EditorHeight/Caption/Fixed": {
"title": "$:/language/Buttons/EditorHeight/Caption/Fixed",
"text": "Fixed height:"
},
"$:/language/Buttons/EditorHeight/Hint": {
"title": "$:/language/Buttons/EditorHeight/Hint",
"text": "Choose the height of the text editor"
},
"$:/language/Buttons/Excise/Caption": {
"title": "$:/language/Buttons/Excise/Caption",
"text": "excise"
},
"$:/language/Buttons/Excise/Caption/Excise": {
"title": "$:/language/Buttons/Excise/Caption/Excise",
"text": "Perform excision"
},
"$:/language/Buttons/Excise/Caption/MacroName": {
"title": "$:/language/Buttons/Excise/Caption/MacroName",
"text": "Macro name:"
},
"$:/language/Buttons/Excise/Caption/NewTitle": {
"title": "$:/language/Buttons/Excise/Caption/NewTitle",
"text": "Title of new tiddler:"
},
"$:/language/Buttons/Excise/Caption/Replace": {
"title": "$:/language/Buttons/Excise/Caption/Replace",
"text": "Replace excised text with:"
},
"$:/language/Buttons/Excise/Caption/Replace/Macro": {
"title": "$:/language/Buttons/Excise/Caption/Replace/Macro",
"text": "macro"
},
"$:/language/Buttons/Excise/Caption/Replace/Link": {
"title": "$:/language/Buttons/Excise/Caption/Replace/Link",
"text": "link"
},
"$:/language/Buttons/Excise/Caption/Replace/Transclusion": {
"title": "$:/language/Buttons/Excise/Caption/Replace/Transclusion",
"text": "transclusion"
},
"$:/language/Buttons/Excise/Caption/Tag": {
"title": "$:/language/Buttons/Excise/Caption/Tag",
"text": "Tag new tiddler with the title of this tiddler"
},
"$:/language/Buttons/Excise/Caption/TiddlerExists": {
"title": "$:/language/Buttons/Excise/Caption/TiddlerExists",
"text": "Warning: tiddler already exists"
},
"$:/language/Buttons/Excise/Hint": {
"title": "$:/language/Buttons/Excise/Hint",
"text": "Excise the selected text into a new tiddler"
},
"$:/language/Buttons/Heading1/Caption": {
"title": "$:/language/Buttons/Heading1/Caption",
"text": "heading 1"
},
"$:/language/Buttons/Heading1/Hint": {
"title": "$:/language/Buttons/Heading1/Hint",
"text": "Apply heading level 1 formatting to lines containing selection"
},
"$:/language/Buttons/Heading2/Caption": {
"title": "$:/language/Buttons/Heading2/Caption",
"text": "heading 2"
},
"$:/language/Buttons/Heading2/Hint": {
"title": "$:/language/Buttons/Heading2/Hint",
"text": "Apply heading level 2 formatting to lines containing selection"
},
"$:/language/Buttons/Heading3/Caption": {
"title": "$:/language/Buttons/Heading3/Caption",
"text": "heading 3"
},
"$:/language/Buttons/Heading3/Hint": {
"title": "$:/language/Buttons/Heading3/Hint",
"text": "Apply heading level 3 formatting to lines containing selection"
},
"$:/language/Buttons/Heading4/Caption": {
"title": "$:/language/Buttons/Heading4/Caption",
"text": "heading 4"
},
"$:/language/Buttons/Heading4/Hint": {
"title": "$:/language/Buttons/Heading4/Hint",
"text": "Apply heading level 4 formatting to lines containing selection"
},
"$:/language/Buttons/Heading5/Caption": {
"title": "$:/language/Buttons/Heading5/Caption",
"text": "heading 5"
},
"$:/language/Buttons/Heading5/Hint": {
"title": "$:/language/Buttons/Heading5/Hint",
"text": "Apply heading level 5 formatting to lines containing selection"
},
"$:/language/Buttons/Heading6/Caption": {
"title": "$:/language/Buttons/Heading6/Caption",
"text": "heading 6"
},
"$:/language/Buttons/Heading6/Hint": {
"title": "$:/language/Buttons/Heading6/Hint",
"text": "Apply heading level 6 formatting to lines containing selection"
},
"$:/language/Buttons/Italic/Caption": {
"title": "$:/language/Buttons/Italic/Caption",
"text": "italic"
},
"$:/language/Buttons/Italic/Hint": {
"title": "$:/language/Buttons/Italic/Hint",
"text": "Apply italic formatting to selection"
},
"$:/language/Buttons/LineWidth/Caption": {
"title": "$:/language/Buttons/LineWidth/Caption",
"text": "line width"
},
"$:/language/Buttons/LineWidth/Hint": {
"title": "$:/language/Buttons/LineWidth/Hint",
"text": "Set line width for painting"
},
"$:/language/Buttons/Link/Caption": {
"title": "$:/language/Buttons/Link/Caption",
"text": "link"
},
"$:/language/Buttons/Link/Hint": {
"title": "$:/language/Buttons/Link/Hint",
"text": "Create wikitext link"
},
"$:/language/Buttons/ListBullet/Caption": {
"title": "$:/language/Buttons/ListBullet/Caption",
"text": "bulleted list"
},
"$:/language/Buttons/ListBullet/Hint": {
"title": "$:/language/Buttons/ListBullet/Hint",
"text": "Apply bulleted list formatting to lines containing selection"
},
"$:/language/Buttons/ListNumber/Caption": {
"title": "$:/language/Buttons/ListNumber/Caption",
"text": "numbered list"
},
"$:/language/Buttons/ListNumber/Hint": {
"title": "$:/language/Buttons/ListNumber/Hint",
"text": "Apply numbered list formatting to lines containing selection"
},
"$:/language/Buttons/MonoBlock/Caption": {
"title": "$:/language/Buttons/MonoBlock/Caption",
"text": "monospaced block"
},
"$:/language/Buttons/MonoBlock/Hint": {
"title": "$:/language/Buttons/MonoBlock/Hint",
"text": "Apply monospaced block formatting to lines containing selection"
},
"$:/language/Buttons/MonoLine/Caption": {
"title": "$:/language/Buttons/MonoLine/Caption",
"text": "monospaced"
},
"$:/language/Buttons/MonoLine/Hint": {
"title": "$:/language/Buttons/MonoLine/Hint",
"text": "Apply monospaced character formatting to selection"
},
"$:/language/Buttons/Opacity/Caption": {
"title": "$:/language/Buttons/Opacity/Caption",
"text": "opacity"
},
"$:/language/Buttons/Opacity/Hint": {
"title": "$:/language/Buttons/Opacity/Hint",
"text": "Set painting opacity"
},
"$:/language/Buttons/Paint/Caption": {
"title": "$:/language/Buttons/Paint/Caption",
"text": "paint colour"
},
"$:/language/Buttons/Paint/Hint": {
"title": "$:/language/Buttons/Paint/Hint",
"text": "Set painting colour"
},
"$:/language/Buttons/Picture/Caption": {
"title": "$:/language/Buttons/Picture/Caption",
"text": "picture"
},
"$:/language/Buttons/Picture/Hint": {
"title": "$:/language/Buttons/Picture/Hint",
"text": "Insert picture"
},
"$:/language/Buttons/Preview/Caption": {
"title": "$:/language/Buttons/Preview/Caption",
"text": "preview"
},
"$:/language/Buttons/Preview/Hint": {
"title": "$:/language/Buttons/Preview/Hint",
"text": "Show preview pane"
},
"$:/language/Buttons/PreviewType/Caption": {
"title": "$:/language/Buttons/PreviewType/Caption",
"text": "preview type"
},
"$:/language/Buttons/PreviewType/Hint": {
"title": "$:/language/Buttons/PreviewType/Hint",
"text": "Choose preview type"
},
"$:/language/Buttons/Quote/Caption": {
"title": "$:/language/Buttons/Quote/Caption",
"text": "quote"
},
"$:/language/Buttons/Quote/Hint": {
"title": "$:/language/Buttons/Quote/Hint",
"text": "Apply quoted text formatting to lines containing selection"
},
"$:/language/Buttons/Size/Caption": {
"title": "$:/language/Buttons/Size/Caption",
"text": "image size"
},
"$:/language/Buttons/Size/Caption/Height": {
"title": "$:/language/Buttons/Size/Caption/Height",
"text": "Height:"
},
"$:/language/Buttons/Size/Caption/Resize": {
"title": "$:/language/Buttons/Size/Caption/Resize",
"text": "Resize image"
},
"$:/language/Buttons/Size/Caption/Width": {
"title": "$:/language/Buttons/Size/Caption/Width",
"text": "Width:"
},
"$:/language/Buttons/Size/Hint": {
"title": "$:/language/Buttons/Size/Hint",
"text": "Set image size"
},
"$:/language/Buttons/Stamp/Caption": {
"title": "$:/language/Buttons/Stamp/Caption",
"text": "stamp"
},
"$:/language/Buttons/Stamp/Caption/New": {
"title": "$:/language/Buttons/Stamp/Caption/New",
"text": "Add your own"
},
"$:/language/Buttons/Stamp/Hint": {
"title": "$:/language/Buttons/Stamp/Hint",
"text": "Insert a preconfigured snippet of text"
},
"$:/language/Buttons/Stamp/New/Title": {
"title": "$:/language/Buttons/Stamp/New/Title",
"text": "Name as shown in menu"
},
"$:/language/Buttons/Stamp/New/Text": {
"title": "$:/language/Buttons/Stamp/New/Text",
"text": "Text of snippet. (Remember to add a descriptive title in the caption field)."
},
"$:/language/Buttons/Strikethrough/Caption": {
"title": "$:/language/Buttons/Strikethrough/Caption",
"text": "strikethrough"
},
"$:/language/Buttons/Strikethrough/Hint": {
"title": "$:/language/Buttons/Strikethrough/Hint",
"text": "Apply strikethrough formatting to selection"
},
"$:/language/Buttons/Subscript/Caption": {
"title": "$:/language/Buttons/Subscript/Caption",
"text": "subscript"
},
"$:/language/Buttons/Subscript/Hint": {
"title": "$:/language/Buttons/Subscript/Hint",
"text": "Apply subscript formatting to selection"
},
"$:/language/Buttons/Superscript/Caption": {
"title": "$:/language/Buttons/Superscript/Caption",
"text": "superscript"
},
"$:/language/Buttons/Superscript/Hint": {
"title": "$:/language/Buttons/Superscript/Hint",
"text": "Apply superscript formatting to selection"
},
"$:/language/Buttons/Underline/Caption": {
"title": "$:/language/Buttons/Underline/Caption",
"text": "underline"
},
"$:/language/Buttons/Underline/Hint": {
"title": "$:/language/Buttons/Underline/Hint",
"text": "Apply underline formatting to selection"
},
"$:/language/ControlPanel/Advanced/Caption": {
"title": "$:/language/ControlPanel/Advanced/Caption",
"text": "Advanced"
},
"$:/language/ControlPanel/Advanced/Hint": {
"title": "$:/language/ControlPanel/Advanced/Hint",
"text": "Internal information about this TiddlyWiki"
},
"$:/language/ControlPanel/Appearance/Caption": {
"title": "$:/language/ControlPanel/Appearance/Caption",
"text": "Appearance"
},
"$:/language/ControlPanel/Appearance/Hint": {
"title": "$:/language/ControlPanel/Appearance/Hint",
"text": "Ways to customise the appearance of your TiddlyWiki."
},
"$:/language/ControlPanel/Basics/AnimDuration/Prompt": {
"title": "$:/language/ControlPanel/Basics/AnimDuration/Prompt",
"text": "Animation duration:"
},
"$:/language/ControlPanel/Basics/Caption": {
"title": "$:/language/ControlPanel/Basics/Caption",
"text": "Basics"
},
"$:/language/ControlPanel/Basics/DefaultTiddlers/BottomHint": {
"title": "$:/language/ControlPanel/Basics/DefaultTiddlers/BottomHint",
"text": "Use [[double square brackets]] for titles with spaces. Or you can choose to <$button set=\"$:/DefaultTiddlers\" setTo=\"[list[$:/StoryList]]\">retain story ordering</$button>"
},
"$:/language/ControlPanel/Basics/DefaultTiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/DefaultTiddlers/Prompt",
"text": "Default tiddlers:"
},
"$:/language/ControlPanel/Basics/DefaultTiddlers/TopHint": {
"title": "$:/language/ControlPanel/Basics/DefaultTiddlers/TopHint",
"text": "Choose which tiddlers are displayed at startup:"
},
"$:/language/ControlPanel/Basics/Language/Prompt": {
"title": "$:/language/ControlPanel/Basics/Language/Prompt",
"text": "Hello! Current language:"
},
"$:/language/ControlPanel/Basics/NewJournal/Title/Prompt": {
"title": "$:/language/ControlPanel/Basics/NewJournal/Title/Prompt",
"text": "Title of new journal tiddlers"
},
"$:/language/ControlPanel/Basics/NewJournal/Text/Prompt": {
"title": "$:/language/ControlPanel/Basics/NewJournal/Text/Prompt",
"text": "Text for new journal tiddlers"
},
"$:/language/ControlPanel/Basics/NewJournal/Tags/Prompt": {
"title": "$:/language/ControlPanel/Basics/NewJournal/Tags/Prompt",
"text": "Tags for new journal tiddlers"
},
"$:/language/ControlPanel/Basics/OverriddenShadowTiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/OverriddenShadowTiddlers/Prompt",
"text": "Number of overridden shadow tiddlers:"
},
"$:/language/ControlPanel/Basics/ShadowTiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/ShadowTiddlers/Prompt",
"text": "Number of shadow tiddlers:"
},
"$:/language/ControlPanel/Basics/Subtitle/Prompt": {
"title": "$:/language/ControlPanel/Basics/Subtitle/Prompt",
"text": "Subtitle:"
},
"$:/language/ControlPanel/Basics/SystemTiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/SystemTiddlers/Prompt",
"text": "Number of system tiddlers:"
},
"$:/language/ControlPanel/Basics/Tags/Prompt": {
"title": "$:/language/ControlPanel/Basics/Tags/Prompt",
"text": "Number of tags:"
},
"$:/language/ControlPanel/Basics/Tiddlers/Prompt": {
"title": "$:/language/ControlPanel/Basics/Tiddlers/Prompt",
"text": "Number of tiddlers:"
},
"$:/language/ControlPanel/Basics/Title/Prompt": {
"title": "$:/language/ControlPanel/Basics/Title/Prompt",
"text": "Title of this ~TiddlyWiki:"
},
"$:/language/ControlPanel/Basics/Username/Prompt": {
"title": "$:/language/ControlPanel/Basics/Username/Prompt",
"text": "Username for signing edits:"
},
"$:/language/ControlPanel/Basics/Version/Prompt": {
"title": "$:/language/ControlPanel/Basics/Version/Prompt",
"text": "~TiddlyWiki version:"
},
"$:/language/ControlPanel/EditorTypes/Caption": {
"title": "$:/language/ControlPanel/EditorTypes/Caption",
"text": "Editor Types"
},
"$:/language/ControlPanel/EditorTypes/Editor/Caption": {
"title": "$:/language/ControlPanel/EditorTypes/Editor/Caption",
"text": "Editor"
},
"$:/language/ControlPanel/EditorTypes/Hint": {
"title": "$:/language/ControlPanel/EditorTypes/Hint",
"text": "These tiddlers determine which editor is used to edit specific tiddler types."
},
"$:/language/ControlPanel/EditorTypes/Type/Caption": {
"title": "$:/language/ControlPanel/EditorTypes/Type/Caption",
"text": "Type"
},
"$:/language/ControlPanel/Info/Caption": {
"title": "$:/language/ControlPanel/Info/Caption",
"text": "Info"
},
"$:/language/ControlPanel/Info/Hint": {
"title": "$:/language/ControlPanel/Info/Hint",
"text": "Information about this TiddlyWiki"
},
"$:/language/ControlPanel/KeyboardShortcuts/Add/Prompt": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Add/Prompt",
"text": "Type shortcut here"
},
"$:/language/ControlPanel/KeyboardShortcuts/Add/Caption": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Add/Caption",
"text": "add shortcut"
},
"$:/language/ControlPanel/KeyboardShortcuts/Caption": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Caption",
"text": "Keyboard Shortcuts"
},
"$:/language/ControlPanel/KeyboardShortcuts/Hint": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Hint",
"text": "Manage keyboard shortcut assignments"
},
"$:/language/ControlPanel/KeyboardShortcuts/NoShortcuts/Caption": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/NoShortcuts/Caption",
"text": "No keyboard shortcuts assigned"
},
"$:/language/ControlPanel/KeyboardShortcuts/Remove/Hint": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Remove/Hint",
"text": "remove keyboard shortcut"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/All": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/All",
"text": "All platforms"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/Mac": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/Mac",
"text": "Macintosh platform only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/NonMac": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/NonMac",
"text": "Non-Macintosh platforms only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/Linux": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/Linux",
"text": "Linux platform only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/NonLinux": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/NonLinux",
"text": "Non-Linux platforms only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/Windows": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/Windows",
"text": "Windows platform only"
},
"$:/language/ControlPanel/KeyboardShortcuts/Platform/NonWindows": {
"title": "$:/language/ControlPanel/KeyboardShortcuts/Platform/NonWindows",
"text": "Non-Windows platforms only"
},
"$:/language/ControlPanel/LoadedModules/Caption": {
"title": "$:/language/ControlPanel/LoadedModules/Caption",
"text": "Loaded Modules"
},
"$:/language/ControlPanel/LoadedModules/Hint": {
"title": "$:/language/ControlPanel/LoadedModules/Hint",
"text": "These are the currently loaded tiddler modules linked to their source tiddlers. Any italicised modules lack a source tiddler, typically because they were setup during the boot process."
},
"$:/language/ControlPanel/Palette/Caption": {
"title": "$:/language/ControlPanel/Palette/Caption",
"text": "Palette"
},
"$:/language/ControlPanel/Palette/Editor/Clone/Caption": {
"title": "$:/language/ControlPanel/Palette/Editor/Clone/Caption",
"text": "clone"
},
"$:/language/ControlPanel/Palette/Editor/Clone/Prompt": {
"title": "$:/language/ControlPanel/Palette/Editor/Clone/Prompt",
"text": "It is recommended that you clone this shadow palette before editing it"
},
"$:/language/ControlPanel/Palette/Editor/Prompt/Modified": {
"title": "$:/language/ControlPanel/Palette/Editor/Prompt/Modified",
"text": "This shadow palette has been modified"
},
"$:/language/ControlPanel/Palette/Editor/Prompt": {
"title": "$:/language/ControlPanel/Palette/Editor/Prompt",
"text": "Editing"
},
"$:/language/ControlPanel/Palette/Editor/Reset/Caption": {
"title": "$:/language/ControlPanel/Palette/Editor/Reset/Caption",
"text": "reset"
},
"$:/language/ControlPanel/Palette/HideEditor/Caption": {
"title": "$:/language/ControlPanel/Palette/HideEditor/Caption",
"text": "hide editor"
},
"$:/language/ControlPanel/Palette/Prompt": {
"title": "$:/language/ControlPanel/Palette/Prompt",
"text": "Current palette:"
},
"$:/language/ControlPanel/Palette/ShowEditor/Caption": {
"title": "$:/language/ControlPanel/Palette/ShowEditor/Caption",
"text": "show editor"
},
"$:/language/ControlPanel/Parsing/Caption": {
"title": "$:/language/ControlPanel/Parsing/Caption",
"text": "Parsing"
},
"$:/language/ControlPanel/Parsing/Hint": {
"title": "$:/language/ControlPanel/Parsing/Hint",
"text": "Here you can globally disable/enable wiki parser rules. For changes to take effect, save and reload your wiki. Disabling certain parser rules can prevent <$text text=\"TiddlyWiki\"/> from functioning correctly. Use [[safe mode|http://tiddlywiki.com/#SafeMode]] to restore normal operation."
},
"$:/language/ControlPanel/Parsing/Block/Caption": {
"title": "$:/language/ControlPanel/Parsing/Block/Caption",
"text": "Block Parse Rules"
},
"$:/language/ControlPanel/Parsing/Inline/Caption": {
"title": "$:/language/ControlPanel/Parsing/Inline/Caption",
"text": "Inline Parse Rules"
},
"$:/language/ControlPanel/Parsing/Pragma/Caption": {
"title": "$:/language/ControlPanel/Parsing/Pragma/Caption",
"text": "Pragma Parse Rules"
},
"$:/language/ControlPanel/Plugins/Add/Caption": {
"title": "$:/language/ControlPanel/Plugins/Add/Caption",
"text": "Get more plugins"
},
"$:/language/ControlPanel/Plugins/Add/Hint": {
"title": "$:/language/ControlPanel/Plugins/Add/Hint",
"text": "Install plugins from the official library"
},
"$:/language/ControlPanel/Plugins/AlreadyInstalled/Hint": {
"title": "$:/language/ControlPanel/Plugins/AlreadyInstalled/Hint",
"text": "This plugin is already installed at version <$text text=<<installedVersion>>/>"
},
"$:/language/ControlPanel/Plugins/Caption": {
"title": "$:/language/ControlPanel/Plugins/Caption",
"text": "Plugins"
},
"$:/language/ControlPanel/Plugins/Disable/Caption": {
"title": "$:/language/ControlPanel/Plugins/Disable/Caption",
"text": "disable"
},
"$:/language/ControlPanel/Plugins/Disable/Hint": {
"title": "$:/language/ControlPanel/Plugins/Disable/Hint",
"text": "Disable this plugin when reloading page"
},
"$:/language/ControlPanel/Plugins/Disabled/Status": {
"title": "$:/language/ControlPanel/Plugins/Disabled/Status",
"text": "(disabled)"
},
"$:/language/ControlPanel/Plugins/Empty/Hint": {
"title": "$:/language/ControlPanel/Plugins/Empty/Hint",
"text": "None"
},
"$:/language/ControlPanel/Plugins/Enable/Caption": {
"title": "$:/language/ControlPanel/Plugins/Enable/Caption",
"text": "enable"
},
"$:/language/ControlPanel/Plugins/Enable/Hint": {
"title": "$:/language/ControlPanel/Plugins/Enable/Hint",
"text": "Enable this plugin when reloading page"
},
"$:/language/ControlPanel/Plugins/Install/Caption": {
"title": "$:/language/ControlPanel/Plugins/Install/Caption",
"text": "install"
},
"$:/language/ControlPanel/Plugins/Installed/Hint": {
"title": "$:/language/ControlPanel/Plugins/Installed/Hint",
"text": "Currently installed plugins:"
},
"$:/language/ControlPanel/Plugins/Languages/Caption": {
"title": "$:/language/ControlPanel/Plugins/Languages/Caption",
"text": "Languages"
},
"$:/language/ControlPanel/Plugins/Languages/Hint": {
"title": "$:/language/ControlPanel/Plugins/Languages/Hint",
"text": "Language pack plugins"
},
"$:/language/ControlPanel/Plugins/NoInfoFound/Hint": {
"title": "$:/language/ControlPanel/Plugins/NoInfoFound/Hint",
"text": "No ''\"<$text text=<<currentTab>>/>\"'' found"
},
"$:/language/ControlPanel/Plugins/NoInformation/Hint": {
"title": "$:/language/ControlPanel/Plugins/NoInformation/Hint",
"text": "No information provided"
},
"$:/language/ControlPanel/Plugins/NotInstalled/Hint": {
"title": "$:/language/ControlPanel/Plugins/NotInstalled/Hint",
"text": "This plugin is not currently installed"
},
"$:/language/ControlPanel/Plugins/OpenPluginLibrary": {
"title": "$:/language/ControlPanel/Plugins/OpenPluginLibrary",
"text": "open plugin library"
},
"$:/language/ControlPanel/Plugins/ClosePluginLibrary": {
"title": "$:/language/ControlPanel/Plugins/ClosePluginLibrary",
"text": "close plugin library"
},
"$:/language/ControlPanel/Plugins/Plugins/Caption": {
"title": "$:/language/ControlPanel/Plugins/Plugins/Caption",
"text": "Plugins"
},
"$:/language/ControlPanel/Plugins/Plugins/Hint": {
"title": "$:/language/ControlPanel/Plugins/Plugins/Hint",
"text": "Plugins"
},
"$:/language/ControlPanel/Plugins/Reinstall/Caption": {
"title": "$:/language/ControlPanel/Plugins/Reinstall/Caption",
"text": "reinstall"
},
"$:/language/ControlPanel/Plugins/Themes/Caption": {
"title": "$:/language/ControlPanel/Plugins/Themes/Caption",
"text": "Themes"
},
"$:/language/ControlPanel/Plugins/Themes/Hint": {
"title": "$:/language/ControlPanel/Plugins/Themes/Hint",
"text": "Theme plugins"
},
"$:/language/ControlPanel/Saving/Caption": {
"title": "$:/language/ControlPanel/Saving/Caption",
"text": "Saving"
},
"$:/language/ControlPanel/Saving/DownloadSaver/AutoSave/Description": {
"title": "$:/language/ControlPanel/Saving/DownloadSaver/AutoSave/Description",
"text": "Permit automatic saving for the download saver"
},
"$:/language/ControlPanel/Saving/DownloadSaver/AutoSave/Hint": {
"title": "$:/language/ControlPanel/Saving/DownloadSaver/AutoSave/Hint",
"text": "Enable Autosave for Download Saver"
},
"$:/language/ControlPanel/Saving/DownloadSaver/Caption": {
"title": "$:/language/ControlPanel/Saving/DownloadSaver/Caption",
"text": "Download Saver"
},
"$:/language/ControlPanel/Saving/DownloadSaver/Hint": {
"title": "$:/language/ControlPanel/Saving/DownloadSaver/Hint",
"text": "These settings apply to the HTML5-compatible download saver"
},
"$:/language/ControlPanel/Saving/General/Caption": {
"title": "$:/language/ControlPanel/Saving/General/Caption",
"text": "General"
},
"$:/language/ControlPanel/Saving/General/Hint": {
"title": "$:/language/ControlPanel/Saving/General/Hint",
"text": "These settings apply to all the loaded savers"
},
"$:/language/ControlPanel/Saving/Hint": {
"title": "$:/language/ControlPanel/Saving/Hint",
"text": "Settings used for saving the entire TiddlyWiki as a single file via a saver module"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Advanced/Heading": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Advanced/Heading",
"text": "Advanced Settings"
},
"$:/language/ControlPanel/Saving/TiddlySpot/BackupDir": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/BackupDir",
"text": "Backup Directory"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Backups": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Backups",
"text": "Backups"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Caption": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Caption",
"text": "~TiddlySpot Saver"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Description": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Description",
"text": "These settings are only used when saving to http://tiddlyspot.com or a compatible remote server"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Filename": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Filename",
"text": "Upload Filename"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Heading": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Heading",
"text": "~TiddlySpot"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Hint": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Hint",
"text": "//The server URL defaults to `http://<wikiname>.tiddlyspot.com/store.cgi` and can be changed to use a custom server address, e.g. `http://example.com/store.php`.//"
},
"$:/language/ControlPanel/Saving/TiddlySpot/Password": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/Password",
"text": "Password"
},
"$:/language/ControlPanel/Saving/TiddlySpot/ServerURL": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/ServerURL",
"text": "Server URL"
},
"$:/language/ControlPanel/Saving/TiddlySpot/UploadDir": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/UploadDir",
"text": "Upload Directory"
},
"$:/language/ControlPanel/Saving/TiddlySpot/UserName": {
"title": "$:/language/ControlPanel/Saving/TiddlySpot/UserName",
"text": "Wiki Name"
},
"$:/language/ControlPanel/Settings/AutoSave/Caption": {
"title": "$:/language/ControlPanel/Settings/AutoSave/Caption",
"text": "Autosave"
},
"$:/language/ControlPanel/Settings/AutoSave/Disabled/Description": {
"title": "$:/language/ControlPanel/Settings/AutoSave/Disabled/Description",
"text": "Do not save changes automatically"
},
"$:/language/ControlPanel/Settings/AutoSave/Enabled/Description": {
"title": "$:/language/ControlPanel/Settings/AutoSave/Enabled/Description",
"text": "Save changes automatically"
},
"$:/language/ControlPanel/Settings/AutoSave/Hint": {
"title": "$:/language/ControlPanel/Settings/AutoSave/Hint",
"text": "Attempt to automatically save changes during editing when using a supporting saver"
},
"$:/language/ControlPanel/Settings/CamelCase/Caption": {
"title": "$:/language/ControlPanel/Settings/CamelCase/Caption",
"text": "Camel Case Wiki Links"
},
"$:/language/ControlPanel/Settings/CamelCase/Hint": {
"title": "$:/language/ControlPanel/Settings/CamelCase/Hint",
"text": "You can globally disable automatic linking of ~CamelCase phrases. Requires reload to take effect"
},
"$:/language/ControlPanel/Settings/CamelCase/Description": {
"title": "$:/language/ControlPanel/Settings/CamelCase/Description",
"text": "Enable automatic ~CamelCase linking"
},
"$:/language/ControlPanel/Settings/Caption": {
"title": "$:/language/ControlPanel/Settings/Caption",
"text": "Settings"
},
"$:/language/ControlPanel/Settings/EditorToolbar/Caption": {
"title": "$:/language/ControlPanel/Settings/EditorToolbar/Caption",
"text": "Editor Toolbar"
},
"$:/language/ControlPanel/Settings/EditorToolbar/Hint": {
"title": "$:/language/ControlPanel/Settings/EditorToolbar/Hint",
"text": "Enable or disable the editor toolbar:"
},
"$:/language/ControlPanel/Settings/EditorToolbar/Description": {
"title": "$:/language/ControlPanel/Settings/EditorToolbar/Description",
"text": "Show editor toolbar"
},
"$:/language/ControlPanel/Settings/InfoPanelMode/Caption": {
"title": "$:/language/ControlPanel/Settings/InfoPanelMode/Caption",
"text": "Tiddler Info Panel Mode"
},
"$:/language/ControlPanel/Settings/InfoPanelMode/Hint": {
"title": "$:/language/ControlPanel/Settings/InfoPanelMode/Hint",
"text": "Control when the tiddler info panel closes:"
},
"$:/language/ControlPanel/Settings/InfoPanelMode/Popup/Description": {
"title": "$:/language/ControlPanel/Settings/InfoPanelMode/Popup/Description",
"text": "Tiddler info panel closes automatically"
},
"$:/language/ControlPanel/Settings/InfoPanelMode/Sticky/Description": {
"title": "$:/language/ControlPanel/Settings/InfoPanelMode/Sticky/Description",
"text": "Tiddler info panel stays open until explicitly closed"
},
"$:/language/ControlPanel/Settings/Hint": {
"title": "$:/language/ControlPanel/Settings/Hint",
"text": "These settings let you customise the behaviour of TiddlyWiki."
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/Caption": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/Caption",
"text": "Navigation Address Bar"
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/Hint": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/Hint",
"text": "Behaviour of the browser address bar when navigating to a tiddler:"
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/No/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/No/Description",
"text": "Do not update the address bar"
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/Permalink/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/Permalink/Description",
"text": "Include the target tiddler"
},
"$:/language/ControlPanel/Settings/NavigationAddressBar/Permaview/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationAddressBar/Permaview/Description",
"text": "Include the target tiddler and the current story sequence"
},
"$:/language/ControlPanel/Settings/NavigationHistory/Caption": {
"title": "$:/language/ControlPanel/Settings/NavigationHistory/Caption",
"text": "Navigation History"
},
"$:/language/ControlPanel/Settings/NavigationHistory/Hint": {
"title": "$:/language/ControlPanel/Settings/NavigationHistory/Hint",
"text": "Update browser history when navigating to a tiddler:"
},
"$:/language/ControlPanel/Settings/NavigationHistory/No/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationHistory/No/Description",
"text": "Do not update history"
},
"$:/language/ControlPanel/Settings/NavigationHistory/Yes/Description": {
"title": "$:/language/ControlPanel/Settings/NavigationHistory/Yes/Description",
"text": "Update history"
},
"$:/language/ControlPanel/Settings/PerformanceInstrumentation/Caption": {
"title": "$:/language/ControlPanel/Settings/PerformanceInstrumentation/Caption",
"text": "Performance Instrumentation"
},
"$:/language/ControlPanel/Settings/PerformanceInstrumentation/Hint": {
"title": "$:/language/ControlPanel/Settings/PerformanceInstrumentation/Hint",
"text": "Displays performance statistics in the browser developer console. Requires reload to take effect"
},
"$:/language/ControlPanel/Settings/PerformanceInstrumentation/Description": {
"title": "$:/language/ControlPanel/Settings/PerformanceInstrumentation/Description",
"text": "Enable performance instrumentation"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Caption": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Caption",
"text": "Toolbar Button Style"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Hint": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Hint",
"text": "Choose the style for toolbar buttons:"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Borderless": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Borderless",
"text": "Borderless"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Boxed": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Boxed",
"text": "Boxed"
},
"$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Rounded": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Rounded",
"text": "Rounded"
},
"$:/language/ControlPanel/Settings/ToolbarButtons/Caption": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtons/Caption",
"text": "Toolbar Buttons"
},
"$:/language/ControlPanel/Settings/ToolbarButtons/Hint": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtons/Hint",
"text": "Default toolbar button appearance:"
},
"$:/language/ControlPanel/Settings/ToolbarButtons/Icons/Description": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtons/Icons/Description",
"text": "Include icon"
},
"$:/language/ControlPanel/Settings/ToolbarButtons/Text/Description": {
"title": "$:/language/ControlPanel/Settings/ToolbarButtons/Text/Description",
"text": "Include text"
},
"$:/language/ControlPanel/Settings/DefaultSidebarTab/Caption": {
"title": "$:/language/ControlPanel/Settings/DefaultSidebarTab/Caption",
"text": "Default Sidebar Tab"
},
"$:/language/ControlPanel/Settings/DefaultSidebarTab/Hint": {
"title": "$:/language/ControlPanel/Settings/DefaultSidebarTab/Hint",
"text": "Specify which sidebar tab is displayed by default"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/Caption": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/Caption",
"text": "Tiddler Opening Behaviour"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/InsideRiver/Hint": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/InsideRiver/Hint",
"text": "Navigation from //within// the story river"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OutsideRiver/Hint": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OutsideRiver/Hint",
"text": "Navigation from //outside// the story river"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAbove": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAbove",
"text": "Open above the current tiddler"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OpenBelow": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OpenBelow",
"text": "Open below the current tiddler"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAtTop": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAtTop",
"text": "Open at the top of the story river"
},
"$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAtBottom": {
"title": "$:/language/ControlPanel/Settings/LinkToBehaviour/OpenAtBottom",
"text": "Open at the bottom of the story river"
},
"$:/language/ControlPanel/Settings/TitleLinks/Caption": {
"title": "$:/language/ControlPanel/Settings/TitleLinks/Caption",
"text": "Tiddler Titles"
},
"$:/language/ControlPanel/Settings/TitleLinks/Hint": {
"title": "$:/language/ControlPanel/Settings/TitleLinks/Hint",
"text": "Optionally display tiddler titles as links"
},
"$:/language/ControlPanel/Settings/TitleLinks/No/Description": {
"title": "$:/language/ControlPanel/Settings/TitleLinks/No/Description",
"text": "Do not display tiddler titles as links"
},
"$:/language/ControlPanel/Settings/TitleLinks/Yes/Description": {
"title": "$:/language/ControlPanel/Settings/TitleLinks/Yes/Description",
"text": "Display tiddler titles as links"
},
"$:/language/ControlPanel/Settings/MissingLinks/Caption": {
"title": "$:/language/ControlPanel/Settings/MissingLinks/Caption",
"text": "Wiki Links"
},
"$:/language/ControlPanel/Settings/MissingLinks/Hint": {
"title": "$:/language/ControlPanel/Settings/MissingLinks/Hint",
"text": "Choose whether to link to tiddlers that do not exist yet"
},
"$:/language/ControlPanel/Settings/MissingLinks/Description": {
"title": "$:/language/ControlPanel/Settings/MissingLinks/Description",
"text": "Enable links to missing tiddlers"
},
"$:/language/ControlPanel/StoryView/Caption": {
"title": "$:/language/ControlPanel/StoryView/Caption",
"text": "Story View"
},
"$:/language/ControlPanel/StoryView/Prompt": {
"title": "$:/language/ControlPanel/StoryView/Prompt",
"text": "Current view:"
},
"$:/language/ControlPanel/Theme/Caption": {
"title": "$:/language/ControlPanel/Theme/Caption",
"text": "Theme"
},
"$:/language/ControlPanel/Theme/Prompt": {
"title": "$:/language/ControlPanel/Theme/Prompt",
"text": "Current theme:"
},
"$:/language/ControlPanel/TiddlerFields/Caption": {
"title": "$:/language/ControlPanel/TiddlerFields/Caption",
"text": "Tiddler Fields"
},
"$:/language/ControlPanel/TiddlerFields/Hint": {
"title": "$:/language/ControlPanel/TiddlerFields/Hint",
"text": "This is the full set of TiddlerFields in use in this wiki (including system tiddlers but excluding shadow tiddlers)."
},
"$:/language/ControlPanel/Toolbars/Caption": {
"title": "$:/language/ControlPanel/Toolbars/Caption",
"text": "Toolbars"
},
"$:/language/ControlPanel/Toolbars/EditToolbar/Caption": {
"title": "$:/language/ControlPanel/Toolbars/EditToolbar/Caption",
"text": "Edit Toolbar"
},
"$:/language/ControlPanel/Toolbars/EditToolbar/Hint": {
"title": "$:/language/ControlPanel/Toolbars/EditToolbar/Hint",
"text": "Choose which buttons are displayed for tiddlers in edit mode. Drag and drop to change the ordering"
},
"$:/language/ControlPanel/Toolbars/Hint": {
"title": "$:/language/ControlPanel/Toolbars/Hint",
"text": "Select which toolbar buttons are displayed"
},
"$:/language/ControlPanel/Toolbars/PageControls/Caption": {
"title": "$:/language/ControlPanel/Toolbars/PageControls/Caption",
"text": "Page Toolbar"
},
"$:/language/ControlPanel/Toolbars/PageControls/Hint": {
"title": "$:/language/ControlPanel/Toolbars/PageControls/Hint",
"text": "Choose which buttons are displayed on the main page toolbar. Drag and drop to change the ordering"
},
"$:/language/ControlPanel/Toolbars/EditorToolbar/Caption": {
"title": "$:/language/ControlPanel/Toolbars/EditorToolbar/Caption",
"text": "Editor Toolbar"
},
"$:/language/ControlPanel/Toolbars/EditorToolbar/Hint": {
"title": "$:/language/ControlPanel/Toolbars/EditorToolbar/Hint",
"text": "Choose which buttons are displayed in the editor toolbar. Note that some buttons will only appear when editing tiddlers of a certain type. Drag and drop to change the ordering"
},
"$:/language/ControlPanel/Toolbars/ViewToolbar/Caption": {
"title": "$:/language/ControlPanel/Toolbars/ViewToolbar/Caption",
"text": "View Toolbar"
},
"$:/language/ControlPanel/Toolbars/ViewToolbar/Hint": {
"title": "$:/language/ControlPanel/Toolbars/ViewToolbar/Hint",
"text": "Choose which buttons are displayed for tiddlers in view mode. Drag and drop to change the ordering"
},
"$:/language/ControlPanel/Tools/Download/Full/Caption": {
"title": "$:/language/ControlPanel/Tools/Download/Full/Caption",
"text": "Download full wiki"
},
"$:/language/Date/DaySuffix/1": {
"title": "$:/language/Date/DaySuffix/1",
"text": "st"
},
"$:/language/Date/DaySuffix/2": {
"title": "$:/language/Date/DaySuffix/2",
"text": "nd"
},
"$:/language/Date/DaySuffix/3": {
"title": "$:/language/Date/DaySuffix/3",
"text": "rd"
},
"$:/language/Date/DaySuffix/4": {
"title": "$:/language/Date/DaySuffix/4",
"text": "th"
},
"$:/language/Date/DaySuffix/5": {
"title": "$:/language/Date/DaySuffix/5",
"text": "th"
},
"$:/language/Date/DaySuffix/6": {
"title": "$:/language/Date/DaySuffix/6",
"text": "th"
},
"$:/language/Date/DaySuffix/7": {
"title": "$:/language/Date/DaySuffix/7",
"text": "th"
},
"$:/language/Date/DaySuffix/8": {
"title": "$:/language/Date/DaySuffix/8",
"text": "th"
},
"$:/language/Date/DaySuffix/9": {
"title": "$:/language/Date/DaySuffix/9",
"text": "th"
},
"$:/language/Date/DaySuffix/10": {
"title": "$:/language/Date/DaySuffix/10",
"text": "th"
},
"$:/language/Date/DaySuffix/11": {
"title": "$:/language/Date/DaySuffix/11",
"text": "th"
},
"$:/language/Date/DaySuffix/12": {
"title": "$:/language/Date/DaySuffix/12",
"text": "th"
},
"$:/language/Date/DaySuffix/13": {
"title": "$:/language/Date/DaySuffix/13",
"text": "th"
},
"$:/language/Date/DaySuffix/14": {
"title": "$:/language/Date/DaySuffix/14",
"text": "th"
},
"$:/language/Date/DaySuffix/15": {
"title": "$:/language/Date/DaySuffix/15",
"text": "th"
},
"$:/language/Date/DaySuffix/16": {
"title": "$:/language/Date/DaySuffix/16",
"text": "th"
},
"$:/language/Date/DaySuffix/17": {
"title": "$:/language/Date/DaySuffix/17",
"text": "th"
},
"$:/language/Date/DaySuffix/18": {
"title": "$:/language/Date/DaySuffix/18",
"text": "th"
},
"$:/language/Date/DaySuffix/19": {
"title": "$:/language/Date/DaySuffix/19",
"text": "th"
},
"$:/language/Date/DaySuffix/20": {
"title": "$:/language/Date/DaySuffix/20",
"text": "th"
},
"$:/language/Date/DaySuffix/21": {
"title": "$:/language/Date/DaySuffix/21",
"text": "st"
},
"$:/language/Date/DaySuffix/22": {
"title": "$:/language/Date/DaySuffix/22",
"text": "nd"
},
"$:/language/Date/DaySuffix/23": {
"title": "$:/language/Date/DaySuffix/23",
"text": "rd"
},
"$:/language/Date/DaySuffix/24": {
"title": "$:/language/Date/DaySuffix/24",
"text": "th"
},
"$:/language/Date/DaySuffix/25": {
"title": "$:/language/Date/DaySuffix/25",
"text": "th"
},
"$:/language/Date/DaySuffix/26": {
"title": "$:/language/Date/DaySuffix/26",
"text": "th"
},
"$:/language/Date/DaySuffix/27": {
"title": "$:/language/Date/DaySuffix/27",
"text": "th"
},
"$:/language/Date/DaySuffix/28": {
"title": "$:/language/Date/DaySuffix/28",
"text": "th"
},
"$:/language/Date/DaySuffix/29": {
"title": "$:/language/Date/DaySuffix/29",
"text": "th"
},
"$:/language/Date/DaySuffix/30": {
"title": "$:/language/Date/DaySuffix/30",
"text": "th"
},
"$:/language/Date/DaySuffix/31": {
"title": "$:/language/Date/DaySuffix/31",
"text": "st"
},
"$:/language/Date/Long/Day/0": {
"title": "$:/language/Date/Long/Day/0",
"text": "Sunday"
},
"$:/language/Date/Long/Day/1": {
"title": "$:/language/Date/Long/Day/1",
"text": "Monday"
},
"$:/language/Date/Long/Day/2": {
"title": "$:/language/Date/Long/Day/2",
"text": "Tuesday"
},
"$:/language/Date/Long/Day/3": {
"title": "$:/language/Date/Long/Day/3",
"text": "Wednesday"
},
"$:/language/Date/Long/Day/4": {
"title": "$:/language/Date/Long/Day/4",
"text": "Thursday"
},
"$:/language/Date/Long/Day/5": {
"title": "$:/language/Date/Long/Day/5",
"text": "Friday"
},
"$:/language/Date/Long/Day/6": {
"title": "$:/language/Date/Long/Day/6",
"text": "Saturday"
},
"$:/language/Date/Long/Month/1": {
"title": "$:/language/Date/Long/Month/1",
"text": "January"
},
"$:/language/Date/Long/Month/2": {
"title": "$:/language/Date/Long/Month/2",
"text": "February"
},
"$:/language/Date/Long/Month/3": {
"title": "$:/language/Date/Long/Month/3",
"text": "March"
},
"$:/language/Date/Long/Month/4": {
"title": "$:/language/Date/Long/Month/4",
"text": "April"
},
"$:/language/Date/Long/Month/5": {
"title": "$:/language/Date/Long/Month/5",
"text": "May"
},
"$:/language/Date/Long/Month/6": {
"title": "$:/language/Date/Long/Month/6",
"text": "June"
},
"$:/language/Date/Long/Month/7": {
"title": "$:/language/Date/Long/Month/7",
"text": "July"
},
"$:/language/Date/Long/Month/8": {
"title": "$:/language/Date/Long/Month/8",
"text": "August"
},
"$:/language/Date/Long/Month/9": {
"title": "$:/language/Date/Long/Month/9",
"text": "September"
},
"$:/language/Date/Long/Month/10": {
"title": "$:/language/Date/Long/Month/10",
"text": "October"
},
"$:/language/Date/Long/Month/11": {
"title": "$:/language/Date/Long/Month/11",
"text": "November"
},
"$:/language/Date/Long/Month/12": {
"title": "$:/language/Date/Long/Month/12",
"text": "December"
},
"$:/language/Date/Period/am": {
"title": "$:/language/Date/Period/am",
"text": "am"
},
"$:/language/Date/Period/pm": {
"title": "$:/language/Date/Period/pm",
"text": "pm"
},
"$:/language/Date/Short/Day/0": {
"title": "$:/language/Date/Short/Day/0",
"text": "Sun"
},
"$:/language/Date/Short/Day/1": {
"title": "$:/language/Date/Short/Day/1",
"text": "Mon"
},
"$:/language/Date/Short/Day/2": {
"title": "$:/language/Date/Short/Day/2",
"text": "Tue"
},
"$:/language/Date/Short/Day/3": {
"title": "$:/language/Date/Short/Day/3",
"text": "Wed"
},
"$:/language/Date/Short/Day/4": {
"title": "$:/language/Date/Short/Day/4",
"text": "Thu"
},
"$:/language/Date/Short/Day/5": {
"title": "$:/language/Date/Short/Day/5",
"text": "Fri"
},
"$:/language/Date/Short/Day/6": {
"title": "$:/language/Date/Short/Day/6",
"text": "Sat"
},
"$:/language/Date/Short/Month/1": {
"title": "$:/language/Date/Short/Month/1",
"text": "Jan"
},
"$:/language/Date/Short/Month/2": {
"title": "$:/language/Date/Short/Month/2",
"text": "Feb"
},
"$:/language/Date/Short/Month/3": {
"title": "$:/language/Date/Short/Month/3",
"text": "Mar"
},
"$:/language/Date/Short/Month/4": {
"title": "$:/language/Date/Short/Month/4",
"text": "Apr"
},
"$:/language/Date/Short/Month/5": {
"title": "$:/language/Date/Short/Month/5",
"text": "May"
},
"$:/language/Date/Short/Month/6": {
"title": "$:/language/Date/Short/Month/6",
"text": "Jun"
},
"$:/language/Date/Short/Month/7": {
"title": "$:/language/Date/Short/Month/7",
"text": "Jul"
},
"$:/language/Date/Short/Month/8": {
"title": "$:/language/Date/Short/Month/8",
"text": "Aug"
},
"$:/language/Date/Short/Month/9": {
"title": "$:/language/Date/Short/Month/9",
"text": "Sep"
},
"$:/language/Date/Short/Month/10": {
"title": "$:/language/Date/Short/Month/10",
"text": "Oct"
},
"$:/language/Date/Short/Month/11": {
"title": "$:/language/Date/Short/Month/11",
"text": "Nov"
},
"$:/language/Date/Short/Month/12": {
"title": "$:/language/Date/Short/Month/12",
"text": "Dec"
},
"$:/language/RelativeDate/Future/Days": {
"title": "$:/language/RelativeDate/Future/Days",
"text": "<<period>> days from now"
},
"$:/language/RelativeDate/Future/Hours": {
"title": "$:/language/RelativeDate/Future/Hours",
"text": "<<period>> hours from now"
},
"$:/language/RelativeDate/Future/Minutes": {
"title": "$:/language/RelativeDate/Future/Minutes",
"text": "<<period>> minutes from now"
},
"$:/language/RelativeDate/Future/Months": {
"title": "$:/language/RelativeDate/Future/Months",
"text": "<<period>> months from now"
},
"$:/language/RelativeDate/Future/Second": {
"title": "$:/language/RelativeDate/Future/Second",
"text": "1 second from now"
},
"$:/language/RelativeDate/Future/Seconds": {
"title": "$:/language/RelativeDate/Future/Seconds",
"text": "<<period>> seconds from now"
},
"$:/language/RelativeDate/Future/Years": {
"title": "$:/language/RelativeDate/Future/Years",
"text": "<<period>> years from now"
},
"$:/language/RelativeDate/Past/Days": {
"title": "$:/language/RelativeDate/Past/Days",
"text": "<<period>> days ago"
},
"$:/language/RelativeDate/Past/Hours": {
"title": "$:/language/RelativeDate/Past/Hours",
"text": "<<period>> hours ago"
},
"$:/language/RelativeDate/Past/Minutes": {
"title": "$:/language/RelativeDate/Past/Minutes",
"text": "<<period>> minutes ago"
},
"$:/language/RelativeDate/Past/Months": {
"title": "$:/language/RelativeDate/Past/Months",
"text": "<<period>> months ago"
},
"$:/language/RelativeDate/Past/Second": {
"title": "$:/language/RelativeDate/Past/Second",
"text": "1 second ago"
},
"$:/language/RelativeDate/Past/Seconds": {
"title": "$:/language/RelativeDate/Past/Seconds",
"text": "<<period>> seconds ago"
},
"$:/language/RelativeDate/Past/Years": {
"title": "$:/language/RelativeDate/Past/Years",
"text": "<<period>> years ago"
},
"$:/language/Docs/ModuleTypes/allfilteroperator": {
"title": "$:/language/Docs/ModuleTypes/allfilteroperator",
"text": "A sub-operator for the ''all'' filter operator."
},
"$:/language/Docs/ModuleTypes/animation": {
"title": "$:/language/Docs/ModuleTypes/animation",
"text": "Animations that may be used with the RevealWidget."
},
"$:/language/Docs/ModuleTypes/bitmapeditoroperation": {
"title": "$:/language/Docs/ModuleTypes/bitmapeditoroperation",
"text": "A bitmap editor toolbar operation."
},
"$:/language/Docs/ModuleTypes/command": {
"title": "$:/language/Docs/ModuleTypes/command",
"text": "Commands that can be executed under Node.js."
},
"$:/language/Docs/ModuleTypes/config": {
"title": "$:/language/Docs/ModuleTypes/config",
"text": "Data to be inserted into `$tw.config`."
},
"$:/language/Docs/ModuleTypes/filteroperator": {
"title": "$:/language/Docs/ModuleTypes/filteroperator",
"text": "Individual filter operator methods."
},
"$:/language/Docs/ModuleTypes/global": {
"title": "$:/language/Docs/ModuleTypes/global",
"text": "Global data to be inserted into `$tw`."
},
"$:/language/Docs/ModuleTypes/info": {
"title": "$:/language/Docs/ModuleTypes/info",
"text": "Publishes system information via the [[$:/temp/info-plugin]] pseudo-plugin."
},
"$:/language/Docs/ModuleTypes/isfilteroperator": {
"title": "$:/language/Docs/ModuleTypes/isfilteroperator",
"text": "Operands for the ''is'' filter operator."
},
"$:/language/Docs/ModuleTypes/library": {
"title": "$:/language/Docs/ModuleTypes/library",
"text": "Generic module type for general purpose JavaScript modules."
},
"$:/language/Docs/ModuleTypes/macro": {
"title": "$:/language/Docs/ModuleTypes/macro",
"text": "JavaScript macro definitions."
},
"$:/language/Docs/ModuleTypes/parser": {
"title": "$:/language/Docs/ModuleTypes/parser",
"text": "Parsers for different content types."
},
"$:/language/Docs/ModuleTypes/saver": {
"title": "$:/language/Docs/ModuleTypes/saver",
"text": "Savers handle different methods for saving files from the browser."
},
"$:/language/Docs/ModuleTypes/startup": {
"title": "$:/language/Docs/ModuleTypes/startup",
"text": "Startup functions."
},
"$:/language/Docs/ModuleTypes/storyview": {
"title": "$:/language/Docs/ModuleTypes/storyview",
"text": "Story views customise the animation and behaviour of list widgets."
},
"$:/language/Docs/ModuleTypes/texteditoroperation": {
"title": "$:/language/Docs/ModuleTypes/texteditoroperation",
"text": "A text editor toolbar operation."
},
"$:/language/Docs/ModuleTypes/tiddlerdeserializer": {
"title": "$:/language/Docs/ModuleTypes/tiddlerdeserializer",
"text": "Converts different content types into tiddlers."
},
"$:/language/Docs/ModuleTypes/tiddlerfield": {
"title": "$:/language/Docs/ModuleTypes/tiddlerfield",
"text": "Defines the behaviour of an individual tiddler field."
},
"$:/language/Docs/ModuleTypes/tiddlermethod": {
"title": "$:/language/Docs/ModuleTypes/tiddlermethod",
"text": "Adds methods to the `$tw.Tiddler` prototype."
},
"$:/language/Docs/ModuleTypes/upgrader": {
"title": "$:/language/Docs/ModuleTypes/upgrader",
"text": "Applies upgrade processing to tiddlers during an upgrade/import."
},
"$:/language/Docs/ModuleTypes/utils": {
"title": "$:/language/Docs/ModuleTypes/utils",
"text": "Adds methods to `$tw.utils`."
},
"$:/language/Docs/ModuleTypes/utils-node": {
"title": "$:/language/Docs/ModuleTypes/utils-node",
"text": "Adds Node.js-specific methods to `$tw.utils`."
},
"$:/language/Docs/ModuleTypes/widget": {
"title": "$:/language/Docs/ModuleTypes/widget",
"text": "Widgets encapsulate DOM rendering and refreshing."
},
"$:/language/Docs/ModuleTypes/wikimethod": {
"title": "$:/language/Docs/ModuleTypes/wikimethod",
"text": "Adds methods to `$tw.Wiki`."
},
"$:/language/Docs/ModuleTypes/wikirule": {
"title": "$:/language/Docs/ModuleTypes/wikirule",
"text": "Individual parser rules for the main WikiText parser."
},
"$:/language/Docs/PaletteColours/alert-background": {
"title": "$:/language/Docs/PaletteColours/alert-background",
"text": "Alert background"
},
"$:/language/Docs/PaletteColours/alert-border": {
"title": "$:/language/Docs/PaletteColours/alert-border",
"text": "Alert border"
},
"$:/language/Docs/PaletteColours/alert-highlight": {
"title": "$:/language/Docs/PaletteColours/alert-highlight",
"text": "Alert highlight"
},
"$:/language/Docs/PaletteColours/alert-muted-foreground": {
"title": "$:/language/Docs/PaletteColours/alert-muted-foreground",
"text": "Alert muted foreground"
},
"$:/language/Docs/PaletteColours/background": {
"title": "$:/language/Docs/PaletteColours/background",
"text": "General background"
},
"$:/language/Docs/PaletteColours/blockquote-bar": {
"title": "$:/language/Docs/PaletteColours/blockquote-bar",
"text": "Blockquote bar"
},
"$:/language/Docs/PaletteColours/button-background": {
"title": "$:/language/Docs/PaletteColours/button-background",
"text": "Default button background"
},
"$:/language/Docs/PaletteColours/button-border": {
"title": "$:/language/Docs/PaletteColours/button-border",
"text": "Default button border"
},
"$:/language/Docs/PaletteColours/button-foreground": {
"title": "$:/language/Docs/PaletteColours/button-foreground",
"text": "Default button foreground"
},
"$:/language/Docs/PaletteColours/dirty-indicator": {
"title": "$:/language/Docs/PaletteColours/dirty-indicator",
"text": "Unsaved changes indicator"
},
"$:/language/Docs/PaletteColours/code-background": {
"title": "$:/language/Docs/PaletteColours/code-background",
"text": "Code background"
},
"$:/language/Docs/PaletteColours/code-border": {
"title": "$:/language/Docs/PaletteColours/code-border",
"text": "Code border"
},
"$:/language/Docs/PaletteColours/code-foreground": {
"title": "$:/language/Docs/PaletteColours/code-foreground",
"text": "Code foreground"
},
"$:/language/Docs/PaletteColours/download-background": {
"title": "$:/language/Docs/PaletteColours/download-background",
"text": "Download button background"
},
"$:/language/Docs/PaletteColours/download-foreground": {
"title": "$:/language/Docs/PaletteColours/download-foreground",
"text": "Download button foreground"
},
"$:/language/Docs/PaletteColours/dragger-background": {
"title": "$:/language/Docs/PaletteColours/dragger-background",
"text": "Dragger background"
},
"$:/language/Docs/PaletteColours/dragger-foreground": {
"title": "$:/language/Docs/PaletteColours/dragger-foreground",
"text": "Dragger foreground"
},
"$:/language/Docs/PaletteColours/dropdown-background": {
"title": "$:/language/Docs/PaletteColours/dropdown-background",
"text": "Dropdown background"
},
"$:/language/Docs/PaletteColours/dropdown-border": {
"title": "$:/language/Docs/PaletteColours/dropdown-border",
"text": "Dropdown border"
},
"$:/language/Docs/PaletteColours/dropdown-tab-background-selected": {
"title": "$:/language/Docs/PaletteColours/dropdown-tab-background-selected",
"text": "Dropdown tab background for selected tabs"
},
"$:/language/Docs/PaletteColours/dropdown-tab-background": {
"title": "$:/language/Docs/PaletteColours/dropdown-tab-background",
"text": "Dropdown tab background"
},
"$:/language/Docs/PaletteColours/dropzone-background": {
"title": "$:/language/Docs/PaletteColours/dropzone-background",
"text": "Dropzone background"
},
"$:/language/Docs/PaletteColours/external-link-background-hover": {
"title": "$:/language/Docs/PaletteColours/external-link-background-hover",
"text": "External link background hover"
},
"$:/language/Docs/PaletteColours/external-link-background-visited": {
"title": "$:/language/Docs/PaletteColours/external-link-background-visited",
"text": "External link background visited"
},
"$:/language/Docs/PaletteColours/external-link-background": {
"title": "$:/language/Docs/PaletteColours/external-link-background",
"text": "External link background"
},
"$:/language/Docs/PaletteColours/external-link-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/external-link-foreground-hover",
"text": "External link foreground hover"
},
"$:/language/Docs/PaletteColours/external-link-foreground-visited": {
"title": "$:/language/Docs/PaletteColours/external-link-foreground-visited",
"text": "External link foreground visited"
},
"$:/language/Docs/PaletteColours/external-link-foreground": {
"title": "$:/language/Docs/PaletteColours/external-link-foreground",
"text": "External link foreground"
},
"$:/language/Docs/PaletteColours/foreground": {
"title": "$:/language/Docs/PaletteColours/foreground",
"text": "General foreground"
},
"$:/language/Docs/PaletteColours/message-background": {
"title": "$:/language/Docs/PaletteColours/message-background",
"text": "Message box background"
},
"$:/language/Docs/PaletteColours/message-border": {
"title": "$:/language/Docs/PaletteColours/message-border",
"text": "Message box border"
},
"$:/language/Docs/PaletteColours/message-foreground": {
"title": "$:/language/Docs/PaletteColours/message-foreground",
"text": "Message box foreground"
},
"$:/language/Docs/PaletteColours/modal-backdrop": {
"title": "$:/language/Docs/PaletteColours/modal-backdrop",
"text": "Modal backdrop"
},
"$:/language/Docs/PaletteColours/modal-background": {
"title": "$:/language/Docs/PaletteColours/modal-background",
"text": "Modal background"
},
"$:/language/Docs/PaletteColours/modal-border": {
"title": "$:/language/Docs/PaletteColours/modal-border",
"text": "Modal border"
},
"$:/language/Docs/PaletteColours/modal-footer-background": {
"title": "$:/language/Docs/PaletteColours/modal-footer-background",
"text": "Modal footer background"
},
"$:/language/Docs/PaletteColours/modal-footer-border": {
"title": "$:/language/Docs/PaletteColours/modal-footer-border",
"text": "Modal footer border"
},
"$:/language/Docs/PaletteColours/modal-header-border": {
"title": "$:/language/Docs/PaletteColours/modal-header-border",
"text": "Modal header border"
},
"$:/language/Docs/PaletteColours/muted-foreground": {
"title": "$:/language/Docs/PaletteColours/muted-foreground",
"text": "General muted foreground"
},
"$:/language/Docs/PaletteColours/notification-background": {
"title": "$:/language/Docs/PaletteColours/notification-background",
"text": "Notification background"
},
"$:/language/Docs/PaletteColours/notification-border": {
"title": "$:/language/Docs/PaletteColours/notification-border",
"text": "Notification border"
},
"$:/language/Docs/PaletteColours/page-background": {
"title": "$:/language/Docs/PaletteColours/page-background",
"text": "Page background"
},
"$:/language/Docs/PaletteColours/pre-background": {
"title": "$:/language/Docs/PaletteColours/pre-background",
"text": "Preformatted code background"
},
"$:/language/Docs/PaletteColours/pre-border": {
"title": "$:/language/Docs/PaletteColours/pre-border",
"text": "Preformatted code border"
},
"$:/language/Docs/PaletteColours/primary": {
"title": "$:/language/Docs/PaletteColours/primary",
"text": "General primary"
},
"$:/language/Docs/PaletteColours/sidebar-button-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-button-foreground",
"text": "Sidebar button foreground"
},
"$:/language/Docs/PaletteColours/sidebar-controls-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/sidebar-controls-foreground-hover",
"text": "Sidebar controls foreground hover"
},
"$:/language/Docs/PaletteColours/sidebar-controls-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-controls-foreground",
"text": "Sidebar controls foreground"
},
"$:/language/Docs/PaletteColours/sidebar-foreground-shadow": {
"title": "$:/language/Docs/PaletteColours/sidebar-foreground-shadow",
"text": "Sidebar foreground shadow"
},
"$:/language/Docs/PaletteColours/sidebar-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-foreground",
"text": "Sidebar foreground"
},
"$:/language/Docs/PaletteColours/sidebar-muted-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/sidebar-muted-foreground-hover",
"text": "Sidebar muted foreground hover"
},
"$:/language/Docs/PaletteColours/sidebar-muted-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-muted-foreground",
"text": "Sidebar muted foreground"
},
"$:/language/Docs/PaletteColours/sidebar-tab-background-selected": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-background-selected",
"text": "Sidebar tab background for selected tabs"
},
"$:/language/Docs/PaletteColours/sidebar-tab-background": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-background",
"text": "Sidebar tab background"
},
"$:/language/Docs/PaletteColours/sidebar-tab-border-selected": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-border-selected",
"text": "Sidebar tab border for selected tabs"
},
"$:/language/Docs/PaletteColours/sidebar-tab-border": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-border",
"text": "Sidebar tab border"
},
"$:/language/Docs/PaletteColours/sidebar-tab-divider": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-divider",
"text": "Sidebar tab divider"
},
"$:/language/Docs/PaletteColours/sidebar-tab-foreground-selected": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-foreground-selected",
"text": "Sidebar tab foreground for selected tabs"
},
"$:/language/Docs/PaletteColours/sidebar-tab-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-tab-foreground",
"text": "Sidebar tab foreground"
},
"$:/language/Docs/PaletteColours/sidebar-tiddler-link-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/sidebar-tiddler-link-foreground-hover",
"text": "Sidebar tiddler link foreground hover"
},
"$:/language/Docs/PaletteColours/sidebar-tiddler-link-foreground": {
"title": "$:/language/Docs/PaletteColours/sidebar-tiddler-link-foreground",
"text": "Sidebar tiddler link foreground"
},
"$:/language/Docs/PaletteColours/site-title-foreground": {
"title": "$:/language/Docs/PaletteColours/site-title-foreground",
"text": "Site title foreground"
},
"$:/language/Docs/PaletteColours/static-alert-foreground": {
"title": "$:/language/Docs/PaletteColours/static-alert-foreground",
"text": "Static alert foreground"
},
"$:/language/Docs/PaletteColours/tab-background-selected": {
"title": "$:/language/Docs/PaletteColours/tab-background-selected",
"text": "Tab background for selected tabs"
},
"$:/language/Docs/PaletteColours/tab-background": {
"title": "$:/language/Docs/PaletteColours/tab-background",
"text": "Tab background"
},
"$:/language/Docs/PaletteColours/tab-border-selected": {
"title": "$:/language/Docs/PaletteColours/tab-border-selected",
"text": "Tab border for selected tabs"
},
"$:/language/Docs/PaletteColours/tab-border": {
"title": "$:/language/Docs/PaletteColours/tab-border",
"text": "Tab border"
},
"$:/language/Docs/PaletteColours/tab-divider": {
"title": "$:/language/Docs/PaletteColours/tab-divider",
"text": "Tab divider"
},
"$:/language/Docs/PaletteColours/tab-foreground-selected": {
"title": "$:/language/Docs/PaletteColours/tab-foreground-selected",
"text": "Tab foreground for selected tabs"
},
"$:/language/Docs/PaletteColours/tab-foreground": {
"title": "$:/language/Docs/PaletteColours/tab-foreground",
"text": "Tab foreground"
},
"$:/language/Docs/PaletteColours/table-border": {
"title": "$:/language/Docs/PaletteColours/table-border",
"text": "Table border"
},
"$:/language/Docs/PaletteColours/table-footer-background": {
"title": "$:/language/Docs/PaletteColours/table-footer-background",
"text": "Table footer background"
},
"$:/language/Docs/PaletteColours/table-header-background": {
"title": "$:/language/Docs/PaletteColours/table-header-background",
"text": "Table header background"
},
"$:/language/Docs/PaletteColours/tag-background": {
"title": "$:/language/Docs/PaletteColours/tag-background",
"text": "Tag background"
},
"$:/language/Docs/PaletteColours/tag-foreground": {
"title": "$:/language/Docs/PaletteColours/tag-foreground",
"text": "Tag foreground"
},
"$:/language/Docs/PaletteColours/tiddler-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-background",
"text": "Tiddler background"
},
"$:/language/Docs/PaletteColours/tiddler-border": {
"title": "$:/language/Docs/PaletteColours/tiddler-border",
"text": "Tiddler border"
},
"$:/language/Docs/PaletteColours/tiddler-controls-foreground-hover": {
"title": "$:/language/Docs/PaletteColours/tiddler-controls-foreground-hover",
"text": "Tiddler controls foreground hover"
},
"$:/language/Docs/PaletteColours/tiddler-controls-foreground-selected": {
"title": "$:/language/Docs/PaletteColours/tiddler-controls-foreground-selected",
"text": "Tiddler controls foreground for selected controls"
},
"$:/language/Docs/PaletteColours/tiddler-controls-foreground": {
"title": "$:/language/Docs/PaletteColours/tiddler-controls-foreground",
"text": "Tiddler controls foreground"
},
"$:/language/Docs/PaletteColours/tiddler-editor-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-background",
"text": "Tiddler editor background"
},
"$:/language/Docs/PaletteColours/tiddler-editor-border-image": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-border-image",
"text": "Tiddler editor border image"
},
"$:/language/Docs/PaletteColours/tiddler-editor-border": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-border",
"text": "Tiddler editor border"
},
"$:/language/Docs/PaletteColours/tiddler-editor-fields-even": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-fields-even",
"text": "Tiddler editor background for even fields"
},
"$:/language/Docs/PaletteColours/tiddler-editor-fields-odd": {
"title": "$:/language/Docs/PaletteColours/tiddler-editor-fields-odd",
"text": "Tiddler editor background for odd fields"
},
"$:/language/Docs/PaletteColours/tiddler-info-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-info-background",
"text": "Tiddler info panel background"
},
"$:/language/Docs/PaletteColours/tiddler-info-border": {
"title": "$:/language/Docs/PaletteColours/tiddler-info-border",
"text": "Tiddler info panel border"
},
"$:/language/Docs/PaletteColours/tiddler-info-tab-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-info-tab-background",
"text": "Tiddler info panel tab background"
},
"$:/language/Docs/PaletteColours/tiddler-link-background": {
"title": "$:/language/Docs/PaletteColours/tiddler-link-background",
"text": "Tiddler link background"
},
"$:/language/Docs/PaletteColours/tiddler-link-foreground": {
"title": "$:/language/Docs/PaletteColours/tiddler-link-foreground",
"text": "Tiddler link foreground"
},
"$:/language/Docs/PaletteColours/tiddler-subtitle-foreground": {
"title": "$:/language/Docs/PaletteColours/tiddler-subtitle-foreground",
"text": "Tiddler subtitle foreground"
},
"$:/language/Docs/PaletteColours/tiddler-title-foreground": {
"title": "$:/language/Docs/PaletteColours/tiddler-title-foreground",
"text": "Tiddler title foreground"
},
"$:/language/Docs/PaletteColours/toolbar-new-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-new-button",
"text": "Toolbar 'new tiddler' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-options-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-options-button",
"text": "Toolbar 'options' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-save-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-save-button",
"text": "Toolbar 'save' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-info-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-info-button",
"text": "Toolbar 'info' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-edit-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-edit-button",
"text": "Toolbar 'edit' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-close-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-close-button",
"text": "Toolbar 'close' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-delete-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-delete-button",
"text": "Toolbar 'delete' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-cancel-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-cancel-button",
"text": "Toolbar 'cancel' button foreground"
},
"$:/language/Docs/PaletteColours/toolbar-done-button": {
"title": "$:/language/Docs/PaletteColours/toolbar-done-button",
"text": "Toolbar 'done' button foreground"
},
"$:/language/Docs/PaletteColours/untagged-background": {
"title": "$:/language/Docs/PaletteColours/untagged-background",
"text": "Untagged pill background"
},
"$:/language/Docs/PaletteColours/very-muted-foreground": {
"title": "$:/language/Docs/PaletteColours/very-muted-foreground",
"text": "Very muted foreground"
},
"$:/language/EditTemplate/Body/External/Hint": {
"title": "$:/language/EditTemplate/Body/External/Hint",
"text": "This is an external tiddler stored outside of the main TiddlyWiki file. You can edit the tags and fields but cannot directly edit the content itself"
},
"$:/language/EditTemplate/Body/Placeholder": {
"title": "$:/language/EditTemplate/Body/Placeholder",
"text": "Type the text for this tiddler"
},
"$:/language/EditTemplate/Body/Preview/Type/Output": {
"title": "$:/language/EditTemplate/Body/Preview/Type/Output",
"text": "output"
},
"$:/language/EditTemplate/Field/Remove/Caption": {
"title": "$:/language/EditTemplate/Field/Remove/Caption",
"text": "remove field"
},
"$:/language/EditTemplate/Field/Remove/Hint": {
"title": "$:/language/EditTemplate/Field/Remove/Hint",
"text": "Remove field"
},
"$:/language/EditTemplate/Fields/Add/Button": {
"title": "$:/language/EditTemplate/Fields/Add/Button",
"text": "add"
},
"$:/language/EditTemplate/Fields/Add/Name/Placeholder": {
"title": "$:/language/EditTemplate/Fields/Add/Name/Placeholder",
"text": "field name"
},
"$:/language/EditTemplate/Fields/Add/Prompt": {
"title": "$:/language/EditTemplate/Fields/Add/Prompt",
"text": "Add a new field:"
},
"$:/language/EditTemplate/Fields/Add/Value/Placeholder": {
"title": "$:/language/EditTemplate/Fields/Add/Value/Placeholder",
"text": "field value"
},
"$:/language/EditTemplate/Fields/Add/Dropdown/System": {
"title": "$:/language/EditTemplate/Fields/Add/Dropdown/System",
"text": "System fields"
},
"$:/language/EditTemplate/Fields/Add/Dropdown/User": {
"title": "$:/language/EditTemplate/Fields/Add/Dropdown/User",
"text": "User fields"
},
"$:/language/EditTemplate/Shadow/Warning": {
"title": "$:/language/EditTemplate/Shadow/Warning",
"text": "This is a shadow tiddler. Any changes you make will override the default version from the plugin <<pluginLink>>"
},
"$:/language/EditTemplate/Shadow/OverriddenWarning": {
"title": "$:/language/EditTemplate/Shadow/OverriddenWarning",
"text": "This is a modified shadow tiddler. You can revert to the default version in the plugin <<pluginLink>> by deleting this tiddler"
},
"$:/language/EditTemplate/Tags/Add/Button": {
"title": "$:/language/EditTemplate/Tags/Add/Button",
"text": "add"
},
"$:/language/EditTemplate/Tags/Add/Placeholder": {
"title": "$:/language/EditTemplate/Tags/Add/Placeholder",
"text": "tag name"
},
"$:/language/EditTemplate/Tags/Dropdown/Caption": {
"title": "$:/language/EditTemplate/Tags/Dropdown/Caption",
"text": "tag list"
},
"$:/language/EditTemplate/Tags/Dropdown/Hint": {
"title": "$:/language/EditTemplate/Tags/Dropdown/Hint",
"text": "Show tag list"
},
"$:/language/EditTemplate/Title/BadCharacterWarning": {
"title": "$:/language/EditTemplate/Title/BadCharacterWarning",
"text": "Warning: avoid using any of the characters <<bad-chars>> in tiddler titles"
},
"$:/language/EditTemplate/Title/Exists/Prompt": {
"title": "$:/language/EditTemplate/Title/Exists/Prompt",
"text": "Target tiddler already exists"
},
"$:/language/EditTemplate/Title/Relink/Prompt": {
"title": "$:/language/EditTemplate/Title/Relink/Prompt",
"text": "Update ''<$text text=<<fromTitle>>/>'' to ''<$text text=<<toTitle>>/>'' in the //tags// and //list// fields of other tiddlers"
},
"$:/language/EditTemplate/Type/Dropdown/Caption": {
"title": "$:/language/EditTemplate/Type/Dropdown/Caption",
"text": "content type list"
},
"$:/language/EditTemplate/Type/Dropdown/Hint": {
"title": "$:/language/EditTemplate/Type/Dropdown/Hint",
"text": "Show content type list"
},
"$:/language/EditTemplate/Type/Delete/Caption": {
"title": "$:/language/EditTemplate/Type/Delete/Caption",
"text": "delete content type"
},
"$:/language/EditTemplate/Type/Delete/Hint": {
"title": "$:/language/EditTemplate/Type/Delete/Hint",
"text": "Delete content type"
},
"$:/language/EditTemplate/Type/Placeholder": {
"title": "$:/language/EditTemplate/Type/Placeholder",
"text": "content type"
},
"$:/language/EditTemplate/Type/Prompt": {
"title": "$:/language/EditTemplate/Type/Prompt",
"text": "Type:"
},
"$:/language/Exporters/StaticRiver": {
"title": "$:/language/Exporters/StaticRiver",
"text": "Static HTML"
},
"$:/language/Exporters/JsonFile": {
"title": "$:/language/Exporters/JsonFile",
"text": "JSON file"
},
"$:/language/Exporters/CsvFile": {
"title": "$:/language/Exporters/CsvFile",
"text": "CSV file"
},
"$:/language/Exporters/TidFile": {
"title": "$:/language/Exporters/TidFile",
"text": "\".tid\" file"
},
"$:/language/Docs/Fields/_canonical_uri": {
"title": "$:/language/Docs/Fields/_canonical_uri",
"text": "The full URI of an external image tiddler"
},
"$:/language/Docs/Fields/bag": {
"title": "$:/language/Docs/Fields/bag",
"text": "The name of the bag from which a tiddler came"
},
"$:/language/Docs/Fields/caption": {
"title": "$:/language/Docs/Fields/caption",
"text": "The text to be displayed on a tab or button"
},
"$:/language/Docs/Fields/color": {
"title": "$:/language/Docs/Fields/color",
"text": "The CSS color value associated with a tiddler"
},
"$:/language/Docs/Fields/component": {
"title": "$:/language/Docs/Fields/component",
"text": "The name of the component responsible for an [[alert tiddler|AlertMechanism]]"
},
"$:/language/Docs/Fields/current-tiddler": {
"title": "$:/language/Docs/Fields/current-tiddler",
"text": "Used to cache the top tiddler in a [[history list|HistoryMechanism]]"
},
"$:/language/Docs/Fields/created": {
"title": "$:/language/Docs/Fields/created",
"text": "The date a tiddler was created"
},
"$:/language/Docs/Fields/creator": {
"title": "$:/language/Docs/Fields/creator",
"text": "The name of the person who created a tiddler"
},
"$:/language/Docs/Fields/dependents": {
"title": "$:/language/Docs/Fields/dependents",
"text": "For a plugin, lists the dependent plugin titles"
},
"$:/language/Docs/Fields/description": {
"title": "$:/language/Docs/Fields/description",
"text": "The descriptive text for a plugin, or a modal dialogue"
},
"$:/language/Docs/Fields/draft.of": {
"title": "$:/language/Docs/Fields/draft.of",
"text": "For draft tiddlers, contains the title of the tiddler of which this is a draft"
},
"$:/language/Docs/Fields/draft.title": {
"title": "$:/language/Docs/Fields/draft.title",
"text": "For draft tiddlers, contains the proposed new title of the tiddler"
},
"$:/language/Docs/Fields/footer": {
"title": "$:/language/Docs/Fields/footer",
"text": "The footer text for a wizard"
},
"$:/language/Docs/Fields/hack-to-give-us-something-to-compare-against": {
"title": "$:/language/Docs/Fields/hack-to-give-us-something-to-compare-against",
"text": "A temporary storage field used in [[$:/core/templates/static.content]]"
},
"$:/language/Docs/Fields/icon": {
"title": "$:/language/Docs/Fields/icon",
"text": "The title of the tiddler containing the icon associated with a tiddler"
},
"$:/language/Docs/Fields/library": {
"title": "$:/language/Docs/Fields/library",
"text": "If set to \"yes\" indicates that a tiddler should be saved as a JavaScript library"
},
"$:/language/Docs/Fields/list": {
"title": "$:/language/Docs/Fields/list",
"text": "An ordered list of tiddler titles associated with a tiddler"
},
"$:/language/Docs/Fields/list-before": {
"title": "$:/language/Docs/Fields/list-before",
"text": "If set, the title of a tiddler before which this tiddler should be added to the ordered list of tiddler titles, or at the start of the list if this field is present but empty"
},
"$:/language/Docs/Fields/list-after": {
"title": "$:/language/Docs/Fields/list-after",
"text": "If set, the title of the tiddler after which this tiddler should be added to the ordered list of tiddler titles"
},
"$:/language/Docs/Fields/modified": {
"title": "$:/language/Docs/Fields/modified",
"text": "The date and time at which a tiddler was last modified"
},
"$:/language/Docs/Fields/modifier": {
"title": "$:/language/Docs/Fields/modifier",
"text": "The tiddler title associated with the person who last modified a tiddler"
},
"$:/language/Docs/Fields/name": {
"title": "$:/language/Docs/Fields/name",
"text": "The human readable name associated with a plugin tiddler"
},
"$:/language/Docs/Fields/plugin-priority": {
"title": "$:/language/Docs/Fields/plugin-priority",
"text": "A numerical value indicating the priority of a plugin tiddler"
},
"$:/language/Docs/Fields/plugin-type": {
"title": "$:/language/Docs/Fields/plugin-type",
"text": "The type of plugin in a plugin tiddler"
},
"$:/language/Docs/Fields/revision": {
"title": "$:/language/Docs/Fields/revision",
"text": "The revision of the tiddler held at the server"
},
"$:/language/Docs/Fields/released": {
"title": "$:/language/Docs/Fields/released",
"text": "Date of a TiddlyWiki release"
},
"$:/language/Docs/Fields/source": {
"title": "$:/language/Docs/Fields/source",
"text": "The source URL associated with a tiddler"
},
"$:/language/Docs/Fields/subtitle": {
"title": "$:/language/Docs/Fields/subtitle",
"text": "The subtitle text for a wizard"
},
"$:/language/Docs/Fields/tags": {
"title": "$:/language/Docs/Fields/tags",
"text": "A list of tags associated with a tiddler"
},
"$:/language/Docs/Fields/text": {
"title": "$:/language/Docs/Fields/text",
"text": "The body text of a tiddler"
},
"$:/language/Docs/Fields/title": {
"title": "$:/language/Docs/Fields/title",
"text": "The unique name of a tiddler"
},
"$:/language/Docs/Fields/type": {
"title": "$:/language/Docs/Fields/type",
"text": "The content type of a tiddler"
},
"$:/language/Docs/Fields/version": {
"title": "$:/language/Docs/Fields/version",
"text": "Version information for a plugin"
},
"$:/language/Filters/AllTiddlers": {
"title": "$:/language/Filters/AllTiddlers",
"text": "All tiddlers except system tiddlers"
},
"$:/language/Filters/RecentSystemTiddlers": {
"title": "$:/language/Filters/RecentSystemTiddlers",
"text": "Recently modified tiddlers, including system tiddlers"
},
"$:/language/Filters/RecentTiddlers": {
"title": "$:/language/Filters/RecentTiddlers",
"text": "Recently modified tiddlers"
},
"$:/language/Filters/AllTags": {
"title": "$:/language/Filters/AllTags",
"text": "All tags except system tags"
},
"$:/language/Filters/Missing": {
"title": "$:/language/Filters/Missing",
"text": "Missing tiddlers"
},
"$:/language/Filters/Drafts": {
"title": "$:/language/Filters/Drafts",
"text": "Draft tiddlers"
},
"$:/language/Filters/Orphans": {
"title": "$:/language/Filters/Orphans",
"text": "Orphan tiddlers"
},
"$:/language/Filters/SystemTiddlers": {
"title": "$:/language/Filters/SystemTiddlers",
"text": "System tiddlers"
},
"$:/language/Filters/ShadowTiddlers": {
"title": "$:/language/Filters/ShadowTiddlers",
"text": "Shadow tiddlers"
},
"$:/language/Filters/OverriddenShadowTiddlers": {
"title": "$:/language/Filters/OverriddenShadowTiddlers",
"text": "Overridden shadow tiddlers"
},
"$:/language/Filters/SystemTags": {
"title": "$:/language/Filters/SystemTags",
"text": "System tags"
},
"$:/language/Filters/StoryList": {
"title": "$:/language/Filters/StoryList",
"text": "Tiddlers in the story river, excluding <$text text=\"$:/AdvancedSearch\"/>"
},
"$:/language/Filters/TypedTiddlers": {
"title": "$:/language/Filters/TypedTiddlers",
"text": "Non wiki-text tiddlers"
},
"GettingStarted": {
"title": "GettingStarted",
"text": "\\define lingo-base() $:/language/ControlPanel/Basics/\nWelcome to ~TiddlyWiki and the ~TiddlyWiki community\n\nBefore you start storing important information in ~TiddlyWiki it is important to make sure that you can reliably save changes. See http://tiddlywiki.com/#GettingStarted for details\n\n!! Set up this ~TiddlyWiki\n\n<div class=\"tc-control-panel\">\n\n|<$link to=\"$:/SiteTitle\"><<lingo Title/Prompt>></$link> |<$edit-text tiddler=\"$:/SiteTitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/SiteSubtitle\"><<lingo Subtitle/Prompt>></$link> |<$edit-text tiddler=\"$:/SiteSubtitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/DefaultTiddlers\"><<lingo DefaultTiddlers/Prompt>></$link> |<<lingo DefaultTiddlers/TopHint>><br> <$edit tag=\"textarea\" tiddler=\"$:/DefaultTiddlers\"/><br>//<<lingo DefaultTiddlers/BottomHint>>// |\n</div>\n\nSee the [[control panel|$:/ControlPanel]] for more options.\n"
},
"$:/language/Help/build": {
"title": "$:/language/Help/build",
"description": "Automatically run configured commands",
"text": "Build the specified build targets for the current wiki. If no build targets are specified then all available targets will be built.\n\n```\n--build <target> [<target> ...]\n```\n\nBuild targets are defined in the `tiddlywiki.info` file of a wiki folder.\n\n"
},
"$:/language/Help/clearpassword": {
"title": "$:/language/Help/clearpassword",
"description": "Clear a password for subsequent crypto operations",
"text": "Clear the password for subsequent crypto operations\n\n```\n--clearpassword\n```\n"
},
"$:/language/Help/default": {
"title": "$:/language/Help/default",
"text": "\\define commandTitle()\n$:/language/Help/$(command)$\n\\end\n```\nusage: tiddlywiki [<wikifolder>] [--<command> [<args>...]...]\n```\n\nAvailable commands:\n\n<ul>\n<$list filter=\"[commands[]sort[title]]\" variable=\"command\">\n<li><$link to=<<commandTitle>>><$macrocall $name=\"command\" $type=\"text/plain\" $output=\"text/plain\"/></$link>: <$transclude tiddler=<<commandTitle>> field=\"description\"/></li>\n</$list>\n</ul>\n\nTo get detailed help on a command:\n\n```\ntiddlywiki --help <command>\n```\n"
},
"$:/language/Help/editions": {
"title": "$:/language/Help/editions",
"description": "Lists the available editions of TiddlyWiki",
"text": "Lists the names and descriptions of the available editions. You can create a new wiki of a specified edition with the `--init` command.\n\n```\n--editions\n```\n"
},
"$:/language/Help/fetch": {
"title": "$:/language/Help/fetch",
"description": "Fetch tiddlers from wiki by URL",
"text": "Fetch one or more files over HTTP/HTTPS, and import the tiddlers matching a filter, optionally transforming the incoming titles.\n\n```\n--fetch file <url> <import-filter> <transform-filter>\n--fetch files <url-filter> <import-filter> <transform-filter>\n```\n\nWith the \"file\" variant only a single file is fetched and the first parameter is the URL of the file to read.\n\nWith the \"files\" variant, multiple files are fetched and the first parameter is a filter yielding a list of URLs of the files to read. For example, given a set of tiddlers tagged \"remote-server\" that have a field \"url\" the filter `[tag[remote-server]get[url]]` will retrieve all the available URLs.\n\nThe `<import-filter>` parameter specifies a filter determining which tiddlers are imported. It defaults to `[all[tiddlers]]` if not provided.\n\nThe `<transform-filter>` parameter specifies an optional filter that transforms the titles of the imported tiddlers. For example, `[addprefix[$:/myimports/]]` would add the prefix `$:/myimports/` to each title.\n\nPreceding the `--fetch` command with `--verbose` will output progress information during the import.\n\nNote that TiddlyWiki will not fetch an older version of an already loaded plugin.\n\nThe following example retrieves all the non-system tiddlers from http://tiddlywiki.com and saves them to a JSON file:\n\n```\ntiddlywiki --verbose --fetch file \"http://tiddlywiki.com/\" \"[!is[system]]\" \"\" --rendertiddler \"$:/core/templates/exporters/JsonFile\" output.json text/plain \"\" exportFilter \"[!is[system]]\"\n```\n\n"
},
"$:/language/Help/help": {
"title": "$:/language/Help/help",
"description": "Display help for TiddlyWiki commands",
"text": "Displays help text for a command:\n\n```\n--help [<command>]\n```\n\nIf the command name is omitted then a list of available commands is displayed.\n"
},
"$:/language/Help/init": {
"title": "$:/language/Help/init",
"description": "Initialise a new wiki folder",
"text": "Initialise an empty [[WikiFolder|WikiFolders]] with a copy of the specified edition.\n\n```\n--init <edition> [<edition> ...]\n```\n\nFor example:\n\n```\ntiddlywiki ./MyWikiFolder --init empty\n```\n\nNote:\n\n* The wiki folder directory will be created if necessary\n* The \"edition\" defaults to ''empty''\n* The init command will fail if the wiki folder is not empty\n* The init command removes any `includeWikis` definitions in the edition's `tiddlywiki.info` file\n* When multiple editions are specified, editions initialised later will overwrite any files shared with earlier editions (so, the final `tiddlywiki.info` file will be copied from the last edition)\n* `--editions` returns a list of available editions\n"
},
"$:/language/Help/load": {
"title": "$:/language/Help/load",
"description": "Load tiddlers from a file",
"text": "Load tiddlers from 2.x.x TiddlyWiki files (`.html`), `.tiddler`, `.tid`, `.json` or other files\n\n```\n--load <filepath>\n```\n\nTo load tiddlers from an encrypted TiddlyWiki file you should first specify the password with the PasswordCommand. For example:\n\n```\ntiddlywiki ./MyWiki --password pa55w0rd --load my_encrypted_wiki.html\n```\n\nNote that TiddlyWiki will not load an older version of an already loaded plugin.\n"
},
"$:/language/Help/makelibrary": {
"title": "$:/language/Help/makelibrary",
"description": "Construct library plugin required by upgrade process",
"text": "Constructs the `$:/UpgradeLibrary` tiddler for the upgrade process.\n\nThe upgrade library is formatted as an ordinary plugin tiddler with the plugin type `library`. It contains a copy of each of the plugins, themes and language packs available within the TiddlyWiki5 repository.\n\nThis command is intended for internal use; it is only relevant to users constructing a custom upgrade procedure.\n\n```\n--makelibrary <title>\n```\n\nThe title argument defaults to `$:/UpgradeLibrary`.\n"
},
"$:/language/Help/notfound": {
"title": "$:/language/Help/notfound",
"text": "No such help item"
},
"$:/language/Help/output": {
"title": "$:/language/Help/output",
"description": "Set the base output directory for subsequent commands",
"text": "Sets the base output directory for subsequent commands. The default output directory is the `output` subdirectory of the edition directory.\n\n```\n--output <pathname>\n```\n\nIf the specified pathname is relative then it is resolved relative to the current working directory. For example `--output .` sets the output directory to the current working directory.\n\n"
},
"$:/language/Help/password": {
"title": "$:/language/Help/password",
"description": "Set a password for subsequent crypto operations",
"text": "Set a password for subsequent crypto operations\n\n```\n--password <password>\n```\n\n''Note'': This should not be used for serving TiddlyWiki with password protection. Instead, see the password option under the [[ServerCommand]].\n"
},
"$:/language/Help/rendertiddler": {
"title": "$:/language/Help/rendertiddler",
"description": "Render an individual tiddler as a specified ContentType",
"text": "Render an individual tiddler as a specified ContentType, defaulting to `text/html` and save it to the specified filename.\n\nOptionally the title of a template tiddler can be specified, in which case the template tiddler is rendered with the \"currentTiddler\" variable set to the tiddler that is being rendered (the first parameter value).\n\nA name and value for an additional variable may optionally also be specified.\n\n```\n--rendertiddler <title> <filename> [<type>] [<template>] [<name>] [<value>]\n```\n\nBy default, the filename is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nAny missing directories in the path to the filename are automatically created.\n\nFor example, the following command saves all tiddlers matching the filter `[tag[done]]` to a JSON file titled `output.json` by employing the core template `$:/core/templates/exporters/JsonFile`.\n\n```\n--rendertiddler \"$:/core/templates/exporters/JsonFile\" output.json text/plain \"\" exportFilter \"[tag[done]]\"\n```\n"
},
"$:/language/Help/rendertiddlers": {
"title": "$:/language/Help/rendertiddlers",
"description": "Render tiddlers matching a filter to a specified ContentType",
"text": "Render a set of tiddlers matching a filter to separate files of a specified ContentType (defaults to `text/html`) and extension (defaults to `.html`).\n\n```\n--rendertiddlers <filter> <template> <pathname> [<type>] [<extension>] [\"noclean\"]\n```\n\nFor example:\n\n```\n--rendertiddlers [!is[system]] $:/core/templates/static.tiddler.html ./static text/plain\n```\n\nBy default, the pathname is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nAny files in the target directory are deleted unless the ''noclean'' flag is specified. The target directory is recursively created if it is missing.\n"
},
"$:/language/Help/savetiddler": {
"title": "$:/language/Help/savetiddler",
"description": "Saves a raw tiddler to a file",
"text": "Saves an individual tiddler in its raw text or binary format to the specified filename.\n\n```\n--savetiddler <title> <filename>\n```\n\nBy default, the filename is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nAny missing directories in the path to the filename are automatically created.\n"
},
"$:/language/Help/savetiddlers": {
"title": "$:/language/Help/savetiddlers",
"description": "Saves a group of raw tiddlers to a directory",
"text": "Saves a group of tiddlers in their raw text or binary format to the specified directory.\n\n```\n--savetiddlers <filter> <pathname> [\"noclean\"]\n```\n\nBy default, the pathname is resolved relative to the `output` subdirectory of the edition directory. The `--output` command can be used to direct output to a different directory.\n\nThe output directory is cleared of existing files before saving the specified files. The deletion can be disabled by specifying the ''noclean'' flag.\n\nAny missing directories in the pathname are automatically created.\n"
},
"$:/language/Help/server": {
"title": "$:/language/Help/server",
"description": "Provides an HTTP server interface to TiddlyWiki",
"text": "The server built in to TiddlyWiki5 is very simple. Although compatible with TiddlyWeb it doesn't support many of the features needed for robust Internet-facing usage.\n\nAt the root, it serves a rendering of a specified tiddler. Away from the root, it serves individual tiddlers encoded in JSON, and supports the basic HTTP operations for `GET`, `PUT` and `DELETE`.\n\n```\n--server <port> <roottiddler> <rendertype> <servetype> <username> <password> <host> <pathprefix>\n```\n\nThe parameters are:\n\n* ''port'' - port number to serve from (defaults to \"8080\")\n* ''roottiddler'' - the tiddler to serve at the root (defaults to \"$:/core/save/all\")\n* ''rendertype'' - the content type to which the root tiddler should be rendered (defaults to \"text/plain\")\n* ''servetype'' - the content type with which the root tiddler should be served (defaults to \"text/html\")\n* ''username'' - the default username for signing edits\n* ''password'' - optional password for basic authentication\n* ''host'' - optional hostname to serve from (defaults to \"127.0.0.1\" aka \"localhost\")\n* ''pathprefix'' - optional prefix for paths\n\nIf the password parameter is specified then the browser will prompt the user for the username and password. Note that the password is transmitted in plain text so this implementation isn't suitable for general use.\n\nFor example:\n\n```\n--server 8080 $:/core/save/all text/plain text/html MyUserName passw0rd\n```\n\nThe username and password can be specified as empty strings if you need to set the hostname or pathprefix and don't want to require a password:\n\n```\n--server 8080 $:/core/save/all text/plain text/html \"\" \"\" 192.168.0.245\n```\n\nTo run multiple TiddlyWiki servers at the same time you'll need to put each one on a different port.\n"
},
"$:/language/Help/setfield": {
"title": "$:/language/Help/setfield",
"description": "Prepares external tiddlers for use",
"text": "//Note that this command is experimental and may change or be replaced before being finalised//\n\nSets the specified field of a group of tiddlers to the result of wikifying a template tiddler with the `currentTiddler` variable set to the tiddler.\n\n```\n--setfield <filter> <fieldname> <templatetitle> <rendertype>\n```\n\nThe parameters are:\n\n* ''filter'' - filter identifying the tiddlers to be affected\n* ''fieldname'' - the field to modify (defaults to \"text\")\n* ''templatetitle'' - the tiddler to wikify into the specified field. If blank or missing then the specified field is deleted\n* ''rendertype'' - the text type to render (defaults to \"text/plain\"; \"text/html\" can be used to include HTML tags)\n"
},
"$:/language/Help/unpackplugin": {
"title": "$:/language/Help/unpackplugin",
"description": "Unpack the payload tiddlers from a plugin",
"text": "Extract the payload tiddlers from a plugin, creating them as ordinary tiddlers:\n\n```\n--unpackplugin <title>\n```\n"
},
"$:/language/Help/verbose": {
"title": "$:/language/Help/verbose",
"description": "Triggers verbose output mode",
"text": "Triggers verbose output, useful for debugging\n\n```\n--verbose\n```\n"
},
"$:/language/Help/version": {
"title": "$:/language/Help/version",
"description": "Displays the version number of TiddlyWiki",
"text": "Displays the version number of TiddlyWiki.\n\n```\n--version\n```\n"
},
"$:/language/Import/Imported/Hint": {
"title": "$:/language/Import/Imported/Hint",
"text": "The following tiddlers were imported:"
},
"$:/language/Import/Listing/Cancel/Caption": {
"title": "$:/language/Import/Listing/Cancel/Caption",
"text": "Cancel"
},
"$:/language/Import/Listing/Hint": {
"title": "$:/language/Import/Listing/Hint",
"text": "These tiddlers are ready to import:"
},
"$:/language/Import/Listing/Import/Caption": {
"title": "$:/language/Import/Listing/Import/Caption",
"text": "Import"
},
"$:/language/Import/Listing/Select/Caption": {
"title": "$:/language/Import/Listing/Select/Caption",
"text": "Select"
},
"$:/language/Import/Listing/Status/Caption": {
"title": "$:/language/Import/Listing/Status/Caption",
"text": "Status"
},
"$:/language/Import/Listing/Title/Caption": {
"title": "$:/language/Import/Listing/Title/Caption",
"text": "Title"
},
"$:/language/Import/Upgrader/Plugins/Suppressed/Incompatible": {
"title": "$:/language/Import/Upgrader/Plugins/Suppressed/Incompatible",
"text": "Blocked incompatible or obsolete plugin"
},
"$:/language/Import/Upgrader/Plugins/Suppressed/Version": {
"title": "$:/language/Import/Upgrader/Plugins/Suppressed/Version",
"text": "Blocked plugin (due to incoming <<incoming>> being older than existing <<existing>>)"
},
"$:/language/Import/Upgrader/Plugins/Upgraded": {
"title": "$:/language/Import/Upgrader/Plugins/Upgraded",
"text": "Upgraded plugin from <<incoming>> to <<upgraded>>"
},
"$:/language/Import/Upgrader/State/Suppressed": {
"title": "$:/language/Import/Upgrader/State/Suppressed",
"text": "Blocked temporary state tiddler"
},
"$:/language/Import/Upgrader/System/Suppressed": {
"title": "$:/language/Import/Upgrader/System/Suppressed",
"text": "Blocked system tiddler"
},
"$:/language/Import/Upgrader/ThemeTweaks/Created": {
"title": "$:/language/Import/Upgrader/ThemeTweaks/Created",
"text": "Migrated theme tweak from <$text text=<<from>>/>"
},
"$:/language/AboveStory/ClassicPlugin/Warning": {
"title": "$:/language/AboveStory/ClassicPlugin/Warning",
"text": "It looks like you are trying to load a plugin designed for ~TiddlyWiki Classic. Please note that [[these plugins do not work with TiddlyWiki version 5.x.x|http://tiddlywiki.com/#TiddlyWikiClassic]]. ~TiddlyWiki Classic plugins detected:"
},
"$:/language/BinaryWarning/Prompt": {
"title": "$:/language/BinaryWarning/Prompt",
"text": "This tiddler contains binary data"
},
"$:/language/ClassicWarning/Hint": {
"title": "$:/language/ClassicWarning/Hint",
"text": "This tiddler is written in TiddlyWiki Classic wiki text format, which is not fully compatible with TiddlyWiki version 5. See http://tiddlywiki.com/static/Upgrading.html for more details."
},
"$:/language/ClassicWarning/Upgrade/Caption": {
"title": "$:/language/ClassicWarning/Upgrade/Caption",
"text": "upgrade"
},
"$:/language/CloseAll/Button": {
"title": "$:/language/CloseAll/Button",
"text": "close all"
},
"$:/language/ColourPicker/Recent": {
"title": "$:/language/ColourPicker/Recent",
"text": "Recent:"
},
"$:/language/ConfirmCancelTiddler": {
"title": "$:/language/ConfirmCancelTiddler",
"text": "Do you wish to discard changes to the tiddler \"<$text text=<<title>>/>\"?"
},
"$:/language/ConfirmDeleteTiddler": {
"title": "$:/language/ConfirmDeleteTiddler",
"text": "Do you wish to delete the tiddler \"<$text text=<<title>>/>\"?"
},
"$:/language/ConfirmOverwriteTiddler": {
"title": "$:/language/ConfirmOverwriteTiddler",
"text": "Do you wish to overwrite the tiddler \"<$text text=<<title>>/>\"?"
},
"$:/language/ConfirmEditShadowTiddler": {
"title": "$:/language/ConfirmEditShadowTiddler",
"text": "You are about to edit a ShadowTiddler. Any changes will override the default system making future upgrades non-trivial. Are you sure you want to edit \"<$text text=<<title>>/>\"?"
},
"$:/language/Count": {
"title": "$:/language/Count",
"text": "count"
},
"$:/language/DefaultNewTiddlerTitle": {
"title": "$:/language/DefaultNewTiddlerTitle",
"text": "New Tiddler"
},
"$:/language/DropMessage": {
"title": "$:/language/DropMessage",
"text": "Drop here (or use the 'Escape' key to cancel)"
},
"$:/language/Encryption/Cancel": {
"title": "$:/language/Encryption/Cancel",
"text": "Cancel"
},
"$:/language/Encryption/ConfirmClearPassword": {
"title": "$:/language/Encryption/ConfirmClearPassword",
"text": "Do you wish to clear the password? This will remove the encryption applied when saving this wiki"
},
"$:/language/Encryption/PromptSetPassword": {
"title": "$:/language/Encryption/PromptSetPassword",
"text": "Set a new password for this TiddlyWiki"
},
"$:/language/Encryption/Username": {
"title": "$:/language/Encryption/Username",
"text": "Username"
},
"$:/language/Encryption/Password": {
"title": "$:/language/Encryption/Password",
"text": "Password"
},
"$:/language/Encryption/RepeatPassword": {
"title": "$:/language/Encryption/RepeatPassword",
"text": "Repeat password"
},
"$:/language/Encryption/PasswordNoMatch": {
"title": "$:/language/Encryption/PasswordNoMatch",
"text": "Passwords do not match"
},
"$:/language/Encryption/SetPassword": {
"title": "$:/language/Encryption/SetPassword",
"text": "Set password"
},
"$:/language/Error/Caption": {
"title": "$:/language/Error/Caption",
"text": "Error"
},
"$:/language/Error/EditConflict": {
"title": "$:/language/Error/EditConflict",
"text": "File changed on server"
},
"$:/language/Error/Filter": {
"title": "$:/language/Error/Filter",
"text": "Filter error"
},
"$:/language/Error/FilterSyntax": {
"title": "$:/language/Error/FilterSyntax",
"text": "Syntax error in filter expression"
},
"$:/language/Error/IsFilterOperator": {
"title": "$:/language/Error/IsFilterOperator",
"text": "Filter Error: Unknown operand for the 'is' filter operator"
},
"$:/language/Error/LoadingPluginLibrary": {
"title": "$:/language/Error/LoadingPluginLibrary",
"text": "Error loading plugin library"
},
"$:/language/Error/RecursiveTransclusion": {
"title": "$:/language/Error/RecursiveTransclusion",
"text": "Recursive transclusion error in transclude widget"
},
"$:/language/Error/RetrievingSkinny": {
"title": "$:/language/Error/RetrievingSkinny",
"text": "Error retrieving skinny tiddler list"
},
"$:/language/Error/SavingToTWEdit": {
"title": "$:/language/Error/SavingToTWEdit",
"text": "Error saving to TWEdit"
},
"$:/language/Error/WhileSaving": {
"title": "$:/language/Error/WhileSaving",
"text": "Error while saving"
},
"$:/language/Error/XMLHttpRequest": {
"title": "$:/language/Error/XMLHttpRequest",
"text": "XMLHttpRequest error code"
},
"$:/language/InternalJavaScriptError/Title": {
"title": "$:/language/InternalJavaScriptError/Title",
"text": "Internal JavaScript Error"
},
"$:/language/InternalJavaScriptError/Hint": {
"title": "$:/language/InternalJavaScriptError/Hint",
"text": "Well, this is embarrassing. It is recommended that you restart TiddlyWiki by refreshing your browser"
},
"$:/language/InvalidFieldName": {
"title": "$:/language/InvalidFieldName",
"text": "Illegal characters in field name \"<$text text=<<fieldName>>/>\". Fields can only contain lowercase letters, digits and the characters underscore (`_`), hyphen (`-`) and period (`.`)"
},
"$:/language/LazyLoadingWarning": {
"title": "$:/language/LazyLoadingWarning",
"text": "<p>Loading external text from ''<$text text={{!!_canonical_uri}}/>''</p><p>If this message doesn't disappear you may be using a browser that doesn't support external text in this configuration. See http://tiddlywiki.com/#ExternalText</p>"
},
"$:/language/LoginToTiddlySpace": {
"title": "$:/language/LoginToTiddlySpace",
"text": "Login to TiddlySpace"
},
"$:/language/Manager/Controls/FilterByTag/None": {
"title": "$:/language/Manager/Controls/FilterByTag/None",
"text": "(none)"
},
"$:/language/Manager/Controls/FilterByTag/Prompt": {
"title": "$:/language/Manager/Controls/FilterByTag/Prompt",
"text": "Filter by tag:"
},
"$:/language/Manager/Controls/Order/Prompt": {
"title": "$:/language/Manager/Controls/Order/Prompt",
"text": "Reverse order"
},
"$:/language/Manager/Controls/Search/Placeholder": {
"title": "$:/language/Manager/Controls/Search/Placeholder",
"text": "Search"
},
"$:/language/Manager/Controls/Search/Prompt": {
"title": "$:/language/Manager/Controls/Search/Prompt",
"text": "Search:"
},
"$:/language/Manager/Controls/Show/Option/Tags": {
"title": "$:/language/Manager/Controls/Show/Option/Tags",
"text": "tags"
},
"$:/language/Manager/Controls/Show/Option/Tiddlers": {
"title": "$:/language/Manager/Controls/Show/Option/Tiddlers",
"text": "tiddlers"
},
"$:/language/Manager/Controls/Show/Prompt": {
"title": "$:/language/Manager/Controls/Show/Prompt",
"text": "Show:"
},
"$:/language/Manager/Controls/Sort/Prompt": {
"title": "$:/language/Manager/Controls/Sort/Prompt",
"text": "Sort by:"
},
"$:/language/Manager/Item/Colour": {
"title": "$:/language/Manager/Item/Colour",
"text": "Colour"
},
"$:/language/Manager/Item/Fields": {
"title": "$:/language/Manager/Item/Fields",
"text": "Fields"
},
"$:/language/Manager/Item/Icon/None": {
"title": "$:/language/Manager/Item/Icon/None",
"text": "(none)"
},
"$:/language/Manager/Item/Icon": {
"title": "$:/language/Manager/Item/Icon",
"text": "Icon"
},
"$:/language/Manager/Item/RawText": {
"title": "$:/language/Manager/Item/RawText",
"text": "Raw text"
},
"$:/language/Manager/Item/Tags": {
"title": "$:/language/Manager/Item/Tags",
"text": "Tags"
},
"$:/language/Manager/Item/Tools": {
"title": "$:/language/Manager/Item/Tools",
"text": "Tools"
},
"$:/language/Manager/Item/WikifiedText": {
"title": "$:/language/Manager/Item/WikifiedText",
"text": "Wikified text"
},
"$:/language/MissingTiddler/Hint": {
"title": "$:/language/MissingTiddler/Hint",
"text": "Missing tiddler \"<$text text=<<currentTiddler>>/>\" - click {{$:/core/images/edit-button}} to create"
},
"$:/language/No": {
"title": "$:/language/No",
"text": "No"
},
"$:/language/OfficialPluginLibrary": {
"title": "$:/language/OfficialPluginLibrary",
"text": "Official ~TiddlyWiki Plugin Library"
},
"$:/language/OfficialPluginLibrary/Hint": {
"title": "$:/language/OfficialPluginLibrary/Hint",
"text": "The official ~TiddlyWiki plugin library at tiddlywiki.com. Plugins, themes and language packs are maintained by the core team."
},
"$:/language/PluginReloadWarning": {
"title": "$:/language/PluginReloadWarning",
"text": "Please save {{$:/core/ui/Buttons/save-wiki}} and reload {{$:/core/ui/Buttons/refresh}} to allow changes to plugins to take effect"
},
"$:/language/RecentChanges/DateFormat": {
"title": "$:/language/RecentChanges/DateFormat",
"text": "DDth MMM YYYY"
},
"$:/language/SystemTiddler/Tooltip": {
"title": "$:/language/SystemTiddler/Tooltip",
"text": "This is a system tiddler"
},
"$:/language/SystemTiddlers/Include/Prompt": {
"title": "$:/language/SystemTiddlers/Include/Prompt",
"text": "Include system tiddlers"
},
"$:/language/TagManager/Colour/Heading": {
"title": "$:/language/TagManager/Colour/Heading",
"text": "Colour"
},
"$:/language/TagManager/Count/Heading": {
"title": "$:/language/TagManager/Count/Heading",
"text": "Count"
},
"$:/language/TagManager/Icon/Heading": {
"title": "$:/language/TagManager/Icon/Heading",
"text": "Icon"
},
"$:/language/TagManager/Info/Heading": {
"title": "$:/language/TagManager/Info/Heading",
"text": "Info"
},
"$:/language/TagManager/Tag/Heading": {
"title": "$:/language/TagManager/Tag/Heading",
"text": "Tag"
},
"$:/language/Tiddler/DateFormat": {
"title": "$:/language/Tiddler/DateFormat",
"text": "DDth MMM YYYY at hh12:0mmam"
},
"$:/language/UnsavedChangesWarning": {
"title": "$:/language/UnsavedChangesWarning",
"text": "You have unsaved changes in TiddlyWiki"
},
"$:/language/Yes": {
"title": "$:/language/Yes",
"text": "Yes"
},
"$:/language/Modals/Download": {
"title": "$:/language/Modals/Download",
"type": "text/vnd.tiddlywiki",
"subtitle": "Download changes",
"footer": "<$button message=\"tm-close-tiddler\">Close</$button>",
"help": "http://tiddlywiki.com/static/DownloadingChanges.html",
"text": "Your browser only supports manual saving.\n\nTo save your modified wiki, right click on the download link below and select \"Download file\" or \"Save file\", and then choose the folder and filename.\n\n//You can marginally speed things up by clicking the link with the control key (Windows) or the options/alt key (Mac OS X). You will not be prompted for the folder or filename, but your browser is likely to give it an unrecognisable name -- you may need to rename the file to include an `.html` extension before you can do anything useful with it.//\n\nOn smartphones that do not allow files to be downloaded you can instead bookmark the link, and then sync your bookmarks to a desktop computer from where the wiki can be saved normally.\n"
},
"$:/language/Modals/SaveInstructions": {
"title": "$:/language/Modals/SaveInstructions",
"type": "text/vnd.tiddlywiki",
"subtitle": "Save your work",
"footer": "<$button message=\"tm-close-tiddler\">Close</$button>",
"help": "http://tiddlywiki.com/static/SavingChanges.html",
"text": "Your changes to this wiki need to be saved as a ~TiddlyWiki HTML file.\n\n!!! Desktop browsers\n\n# Select ''Save As'' from the ''File'' menu\n# Choose a filename and location\n#* Some browsers also require you to explicitly specify the file saving format as ''Webpage, HTML only'' or similar\n# Close this tab\n\n!!! Smartphone browsers\n\n# Create a bookmark to this page\n#* If you've got iCloud or Google Sync set up then the bookmark will automatically sync to your desktop where you can open it and save it as above\n# Close this tab\n\n//If you open the bookmark again in Mobile Safari you will see this message again. If you want to go ahead and use the file, just click the ''close'' button below//\n"
},
"$:/config/NewJournal/Title": {
"title": "$:/config/NewJournal/Title",
"text": "DDth MMM YYYY"
},
"$:/config/NewJournal/Text": {
"title": "$:/config/NewJournal/Text",
"text": ""
},
"$:/config/NewJournal/Tags": {
"title": "$:/config/NewJournal/Tags",
"text": "Journal"
},
"$:/language/Notifications/Save/Done": {
"title": "$:/language/Notifications/Save/Done",
"text": "Saved wiki"
},
"$:/language/Notifications/Save/Starting": {
"title": "$:/language/Notifications/Save/Starting",
"text": "Starting to save wiki"
},
"$:/language/Search/DefaultResults/Caption": {
"title": "$:/language/Search/DefaultResults/Caption",
"text": "List"
},
"$:/language/Search/Filter/Caption": {
"title": "$:/language/Search/Filter/Caption",
"text": "Filter"
},
"$:/language/Search/Filter/Hint": {
"title": "$:/language/Search/Filter/Hint",
"text": "Search via a [[filter expression|http://tiddlywiki.com/static/Filters.html]]"
},
"$:/language/Search/Filter/Matches": {
"title": "$:/language/Search/Filter/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/Search/Matches": {
"title": "$:/language/Search/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/Search/Matches/All": {
"title": "$:/language/Search/Matches/All",
"text": "All matches:"
},
"$:/language/Search/Matches/Title": {
"title": "$:/language/Search/Matches/Title",
"text": "Title matches:"
},
"$:/language/Search/Search": {
"title": "$:/language/Search/Search",
"text": "Search"
},
"$:/language/Search/Search/TooShort": {
"title": "$:/language/Search/Search/TooShort",
"text": "Search text too short"
},
"$:/language/Search/Shadows/Caption": {
"title": "$:/language/Search/Shadows/Caption",
"text": "Shadows"
},
"$:/language/Search/Shadows/Hint": {
"title": "$:/language/Search/Shadows/Hint",
"text": "Search for shadow tiddlers"
},
"$:/language/Search/Shadows/Matches": {
"title": "$:/language/Search/Shadows/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/Search/Standard/Caption": {
"title": "$:/language/Search/Standard/Caption",
"text": "Standard"
},
"$:/language/Search/Standard/Hint": {
"title": "$:/language/Search/Standard/Hint",
"text": "Search for standard tiddlers"
},
"$:/language/Search/Standard/Matches": {
"title": "$:/language/Search/Standard/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/Search/System/Caption": {
"title": "$:/language/Search/System/Caption",
"text": "System"
},
"$:/language/Search/System/Hint": {
"title": "$:/language/Search/System/Hint",
"text": "Search for system tiddlers"
},
"$:/language/Search/System/Matches": {
"title": "$:/language/Search/System/Matches",
"text": "//<small><<resultCount>> matches</small>//"
},
"$:/language/SideBar/All/Caption": {
"title": "$:/language/SideBar/All/Caption",
"text": "All"
},
"$:/language/SideBar/Contents/Caption": {
"title": "$:/language/SideBar/Contents/Caption",
"text": "Contents"
},
"$:/language/SideBar/Drafts/Caption": {
"title": "$:/language/SideBar/Drafts/Caption",
"text": "Drafts"
},
"$:/language/SideBar/Missing/Caption": {
"title": "$:/language/SideBar/Missing/Caption",
"text": "Missing"
},
"$:/language/SideBar/More/Caption": {
"title": "$:/language/SideBar/More/Caption",
"text": "More"
},
"$:/language/SideBar/Open/Caption": {
"title": "$:/language/SideBar/Open/Caption",
"text": "Open"
},
"$:/language/SideBar/Orphans/Caption": {
"title": "$:/language/SideBar/Orphans/Caption",
"text": "Orphans"
},
"$:/language/SideBar/Recent/Caption": {
"title": "$:/language/SideBar/Recent/Caption",
"text": "Recent"
},
"$:/language/SideBar/Shadows/Caption": {
"title": "$:/language/SideBar/Shadows/Caption",
"text": "Shadows"
},
"$:/language/SideBar/System/Caption": {
"title": "$:/language/SideBar/System/Caption",
"text": "System"
},
"$:/language/SideBar/Tags/Caption": {
"title": "$:/language/SideBar/Tags/Caption",
"text": "Tags"
},
"$:/language/SideBar/Tags/Untagged/Caption": {
"title": "$:/language/SideBar/Tags/Untagged/Caption",
"text": "untagged"
},
"$:/language/SideBar/Tools/Caption": {
"title": "$:/language/SideBar/Tools/Caption",
"text": "Tools"
},
"$:/language/SideBar/Types/Caption": {
"title": "$:/language/SideBar/Types/Caption",
"text": "Types"
},
"$:/SiteSubtitle": {
"title": "$:/SiteSubtitle",
"text": "a non-linear personal web notebook"
},
"$:/SiteTitle": {
"title": "$:/SiteTitle",
"text": "My ~TiddlyWiki"
},
"$:/language/Snippets/ListByTag": {
"title": "$:/language/Snippets/ListByTag",
"tags": "$:/tags/TextEditor/Snippet",
"caption": "List of tiddlers by tag",
"text": "<<list-links \"[tag[task]sort[title]]\">>\n"
},
"$:/language/Snippets/MacroDefinition": {
"title": "$:/language/Snippets/MacroDefinition",
"tags": "$:/tags/TextEditor/Snippet",
"caption": "Macro definition",
"text": "\\define macroName(param1:\"default value\",param2)\nText of the macro\n\\end\n"
},
"$:/language/Snippets/Table4x3": {
"title": "$:/language/Snippets/Table4x3",
"tags": "$:/tags/TextEditor/Snippet",
"caption": "Table with 4 columns by 3 rows",
"text": "|! |!Alpha |!Beta |!Gamma |!Delta |\n|!One | | | | |\n|!Two | | | | |\n|!Three | | | | |\n"
},
"$:/language/Snippets/TableOfContents": {
"title": "$:/language/Snippets/TableOfContents",
"tags": "$:/tags/TextEditor/Snippet",
"caption": "Table of Contents",
"text": "<div class=\"tc-table-of-contents\">\n\n<<toc-selective-expandable 'TableOfContents'>>\n\n</div>"
},
"$:/language/ThemeTweaks/ThemeTweaks": {
"title": "$:/language/ThemeTweaks/ThemeTweaks",
"text": "Theme Tweaks"
},
"$:/language/ThemeTweaks/ThemeTweaks/Hint": {
"title": "$:/language/ThemeTweaks/ThemeTweaks/Hint",
"text": "You can tweak certain aspects of the ''Vanilla'' theme."
},
"$:/language/ThemeTweaks/Options": {
"title": "$:/language/ThemeTweaks/Options",
"text": "Options"
},
"$:/language/ThemeTweaks/Options/SidebarLayout": {
"title": "$:/language/ThemeTweaks/Options/SidebarLayout",
"text": "Sidebar layout"
},
"$:/language/ThemeTweaks/Options/SidebarLayout/Fixed-Fluid": {
"title": "$:/language/ThemeTweaks/Options/SidebarLayout/Fixed-Fluid",
"text": "Fixed story, fluid sidebar"
},
"$:/language/ThemeTweaks/Options/SidebarLayout/Fluid-Fixed": {
"title": "$:/language/ThemeTweaks/Options/SidebarLayout/Fluid-Fixed",
"text": "Fluid story, fixed sidebar"
},
"$:/language/ThemeTweaks/Options/StickyTitles": {
"title": "$:/language/ThemeTweaks/Options/StickyTitles",
"text": "Sticky titles"
},
"$:/language/ThemeTweaks/Options/StickyTitles/Hint": {
"title": "$:/language/ThemeTweaks/Options/StickyTitles/Hint",
"text": "Causes tiddler titles to \"stick\" to the top of the browser window. Caution: Does not work at all with Chrome, and causes some layout issues in Firefox"
},
"$:/language/ThemeTweaks/Options/CodeWrapping": {
"title": "$:/language/ThemeTweaks/Options/CodeWrapping",
"text": "Wrap long lines in code blocks"
},
"$:/language/ThemeTweaks/Settings": {
"title": "$:/language/ThemeTweaks/Settings",
"text": "Settings"
},
"$:/language/ThemeTweaks/Settings/FontFamily": {
"title": "$:/language/ThemeTweaks/Settings/FontFamily",
"text": "Font family"
},
"$:/language/ThemeTweaks/Settings/CodeFontFamily": {
"title": "$:/language/ThemeTweaks/Settings/CodeFontFamily",
"text": "Code font family"
},
"$:/language/ThemeTweaks/Settings/BackgroundImage": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImage",
"text": "Page background image"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageAttachment": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageAttachment",
"text": "Page background image attachment"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageAttachment/Scroll": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageAttachment/Scroll",
"text": "Scroll with tiddlers"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageAttachment/Fixed": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageAttachment/Fixed",
"text": "Fixed to window"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageSize": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageSize",
"text": "Page background image size"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageSize/Auto": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageSize/Auto",
"text": "Auto"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageSize/Cover": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageSize/Cover",
"text": "Cover"
},
"$:/language/ThemeTweaks/Settings/BackgroundImageSize/Contain": {
"title": "$:/language/ThemeTweaks/Settings/BackgroundImageSize/Contain",
"text": "Contain"
},
"$:/language/ThemeTweaks/Metrics": {
"title": "$:/language/ThemeTweaks/Metrics",
"text": "Sizes"
},
"$:/language/ThemeTweaks/Metrics/FontSize": {
"title": "$:/language/ThemeTweaks/Metrics/FontSize",
"text": "Font size"
},
"$:/language/ThemeTweaks/Metrics/LineHeight": {
"title": "$:/language/ThemeTweaks/Metrics/LineHeight",
"text": "Line height"
},
"$:/language/ThemeTweaks/Metrics/BodyFontSize": {
"title": "$:/language/ThemeTweaks/Metrics/BodyFontSize",
"text": "Font size for tiddler body"
},
"$:/language/ThemeTweaks/Metrics/BodyLineHeight": {
"title": "$:/language/ThemeTweaks/Metrics/BodyLineHeight",
"text": "Line height for tiddler body"
},
"$:/language/ThemeTweaks/Metrics/StoryLeft": {
"title": "$:/language/ThemeTweaks/Metrics/StoryLeft",
"text": "Story left position"
},
"$:/language/ThemeTweaks/Metrics/StoryLeft/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/StoryLeft/Hint",
"text": "how far the left margin of the story river<br>(tiddler area) is from the left of the page"
},
"$:/language/ThemeTweaks/Metrics/StoryTop": {
"title": "$:/language/ThemeTweaks/Metrics/StoryTop",
"text": "Story top position"
},
"$:/language/ThemeTweaks/Metrics/StoryTop/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/StoryTop/Hint",
"text": "how far the top margin of the story river<br>is from the top of the page"
},
"$:/language/ThemeTweaks/Metrics/StoryRight": {
"title": "$:/language/ThemeTweaks/Metrics/StoryRight",
"text": "Story right"
},
"$:/language/ThemeTweaks/Metrics/StoryRight/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/StoryRight/Hint",
"text": "how far the left margin of the sidebar <br>is from the left of the page"
},
"$:/language/ThemeTweaks/Metrics/StoryWidth": {
"title": "$:/language/ThemeTweaks/Metrics/StoryWidth",
"text": "Story width"
},
"$:/language/ThemeTweaks/Metrics/StoryWidth/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/StoryWidth/Hint",
"text": "the overall width of the story river"
},
"$:/language/ThemeTweaks/Metrics/TiddlerWidth": {
"title": "$:/language/ThemeTweaks/Metrics/TiddlerWidth",
"text": "Tiddler width"
},
"$:/language/ThemeTweaks/Metrics/TiddlerWidth/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/TiddlerWidth/Hint",
"text": "within the story river"
},
"$:/language/ThemeTweaks/Metrics/SidebarBreakpoint": {
"title": "$:/language/ThemeTweaks/Metrics/SidebarBreakpoint",
"text": "Sidebar breakpoint"
},
"$:/language/ThemeTweaks/Metrics/SidebarBreakpoint/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/SidebarBreakpoint/Hint",
"text": "the minimum page width at which the story<br>river and sidebar will appear side by side"
},
"$:/language/ThemeTweaks/Metrics/SidebarWidth": {
"title": "$:/language/ThemeTweaks/Metrics/SidebarWidth",
"text": "Sidebar width"
},
"$:/language/ThemeTweaks/Metrics/SidebarWidth/Hint": {
"title": "$:/language/ThemeTweaks/Metrics/SidebarWidth/Hint",
"text": "the width of the sidebar in fluid-fixed layout"
},
"$:/language/TiddlerInfo/Advanced/Caption": {
"title": "$:/language/TiddlerInfo/Advanced/Caption",
"text": "Advanced"
},
"$:/language/TiddlerInfo/Advanced/PluginInfo/Empty/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/PluginInfo/Empty/Hint",
"text": "none"
},
"$:/language/TiddlerInfo/Advanced/PluginInfo/Heading": {
"title": "$:/language/TiddlerInfo/Advanced/PluginInfo/Heading",
"text": "Plugin Details"
},
"$:/language/TiddlerInfo/Advanced/PluginInfo/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/PluginInfo/Hint",
"text": "This plugin contains the following shadow tiddlers:"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/Heading": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/Heading",
"text": "Shadow Status"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/NotShadow/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/NotShadow/Hint",
"text": "The tiddler <$link to=<<infoTiddler>>><$text text=<<infoTiddler>>/></$link> is not a shadow tiddler"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/Shadow/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/Shadow/Hint",
"text": "The tiddler <$link to=<<infoTiddler>>><$text text=<<infoTiddler>>/></$link> is a shadow tiddler"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/Shadow/Source": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/Shadow/Source",
"text": "It is defined in the plugin <$link to=<<pluginTiddler>>><$text text=<<pluginTiddler>>/></$link>"
},
"$:/language/TiddlerInfo/Advanced/ShadowInfo/OverriddenShadow/Hint": {
"title": "$:/language/TiddlerInfo/Advanced/ShadowInfo/OverriddenShadow/Hint",
"text": "It is overridden by an ordinary tiddler"
},
"$:/language/TiddlerInfo/Fields/Caption": {
"title": "$:/language/TiddlerInfo/Fields/Caption",
"text": "Fields"
},
"$:/language/TiddlerInfo/List/Caption": {
"title": "$:/language/TiddlerInfo/List/Caption",
"text": "List"
},
"$:/language/TiddlerInfo/List/Empty": {
"title": "$:/language/TiddlerInfo/List/Empty",
"text": "This tiddler does not have a list"
},
"$:/language/TiddlerInfo/Listed/Caption": {
"title": "$:/language/TiddlerInfo/Listed/Caption",
"text": "Listed"
},
"$:/language/TiddlerInfo/Listed/Empty": {
"title": "$:/language/TiddlerInfo/Listed/Empty",
"text": "This tiddler is not listed by any others"
},
"$:/language/TiddlerInfo/References/Caption": {
"title": "$:/language/TiddlerInfo/References/Caption",
"text": "References"
},
"$:/language/TiddlerInfo/References/Empty": {
"title": "$:/language/TiddlerInfo/References/Empty",
"text": "No tiddlers link to this one"
},
"$:/language/TiddlerInfo/Tagging/Caption": {
"title": "$:/language/TiddlerInfo/Tagging/Caption",
"text": "Tagging"
},
"$:/language/TiddlerInfo/Tagging/Empty": {
"title": "$:/language/TiddlerInfo/Tagging/Empty",
"text": "No tiddlers are tagged with this one"
},
"$:/language/TiddlerInfo/Tools/Caption": {
"title": "$:/language/TiddlerInfo/Tools/Caption",
"text": "Tools"
},
"$:/language/Docs/Types/application/javascript": {
"title": "$:/language/Docs/Types/application/javascript",
"description": "JavaScript code",
"name": "application/javascript",
"group": "Developer",
"group-sort": "2"
},
"$:/language/Docs/Types/application/json": {
"title": "$:/language/Docs/Types/application/json",
"description": "JSON data",
"name": "application/json",
"group": "Developer",
"group-sort": "2"
},
"$:/language/Docs/Types/application/x-tiddler-dictionary": {
"title": "$:/language/Docs/Types/application/x-tiddler-dictionary",
"description": "Data dictionary",
"name": "application/x-tiddler-dictionary",
"group": "Developer",
"group-sort": "2"
},
"$:/language/Docs/Types/image/gif": {
"title": "$:/language/Docs/Types/image/gif",
"description": "GIF image",
"name": "image/gif",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/image/jpeg": {
"title": "$:/language/Docs/Types/image/jpeg",
"description": "JPEG image",
"name": "image/jpeg",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/image/png": {
"title": "$:/language/Docs/Types/image/png",
"description": "PNG image",
"name": "image/png",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/image/svg+xml": {
"title": "$:/language/Docs/Types/image/svg+xml",
"description": "Structured Vector Graphics image",
"name": "image/svg+xml",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/image/x-icon": {
"title": "$:/language/Docs/Types/image/x-icon",
"description": "ICO format icon file",
"name": "image/x-icon",
"group": "Image",
"group-sort": "1"
},
"$:/language/Docs/Types/text/css": {
"title": "$:/language/Docs/Types/text/css",
"description": "Static stylesheet",
"name": "text/css",
"group": "Developer",
"group-sort": "2"
},
"$:/language/Docs/Types/text/html": {
"title": "$:/language/Docs/Types/text/html",
"description": "HTML markup",
"name": "text/html",
"group": "Text",
"group-sort": "0"
},
"$:/language/Docs/Types/text/plain": {
"title": "$:/language/Docs/Types/text/plain",
"description": "Plain text",
"name": "text/plain",
"group": "Text",
"group-sort": "0"
},
"$:/language/Docs/Types/text/vnd.tiddlywiki": {
"title": "$:/language/Docs/Types/text/vnd.tiddlywiki",
"description": "TiddlyWiki 5",
"name": "text/vnd.tiddlywiki",
"group": "Text",
"group-sort": "0"
},
"$:/language/Docs/Types/text/x-tiddlywiki": {
"title": "$:/language/Docs/Types/text/x-tiddlywiki",
"description": "TiddlyWiki Classic",
"name": "text/x-tiddlywiki",
"group": "Text",
"group-sort": "0"
},
"$:/languages/en-GB/icon": {
"title": "$:/languages/en-GB/icon",
"type": "image/svg+xml",
"text": "<svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 60 30\" width=\"1200\" height=\"600\">\n<clipPath id=\"t\">\n\t<path d=\"M30,15 h30 v15 z v15 h-30 z h-30 v-15 z v-15 h30 z\"/>\n</clipPath>\n<path d=\"M0,0 v30 h60 v-30 z\" fill=\"#00247d\"/>\n<path d=\"M0,0 L60,30 M60,0 L0,30\" stroke=\"#fff\" stroke-width=\"6\"/>\n<path d=\"M0,0 L60,30 M60,0 L0,30\" clip-path=\"url(#t)\" stroke=\"#cf142b\" stroke-width=\"4\"/>\n<path d=\"M30,0 v30 M0,15 h60\" stroke=\"#fff\" stroke-width=\"10\"/>\n<path d=\"M30,0 v30 M0,15 h60\" stroke=\"#cf142b\" stroke-width=\"6\"/>\n</svg>\n"
},
"$:/languages/en-GB": {
"title": "$:/languages/en-GB",
"name": "en-GB",
"description": "English (British)",
"author": "JeremyRuston",
"core-version": ">=5.0.0\"",
"text": "Stub pseudo-plugin for the default language"
},
"$:/core/modules/commander.js": {
"text": "/*\\\ntitle: $:/core/modules/commander.js\ntype: application/javascript\nmodule-type: global\n\nThe $tw.Commander class is a command interpreter\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nParse a sequence of commands\n\tcommandTokens: an array of command string tokens\n\twiki: reference to the wiki store object\n\tstreams: {output:, error:}, each of which has a write(string) method\n\tcallback: a callback invoked as callback(err) where err is null if there was no error\n*/\nvar Commander = function(commandTokens,callback,wiki,streams) {\n\tvar path = require(\"path\");\n\tthis.commandTokens = commandTokens;\n\tthis.nextToken = 0;\n\tthis.callback = callback;\n\tthis.wiki = wiki;\n\tthis.streams = streams;\n\tthis.outputPath = path.resolve($tw.boot.wikiPath,$tw.config.wikiOutputSubDir);\n};\n\n/*\nLog a string if verbose flag is set\n*/\nCommander.prototype.log = function(str) {\n\tif(this.verbose) {\n\t\tthis.streams.output.write(str + \"\\n\");\n\t}\n};\n\n/*\nWrite a string if verbose flag is set\n*/\nCommander.prototype.write = function(str) {\n\tif(this.verbose) {\n\t\tthis.streams.output.write(str);\n\t}\n};\n\n/*\nAdd a string of tokens to the command queue\n*/\nCommander.prototype.addCommandTokens = function(commandTokens) {\n\tvar params = commandTokens.slice(0);\n\tparams.unshift(0);\n\tparams.unshift(this.nextToken);\n\tArray.prototype.splice.apply(this.commandTokens,params);\n};\n\n/*\nExecute the sequence of commands and invoke a callback on completion\n*/\nCommander.prototype.execute = function() {\n\tthis.executeNextCommand();\n};\n\n/*\nExecute the next command in the sequence\n*/\nCommander.prototype.executeNextCommand = function() {\n\tvar self = this;\n\t// Invoke the callback if there are no more commands\n\tif(this.nextToken >= this.commandTokens.length) {\n\t\tthis.callback(null);\n\t} else {\n\t\t// Get and check the command token\n\t\tvar commandName = this.commandTokens[this.nextToken++];\n\t\tif(commandName.substr(0,2) !== \"--\") {\n\t\t\tthis.callback(\"Missing command: \" + commandName);\n\t\t} else {\n\t\t\tcommandName = commandName.substr(2); // Trim off the --\n\t\t\t// Accumulate the parameters to the command\n\t\t\tvar params = [];\n\t\t\twhile(this.nextToken < this.commandTokens.length && \n\t\t\t\tthis.commandTokens[this.nextToken].substr(0,2) !== \"--\") {\n\t\t\t\tparams.push(this.commandTokens[this.nextToken++]);\n\t\t\t}\n\t\t\t// Get the command info\n\t\t\tvar command = $tw.commands[commandName],\n\t\t\t\tc,err;\n\t\t\tif(!command) {\n\t\t\t\tthis.callback(\"Unknown command: \" + commandName);\n\t\t\t} else {\n\t\t\t\tif(this.verbose) {\n\t\t\t\t\tthis.streams.output.write(\"Executing command: \" + commandName + \" \" + params.join(\" \") + \"\\n\");\n\t\t\t\t}\n\t\t\t\tif(command.info.synchronous) {\n\t\t\t\t\t// Synchronous command\n\t\t\t\t\tc = new command.Command(params,this);\n\t\t\t\t\terr = c.execute();\n\t\t\t\t\tif(err) {\n\t\t\t\t\t\tthis.callback(err);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tthis.executeNextCommand();\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\t// Asynchronous command\n\t\t\t\t\tc = new command.Command(params,this,function(err) {\n\t\t\t\t\t\tif(err) {\n\t\t\t\t\t\t\tself.callback(err);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tself.executeNextCommand();\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t\terr = c.execute();\n\t\t\t\t\tif(err) {\n\t\t\t\t\t\tthis.callback(err);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n};\n\nCommander.initCommands = function(moduleType) {\n\tmoduleType = moduleType || \"command\";\n\t$tw.commands = {};\n\t$tw.modules.forEachModuleOfType(moduleType,function(title,module) {\n\t\tvar c = $tw.commands[module.info.name] = {};\n\t\t// Add the methods defined by the module\n\t\tfor(var f in module) {\n\t\t\tif($tw.utils.hop(module,f)) {\n\t\t\t\tc[f] = module[f];\n\t\t\t}\n\t\t}\n\t});\n};\n\nexports.Commander = Commander;\n\n})();\n",
"title": "$:/core/modules/commander.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/commands/build.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/build.js\ntype: application/javascript\nmodule-type: command\n\nCommand to build a build target\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"build\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\t// Get the build targets defined in the wiki\n\tvar buildTargets = $tw.boot.wikiInfo.build;\n\tif(!buildTargets) {\n\t\treturn \"No build targets defined\";\n\t}\n\t// Loop through each of the specified targets\n\tvar targets;\n\tif(this.params.length > 0) {\n\t\ttargets = this.params;\n\t} else {\n\t\ttargets = Object.keys(buildTargets);\n\t}\n\tfor(var targetIndex=0; targetIndex<targets.length; targetIndex++) {\n\t\tvar target = targets[targetIndex],\n\t\t\tcommands = buildTargets[target];\n\t\tif(!commands) {\n\t\t\treturn \"Build target '\" + target + \"' not found\";\n\t\t}\n\t\t// Add the commands to the queue\n\t\tthis.commander.addCommandTokens(commands);\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/build.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/clearpassword.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/clearpassword.js\ntype: application/javascript\nmodule-type: command\n\nClear password for crypto operations\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"clearpassword\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\t$tw.crypto.setPassword(null);\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/clearpassword.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/editions.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/editions.js\ntype: application/javascript\nmodule-type: command\n\nCommand to list the available editions\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"editions\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tvar self = this;\n\t// Output the list\n\tthis.commander.streams.output.write(\"Available editions:\\n\\n\");\n\tvar editionInfo = $tw.utils.getEditionInfo();\n\t$tw.utils.each(editionInfo,function(info,name) {\n\t\tself.commander.streams.output.write(\" \" + name + \": \" + info.description + \"\\n\");\n\t});\n\tthis.commander.streams.output.write(\"\\n\");\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/editions.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/fetch.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/fetch.js\ntype: application/javascript\nmodule-type: command\n\nCommands to fetch external tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"fetch\",\n\tsynchronous: false\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing subcommand and url\";\n\t}\n\tvar subcommand = this.params[0],\n\t\turl = this.params[1],\n\t\timportFilter = this.params[2] || \"[all[tiddlers]]\",\n\t\ttransformFilter = this.params[3] || \"\";\n\tswitch(subcommand) {\n\t\tcase \"file\":\n\t\t\treturn this.fetchFiles({\n\t\t\t\turl: url,\n\t\t\t\timportFilter: importFilter,\n\t\t\t\ttransformFilter: transformFilter,\n\t\t\t\tcallback: this.callback\n\t\t\t});\n\t\t\tbreak;\n\t\tcase \"files\":\n\t\t\treturn this.fetchFiles({\n\t\t\t\turlFilter: url,\n\t\t\t\timportFilter: importFilter,\n\t\t\t\ttransformFilter: transformFilter,\n\t\t\t\tcallback: this.callback\n\t\t\t});\n\t\t\tbreak;\n\t}\n\treturn null;\n};\n\nCommand.prototype.fetchFiles = function(options) {\n\tvar self = this;\n\t// Get the list of URLs\n\tvar urls;\n\tif(options.url) {\n\t\turls = [options.url]\n\t} else if(options.urlFilter) {\n\t\turls = $tw.wiki.filterTiddlers(options.urlFilter);\n\t} else {\n\t\treturn \"Missing URL\";\n\t}\n\t// Process each URL in turn\n\tvar next = 0;\n\tvar getNextFile = function(err) {\n\t\tif(err) {\n\t\t\treturn options.callback(err);\n\t\t}\n\t\tif(next < urls.length) {\n\t\t\tself.fetchFile(urls[next++],options,getNextFile);\n\t\t} else {\n\t\t\toptions.callback(null);\n\t\t}\n\t};\n\tgetNextFile(null);\n\t// Success\n\treturn null;\n};\n\nCommand.prototype.fetchFile = function(url,options,callback) {\n\tvar self = this,\n\t\tlib = url.substr(0,8) === \"https://\" ? require(\"https\") : require(\"http\");\n\tlib.get(url).on(\"response\",function(response) {\n\t var type = (response.headers[\"content-type\"] || \"\").split(\";\")[0],\n\t \tbody = \"\";\n\t self.commander.write(\"Reading \" + url + \": \");\n\t response.on(\"data\",function(chunk) {\n\t body += chunk;\n\t self.commander.write(\".\");\n\t });\n\t response.on(\"end\",function() {\n\t self.commander.write(\"\\n\");\n\t if(response.statusCode === 200) {\n\t\t self.processBody(body,type,options);\n\t\t callback(null);\n\t } else {\n\t \tcallback(\"Error \" + response.statusCode + \" retrieving \" + url)\n\t }\n\t \t});\n\t \tresponse.on(\"error\",function(e) {\n\t\t\tconsole.log(\"Error on GET request: \" + e);\n\t\t\tcallback(e);\n\t \t});\n\t});\n\treturn null;\n};\n\nCommand.prototype.processBody = function(body,type,options) {\n\t// Deserialise the HTML file and put the tiddlers in their own wiki\n\tvar self = this,\n\t\tincomingWiki = new $tw.Wiki(),\n\t\ttiddlers = this.commander.wiki.deserializeTiddlers(type || \"text/html\",body,{});\n\t$tw.utils.each(tiddlers,function(tiddler) {\n\t\tincomingWiki.addTiddler(new $tw.Tiddler(tiddler));\n\t});\n\t// Filter the tiddlers to select the ones we want\n\tvar filteredTitles = incomingWiki.filterTiddlers(options.importFilter);\n\t// Import the selected tiddlers\n\tvar count = 0;\n\tincomingWiki.each(function(tiddler,title) {\n\t\tif(filteredTitles.indexOf(title) !== -1) {\n\t\t\tvar newTiddler;\n\t\t\tif(options.transformFilter) {\n\t\t\t\tvar transformedTitle = (incomingWiki.filterTiddlers(options.transformFilter,null,self.commander.wiki.makeTiddlerIterator([title])) || [\"\"])[0];\n\t\t\t\tif(transformedTitle) {\n\t\t\t\t\tself.commander.log(\"Importing \" + title + \" as \" + transformedTitle)\n\t\t\t\t\tnewTiddler = new $tw.Tiddler(tiddler,{title: transformedTitle});\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tself.commander.log(\"Importing \" + title)\n\t\t\t\tnewTiddler = tiddler;\n\t\t\t}\n\t\t\tself.commander.wiki.importTiddler(newTiddler);\n\t\t\tcount++;\n\t\t}\n\t});\n\tself.commander.log(\"Imported \" + count + \" tiddlers\")\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/fetch.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/help.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/help.js\ntype: application/javascript\nmodule-type: command\n\nHelp command\n\n\\*/\n(function(){\n\n/*jshint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"help\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tvar subhelp = this.params[0] || \"default\",\n\t\thelpBase = \"$:/language/Help/\",\n\t\ttext;\n\tif(!this.commander.wiki.getTiddler(helpBase + subhelp)) {\n\t\tsubhelp = \"notfound\";\n\t}\n\t// Wikify the help as formatted text (ie block elements generate newlines)\n\ttext = this.commander.wiki.renderTiddler(\"text/plain-formatted\",helpBase + subhelp);\n\t// Remove any leading linebreaks\n\ttext = text.replace(/^(\\r?\\n)*/g,\"\");\n\tthis.commander.streams.output.write(text);\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/help.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/init.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/init.js\ntype: application/javascript\nmodule-type: command\n\nCommand to initialise an empty wiki folder\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"init\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tvar fs = require(\"fs\"),\n\t\tpath = require(\"path\");\n\t// Check that we don't already have a valid wiki folder\n\tif($tw.boot.wikiTiddlersPath || ($tw.utils.isDirectory($tw.boot.wikiPath) && !$tw.utils.isDirectoryEmpty($tw.boot.wikiPath))) {\n\t\treturn \"Wiki folder is not empty\";\n\t}\n\t// Loop through each of the specified editions\n\tvar editions = this.params.length > 0 ? this.params : [\"empty\"];\n\tfor(var editionIndex=0; editionIndex<editions.length; editionIndex++) {\n\t\tvar editionName = editions[editionIndex];\n\t\t// Check the edition exists\n\t\tvar editionPath = $tw.findLibraryItem(editionName,$tw.getLibraryItemSearchPaths($tw.config.editionsPath,$tw.config.editionsEnvVar));\n\t\tif(!$tw.utils.isDirectory(editionPath)) {\n\t\t\treturn \"Edition '\" + editionName + \"' not found\";\n\t\t}\n\t\t// Copy the edition content\n\t\tvar err = $tw.utils.copyDirectory(editionPath,$tw.boot.wikiPath);\n\t\tif(!err) {\n\t\t\tthis.commander.streams.output.write(\"Copied edition '\" + editionName + \"' to \" + $tw.boot.wikiPath + \"\\n\");\n\t\t} else {\n\t\t\treturn err;\n\t\t}\n\t}\n\t// Tweak the tiddlywiki.info to remove any included wikis\n\tvar packagePath = $tw.boot.wikiPath + \"/tiddlywiki.info\",\n\t\tpackageJson = JSON.parse(fs.readFileSync(packagePath));\n\tdelete packageJson.includeWikis;\n\tfs.writeFileSync(packagePath,JSON.stringify(packageJson,null,$tw.config.preferences.jsonSpaces));\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/init.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/load.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/load.js\ntype: application/javascript\nmodule-type: command\n\nCommand to load tiddlers from a file\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"load\",\n\tsynchronous: false\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\");\n\tif(this.params.length < 1) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar ext = path.extname(self.params[0]),\n\t\tstat = fs.statSync(self.params[0]),\n\t\ttiddlers = $tw.loadTiddlersFromPath(self.params[0]),\n\t\tcount = 0;\n\t$tw.utils.each(tiddlers,function(tiddlerInfo) {\n\t\t$tw.utils.each(tiddlerInfo.tiddlers,function(tiddler) {\n\t\t\tself.commander.wiki.importTiddler(new $tw.Tiddler(tiddler));\n\t\t\tcount++;\n\t\t});\n\t});\n\tif(!count) {\n\t\tself.callback(\"No tiddlers found in file \\\"\" + self.params[0] + \"\\\"\");\n\t} else {\n\t\tself.callback(null);\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/load.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/makelibrary.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/makelibrary.js\ntype: application/javascript\nmodule-type: command\n\nCommand to pack all of the plugins in the library into a plugin tiddler of type \"library\"\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"makelibrary\",\n\tsynchronous: true\n};\n\nvar UPGRADE_LIBRARY_TITLE = \"$:/UpgradeLibrary\";\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tvar wiki = this.commander.wiki,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\tupgradeLibraryTitle = this.params[0] || UPGRADE_LIBRARY_TITLE,\n\t\ttiddlers = {};\n\t// Collect up the library plugins\n\tvar collectPlugins = function(folder) {\n\t\t\tvar pluginFolders = fs.readdirSync(folder);\n\t\t\tfor(var p=0; p<pluginFolders.length; p++) {\n\t\t\t\tif(!$tw.boot.excludeRegExp.test(pluginFolders[p])) {\n\t\t\t\t\tpluginFields = $tw.loadPluginFolder(path.resolve(folder,\"./\" + pluginFolders[p]));\n\t\t\t\t\tif(pluginFields && pluginFields.title) {\n\t\t\t\t\t\ttiddlers[pluginFields.title] = pluginFields;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t},\n\t\tcollectPublisherPlugins = function(folder) {\n\t\t\tvar publisherFolders = fs.readdirSync(folder);\n\t\t\tfor(var t=0; t<publisherFolders.length; t++) {\n\t\t\t\tif(!$tw.boot.excludeRegExp.test(publisherFolders[t])) {\n\t\t\t\t\tcollectPlugins(path.resolve(folder,\"./\" + publisherFolders[t]));\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\tcollectPublisherPlugins(path.resolve($tw.boot.corePath,$tw.config.pluginsPath));\n\tcollectPublisherPlugins(path.resolve($tw.boot.corePath,$tw.config.themesPath));\n\tcollectPlugins(path.resolve($tw.boot.corePath,$tw.config.languagesPath));\n\t// Save the upgrade library tiddler\n\tvar pluginFields = {\n\t\ttitle: upgradeLibraryTitle,\n\t\ttype: \"application/json\",\n\t\t\"plugin-type\": \"library\",\n\t\t\"text\": JSON.stringify({tiddlers: tiddlers},null,$tw.config.preferences.jsonSpaces)\n\t};\n\twiki.addTiddler(new $tw.Tiddler(pluginFields));\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/makelibrary.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/output.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/output.js\ntype: application/javascript\nmodule-type: command\n\nCommand to set the default output location (defaults to current working directory)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"output\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tvar fs = require(\"fs\"),\n\t\tpath = require(\"path\");\n\tif(this.params.length < 1) {\n\t\treturn \"Missing output path\";\n\t}\n\tthis.commander.outputPath = path.resolve(process.cwd(),this.params[0]);\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/output.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/password.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/password.js\ntype: application/javascript\nmodule-type: command\n\nSave password for crypto operations\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"password\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing password\";\n\t}\n\t$tw.crypto.setPassword(this.params[0]);\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/password.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/rendertiddler.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/rendertiddler.js\ntype: application/javascript\nmodule-type: command\n\nCommand to render a tiddler and save it to a file\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"rendertiddler\",\n\tsynchronous: false\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\ttitle = this.params[0],\n\t\tfilename = path.resolve(this.commander.outputPath,this.params[1]),\n\t\ttype = this.params[2] || \"text/html\",\n\t\ttemplate = this.params[3],\n\t\tname = this.params[4],\n\t\tvalue = this.params[5],\n\t\tvariables = {};\n\t$tw.utils.createFileDirectories(filename);\n\tif(template) {\n\t\tvariables.currentTiddler = title;\n\t\ttitle = template;\n\t}\n\tif(name && value) {\n\t\tvariables[name] = value;\n\t}\n\tfs.writeFile(filename,this.commander.wiki.renderTiddler(type,title,{variables: variables}),\"utf8\",function(err) {\n\t\tself.callback(err);\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/rendertiddler.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/rendertiddlers.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/rendertiddlers.js\ntype: application/javascript\nmodule-type: command\n\nCommand to render several tiddlers to a folder of files\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.info = {\n\tname: \"rendertiddlers\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\twiki = this.commander.wiki,\n\t\tfilter = this.params[0],\n\t\ttemplate = this.params[1],\n\t\toutputPath = this.commander.outputPath,\n\t\tpathname = path.resolve(outputPath,this.params[2]),\t\t\n\t\ttype = this.params[3] || \"text/html\",\n\t\textension = this.params[4] || \".html\",\n\t\tdeleteDirectory = (this.params[5] || \"\").toLowerCase() !== \"noclean\",\n\t\ttiddlers = wiki.filterTiddlers(filter);\n\tif(deleteDirectory) {\n\t\t$tw.utils.deleteDirectory(pathname);\n\t}\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar parser = wiki.parseTiddler(template),\n\t\t\twidgetNode = wiki.makeWidget(parser,{variables: {currentTiddler: title}}),\n\t\t\tcontainer = $tw.fakeDocument.createElement(\"div\");\n\t\twidgetNode.render(container,null);\n\t\tvar text = type === \"text/html\" ? container.innerHTML : container.textContent,\n\t\t\texportPath = null;\n\t\tif($tw.utils.hop($tw.macros,\"tv-get-export-path\")) {\n\t\t\tvar macroPath = $tw.macros[\"tv-get-export-path\"].run.apply(self,[title]);\n\t\t\tif(macroPath) {\n\t\t\t\texportPath = path.resolve(outputPath,macroPath + extension);\n\t\t\t}\n\t\t}\n\t\tvar finalPath = exportPath || path.resolve(pathname,encodeURIComponent(title) + extension);\n\t\t$tw.utils.createFileDirectories(finalPath);\n\t\tfs.writeFileSync(finalPath,text,\"utf8\");\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/rendertiddlers.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/savelibrarytiddlers.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/savelibrarytiddlers.js\ntype: application/javascript\nmodule-type: command\n\nCommand to save the subtiddlers of a bundle tiddler as a series of JSON files\n\n--savelibrarytiddlers <tiddler> <pathname> <skinnylisting>\n\nThe tiddler identifies the bundle tiddler that contains the subtiddlers.\n\nThe pathname specifies the pathname to the folder in which the JSON files should be saved. The filename is the URL encoded title of the subtiddler.\n\nThe skinnylisting specifies the title of the tiddler to which a JSON catalogue of the subtiddlers will be saved. The JSON file contains the same data as the bundle tiddler but with the `text` field removed.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"savelibrarytiddlers\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\tcontainerTitle = this.params[0],\n\t\tfilter = this.params[1],\n\t\tbasepath = this.params[2],\n\t\tskinnyListTitle = this.params[3];\n\t// Get the container tiddler as data\n\tvar containerData = self.commander.wiki.getTiddlerDataCached(containerTitle,undefined);\n\tif(!containerData) {\n\t\treturn \"'\" + containerTitle + \"' is not a tiddler bundle\";\n\t}\n\t// Filter the list of plugins\n\tvar pluginList = [];\n\t$tw.utils.each(containerData.tiddlers,function(tiddler,title) {\n\t\tpluginList.push(title);\n\t});\n\tvar filteredPluginList;\n\tif(filter) {\n\t\tfilteredPluginList = self.commander.wiki.filterTiddlers(filter,null,self.commander.wiki.makeTiddlerIterator(pluginList));\n\t} else {\n\t\tfilteredPluginList = pluginList;\n\t}\n\t// Iterate through the plugins\n\tvar skinnyList = [];\n\t$tw.utils.each(filteredPluginList,function(title) {\n\t\tvar tiddler = containerData.tiddlers[title];\n\t\t// Save each JSON file and collect the skinny data\n\t\tvar pathname = path.resolve(self.commander.outputPath,basepath + encodeURIComponent(title) + \".json\");\n\t\t$tw.utils.createFileDirectories(pathname);\n\t\tfs.writeFileSync(pathname,JSON.stringify(tiddler,null,$tw.config.preferences.jsonSpaces),\"utf8\");\n\t\t// Collect the skinny list data\n\t\tvar pluginTiddlers = JSON.parse(tiddler.text),\n\t\t\treadmeContent = (pluginTiddlers.tiddlers[title + \"/readme\"] || {}).text,\n\t\t\ticonTiddler = pluginTiddlers.tiddlers[title + \"/icon\"] || {},\n\t\t\ticonType = iconTiddler.type,\n\t\t\ticonText = iconTiddler.text,\n\t\t\ticonContent;\n\t\tif(iconType && iconText) {\n\t\t\ticonContent = $tw.utils.makeDataUri(iconText,iconType);\n\t\t}\n\t\tskinnyList.push($tw.utils.extend({},tiddler,{text: undefined, readme: readmeContent, icon: iconContent}));\n\t});\n\t// Save the catalogue tiddler\n\tif(skinnyListTitle) {\n\t\tself.commander.wiki.setTiddlerData(skinnyListTitle,skinnyList);\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/savelibrarytiddlers.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/savetiddler.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/savetiddler.js\ntype: application/javascript\nmodule-type: command\n\nCommand to save the content of a tiddler to a file\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"savetiddler\",\n\tsynchronous: false\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 2) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\ttitle = this.params[0],\n\t\tfilename = path.resolve(this.commander.outputPath,this.params[1]),\n\t\ttiddler = this.commander.wiki.getTiddler(title);\n\tif(tiddler) {\n\t\tvar type = tiddler.fields.type || \"text/vnd.tiddlywiki\",\n\t\t\tcontentTypeInfo = $tw.config.contentTypeInfo[type] || {encoding: \"utf8\"};\n\t\t$tw.utils.createFileDirectories(filename);\n\t\tfs.writeFile(filename,tiddler.fields.text,contentTypeInfo.encoding,function(err) {\n\t\t\tself.callback(err);\n\t\t});\n\t} else {\n\t\treturn \"Missing tiddler: \" + title;\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/savetiddler.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/savetiddlers.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/savetiddlers.js\ntype: application/javascript\nmodule-type: command\n\nCommand to save several tiddlers to a folder of files\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.info = {\n\tname: \"savetiddlers\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing filename\";\n\t}\n\tvar self = this,\n\t\tfs = require(\"fs\"),\n\t\tpath = require(\"path\"),\n\t\twiki = this.commander.wiki,\n\t\tfilter = this.params[0],\n\t\tpathname = path.resolve(this.commander.outputPath,this.params[1]),\n\t\tdeleteDirectory = (this.params[2] || \"\").toLowerCase() !== \"noclean\",\n\t\ttiddlers = wiki.filterTiddlers(filter);\n\tif(deleteDirectory) {\n\t\t$tw.utils.deleteDirectory(pathname);\n\t}\n\t$tw.utils.createDirectory(pathname);\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = self.commander.wiki.getTiddler(title),\n\t\t\ttype = tiddler.fields.type || \"text/vnd.tiddlywiki\",\n\t\t\tcontentTypeInfo = $tw.config.contentTypeInfo[type] || {encoding: \"utf8\"},\n\t\t\tfilename = path.resolve(pathname,encodeURIComponent(title));\n\t\tfs.writeFileSync(filename,tiddler.fields.text,contentTypeInfo.encoding);\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/savetiddlers.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/server.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/server.js\ntype: application/javascript\nmodule-type: command\n\nServe tiddlers over http\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nif($tw.node) {\n\tvar util = require(\"util\"),\n\t\tfs = require(\"fs\"),\n\t\turl = require(\"url\"),\n\t\tpath = require(\"path\"),\n\t\thttp = require(\"http\");\n}\n\nexports.info = {\n\tname: \"server\",\n\tsynchronous: true\n};\n\n/*\nA simple HTTP server with regexp-based routes\n*/\nfunction SimpleServer(options) {\n\tthis.routes = options.routes || [];\n\tthis.wiki = options.wiki;\n\tthis.variables = options.variables || {};\n}\n\nSimpleServer.prototype.set = function(obj) {\n\tvar self = this;\n\t$tw.utils.each(obj,function(value,name) {\n\t\tself.variables[name] = value;\n\t});\n};\n\nSimpleServer.prototype.get = function(name) {\n\treturn this.variables[name];\n};\n\nSimpleServer.prototype.addRoute = function(route) {\n\tthis.routes.push(route);\n};\n\nSimpleServer.prototype.findMatchingRoute = function(request,state) {\n\tvar pathprefix = this.get(\"pathprefix\") || \"\";\n\tfor(var t=0; t<this.routes.length; t++) {\n\t\tvar potentialRoute = this.routes[t],\n\t\t\tpathRegExp = potentialRoute.path,\n\t\t\tpathname = state.urlInfo.pathname,\n\t\t\tmatch;\n\t\tif(pathprefix) {\n\t\t\tif(pathname.substr(0,pathprefix.length) === pathprefix) {\n\t\t\t\tpathname = pathname.substr(pathprefix.length);\n\t\t\t\tmatch = potentialRoute.path.exec(pathname);\n\t\t\t} else {\n\t\t\t\tmatch = false;\n\t\t\t}\n\t\t} else {\n\t\t\tmatch = potentialRoute.path.exec(pathname);\n\t\t}\n\t\tif(match && request.method === potentialRoute.method) {\n\t\t\tstate.params = [];\n\t\t\tfor(var p=1; p<match.length; p++) {\n\t\t\t\tstate.params.push(match[p]);\n\t\t\t}\n\t\t\treturn potentialRoute;\n\t\t}\n\t}\n\treturn null;\n};\n\nSimpleServer.prototype.checkCredentials = function(request,incomingUsername,incomingPassword) {\n\tvar header = request.headers.authorization || \"\",\n\t\ttoken = header.split(/\\s+/).pop() || \"\",\n\t\tauth = $tw.utils.base64Decode(token),\n\t\tparts = auth.split(/:/),\n\t\tusername = parts[0],\n\t\tpassword = parts[1];\n\tif(incomingUsername === username && incomingPassword === password) {\n\t\treturn \"ALLOWED\";\n\t} else {\n\t\treturn \"DENIED\";\n\t}\n};\n\nSimpleServer.prototype.requestHandler = function(request,response) {\n\t// Compose the state object\n\tvar self = this;\n\tvar state = {};\n\tstate.wiki = self.wiki;\n\tstate.server = self;\n\tstate.urlInfo = url.parse(request.url);\n\t// Find the route that matches this path\n\tvar route = self.findMatchingRoute(request,state);\n\t// Check for the username and password if we've got one\n\tvar username = self.get(\"username\"),\n\t\tpassword = self.get(\"password\");\n\tif(username && password) {\n\t\t// Check they match\n\t\tif(self.checkCredentials(request,username,password) !== \"ALLOWED\") {\n\t\t\tvar servername = state.wiki.getTiddlerText(\"$:/SiteTitle\") || \"TiddlyWiki5\";\n\t\t\tresponse.writeHead(401,\"Authentication required\",{\n\t\t\t\t\"WWW-Authenticate\": 'Basic realm=\"Please provide your username and password to login to ' + servername + '\"'\n\t\t\t});\n\t\t\tresponse.end();\n\t\t\treturn;\n\t\t}\n\t}\n\t// Return a 404 if we didn't find a route\n\tif(!route) {\n\t\tresponse.writeHead(404);\n\t\tresponse.end();\n\t\treturn;\n\t}\n\t// Set the encoding for the incoming request\n\t// TODO: Presumably this would need tweaking if we supported PUTting binary tiddlers\n\trequest.setEncoding(\"utf8\");\n\t// Dispatch the appropriate method\n\tswitch(request.method) {\n\t\tcase \"GET\": // Intentional fall-through\n\t\tcase \"DELETE\":\n\t\t\troute.handler(request,response,state);\n\t\t\tbreak;\n\t\tcase \"PUT\":\n\t\t\tvar data = \"\";\n\t\t\trequest.on(\"data\",function(chunk) {\n\t\t\t\tdata += chunk.toString();\n\t\t\t});\n\t\t\trequest.on(\"end\",function() {\n\t\t\t\tstate.data = data;\n\t\t\t\troute.handler(request,response,state);\n\t\t\t});\n\t\t\tbreak;\n\t}\n};\n\t\nSimpleServer.prototype.listen = function(port,host) {\n\thttp.createServer(this.requestHandler.bind(this)).listen(port,host);\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n\t// Set up server\n\tthis.server = new SimpleServer({\n\t\twiki: this.commander.wiki\n\t});\n\t// Add route handlers\n\tthis.server.addRoute({\n\t\tmethod: \"PUT\",\n\t\tpath: /^\\/recipes\\/default\\/tiddlers\\/(.+)$/,\n\t\thandler: function(request,response,state) {\n\t\t\tvar title = decodeURIComponent(state.params[0]),\n\t\t\t\tfields = JSON.parse(state.data);\n\t\t\t// Pull up any subfields in the `fields` object\n\t\t\tif(fields.fields) {\n\t\t\t\t$tw.utils.each(fields.fields,function(field,name) {\n\t\t\t\t\tfields[name] = field;\n\t\t\t\t});\n\t\t\t\tdelete fields.fields;\n\t\t\t}\n\t\t\t// Remove any revision field\n\t\t\tif(fields.revision) {\n\t\t\t\tdelete fields.revision;\n\t\t\t}\n\t\t\tstate.wiki.addTiddler(new $tw.Tiddler(state.wiki.getCreationFields(),fields,{title: title},state.wiki.getModificationFields()));\n\t\t\tvar changeCount = state.wiki.getChangeCount(title).toString();\n\t\t\tresponse.writeHead(204, \"OK\",{\n\t\t\t\tEtag: \"\\\"default/\" + encodeURIComponent(title) + \"/\" + changeCount + \":\\\"\",\n\t\t\t\t\"Content-Type\": \"text/plain\"\n\t\t\t});\n\t\t\tresponse.end();\n\t\t}\n\t});\n\tthis.server.addRoute({\n\t\tmethod: \"DELETE\",\n\t\tpath: /^\\/bags\\/default\\/tiddlers\\/(.+)$/,\n\t\thandler: function(request,response,state) {\n\t\t\tvar title = decodeURIComponent(state.params[0]);\n\t\t\tstate.wiki.deleteTiddler(title);\n\t\t\tresponse.writeHead(204, \"OK\", {\n\t\t\t\t\"Content-Type\": \"text/plain\"\n\t\t\t});\n\t\t\tresponse.end();\n\t\t}\n\t});\n\tthis.server.addRoute({\n\t\tmethod: \"GET\",\n\t\tpath: /^\\/$/,\n\t\thandler: function(request,response,state) {\n\t\t\tresponse.writeHead(200, {\"Content-Type\": state.server.get(\"serveType\")});\n\t\t\tvar text = state.wiki.renderTiddler(state.server.get(\"renderType\"),state.server.get(\"rootTiddler\"));\n\t\t\tresponse.end(text,\"utf8\");\n\t\t}\n\t});\n\tthis.server.addRoute({\n\t\tmethod: \"GET\",\n\t\tpath: /^\\/status$/,\n\t\thandler: function(request,response,state) {\n\t\t\tresponse.writeHead(200, {\"Content-Type\": \"application/json\"});\n\t\t\tvar text = JSON.stringify({\n\t\t\t\tusername: state.server.get(\"username\"),\n\t\t\t\tspace: {\n\t\t\t\t\trecipe: \"default\"\n\t\t\t\t},\n\t\t\t\ttiddlywiki_version: $tw.version\n\t\t\t});\n\t\t\tresponse.end(text,\"utf8\");\n\t\t}\n\t});\n\tthis.server.addRoute({\n\t\tmethod: \"GET\",\n\t\tpath: /^\\/favicon.ico$/,\n\t\thandler: function(request,response,state) {\n\t\t\tresponse.writeHead(200, {\"Content-Type\": \"image/x-icon\"});\n\t\t\tvar buffer = state.wiki.getTiddlerText(\"$:/favicon.ico\",\"\");\n\t\t\tresponse.end(buffer,\"base64\");\n\t\t}\n\t});\n\tthis.server.addRoute({\n\t\tmethod: \"GET\",\n\t\tpath: /^\\/recipes\\/default\\/tiddlers.json$/,\n\t\thandler: function(request,response,state) {\n\t\t\tresponse.writeHead(200, {\"Content-Type\": \"application/json\"});\n\t\t\tvar tiddlers = [];\n\t\t\tstate.wiki.forEachTiddler({sortField: \"title\"},function(title,tiddler) {\n\t\t\t\tvar tiddlerFields = {};\n\t\t\t\t$tw.utils.each(tiddler.fields,function(field,name) {\n\t\t\t\t\tif(name !== \"text\") {\n\t\t\t\t\t\ttiddlerFields[name] = tiddler.getFieldString(name);\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t\ttiddlerFields.revision = state.wiki.getChangeCount(title);\n\t\t\t\ttiddlerFields.type = tiddlerFields.type || \"text/vnd.tiddlywiki\";\n\t\t\t\ttiddlers.push(tiddlerFields);\n\t\t\t});\n\t\t\tvar text = JSON.stringify(tiddlers);\n\t\t\tresponse.end(text,\"utf8\");\n\t\t}\n\t});\n\tthis.server.addRoute({\n\t\tmethod: \"GET\",\n\t\tpath: /^\\/recipes\\/default\\/tiddlers\\/(.+)$/,\n\t\thandler: function(request,response,state) {\n\t\t\tvar title = decodeURIComponent(state.params[0]),\n\t\t\t\ttiddler = state.wiki.getTiddler(title),\n\t\t\t\ttiddlerFields = {},\n\t\t\t\tknownFields = [\n\t\t\t\t\t\"bag\", \"created\", \"creator\", \"modified\", \"modifier\", \"permissions\", \"recipe\", \"revision\", \"tags\", \"text\", \"title\", \"type\", \"uri\"\n\t\t\t\t];\n\t\t\tif(tiddler) {\n\t\t\t\t$tw.utils.each(tiddler.fields,function(field,name) {\n\t\t\t\t\tvar value = tiddler.getFieldString(name);\n\t\t\t\t\tif(knownFields.indexOf(name) !== -1) {\n\t\t\t\t\t\ttiddlerFields[name] = value;\n\t\t\t\t\t} else {\n\t\t\t\t\t\ttiddlerFields.fields = tiddlerFields.fields || {};\n\t\t\t\t\t\ttiddlerFields.fields[name] = value;\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t\ttiddlerFields.revision = state.wiki.getChangeCount(title);\n\t\t\t\ttiddlerFields.type = tiddlerFields.type || \"text/vnd.tiddlywiki\";\n\t\t\t\tresponse.writeHead(200, {\"Content-Type\": \"application/json\"});\n\t\t\t\tresponse.end(JSON.stringify(tiddlerFields),\"utf8\");\n\t\t\t} else {\n\t\t\t\tresponse.writeHead(404);\n\t\t\t\tresponse.end();\n\t\t\t}\n\t\t}\n\t});\n};\n\nCommand.prototype.execute = function() {\n\tif(!$tw.boot.wikiTiddlersPath) {\n\t\t$tw.utils.warning(\"Warning: Wiki folder '\" + $tw.boot.wikiPath + \"' does not exist or is missing a tiddlywiki.info file\");\n\t}\n\tvar port = this.params[0] || \"8080\",\n\t\trootTiddler = this.params[1] || \"$:/core/save/all\",\n\t\trenderType = this.params[2] || \"text/plain\",\n\t\tserveType = this.params[3] || \"text/html\",\n\t\tusername = this.params[4],\n\t\tpassword = this.params[5],\n\t\thost = this.params[6] || \"127.0.0.1\",\n\t\tpathprefix = this.params[7];\n\tthis.server.set({\n\t\trootTiddler: rootTiddler,\n\t\trenderType: renderType,\n\t\tserveType: serveType,\n\t\tusername: username,\n\t\tpassword: password,\n\t\tpathprefix: pathprefix\n\t});\n\tthis.server.listen(port,host);\n\tconsole.log(\"Serving on \" + host + \":\" + port);\n\tconsole.log(\"(press ctrl-C to exit)\");\n\t// Warn if required plugins are missing\n\tif(!$tw.wiki.getTiddler(\"$:/plugins/tiddlywiki/tiddlyweb\") || !$tw.wiki.getTiddler(\"$:/plugins/tiddlywiki/filesystem\")) {\n\t\t$tw.utils.warning(\"Warning: Plugins required for client-server operation (\\\"tiddlywiki/filesystem\\\" and \\\"tiddlywiki/tiddlyweb\\\") are missing from tiddlywiki.info file\");\n\t}\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/server.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/setfield.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/setfield.js\ntype: application/javascript\nmodule-type: command\n\nCommand to modify selected tiddlers to set a field to the text of a template tiddler that has been wikified with the selected tiddler as the current tiddler.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.info = {\n\tname: \"setfield\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 4) {\n\t\treturn \"Missing parameters\";\n\t}\n\tvar self = this,\n\t\twiki = this.commander.wiki,\n\t\tfilter = this.params[0],\n\t\tfieldname = this.params[1] || \"text\",\n\t\ttemplatetitle = this.params[2],\n\t\trendertype = this.params[3] || \"text/plain\",\n\t\ttiddlers = wiki.filterTiddlers(filter);\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar parser = wiki.parseTiddler(templatetitle),\n\t\t\tnewFields = {},\n\t\t\ttiddler = wiki.getTiddler(title);\n\t\tif(parser) {\n\t\t\tvar widgetNode = wiki.makeWidget(parser,{variables: {currentTiddler: title}});\n\t\t\tvar container = $tw.fakeDocument.createElement(\"div\");\n\t\t\twidgetNode.render(container,null);\n\t\t\tnewFields[fieldname] = rendertype === \"text/html\" ? container.innerHTML : container.textContent;\n\t\t} else {\n\t\t\tnewFields[fieldname] = undefined;\n\t\t}\n\t\twiki.addTiddler(new $tw.Tiddler(tiddler,newFields));\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/setfield.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/unpackplugin.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/unpackplugin.js\ntype: application/javascript\nmodule-type: command\n\nCommand to extract the shadow tiddlers from within a plugin\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"unpackplugin\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander,callback) {\n\tthis.params = params;\n\tthis.commander = commander;\n\tthis.callback = callback;\n};\n\nCommand.prototype.execute = function() {\n\tif(this.params.length < 1) {\n\t\treturn \"Missing plugin name\";\n\t}\n\tvar self = this,\n\t\ttitle = this.params[0],\n\t\tpluginData = this.commander.wiki.getTiddlerDataCached(title);\n\tif(!pluginData) {\n\t\treturn \"Plugin '\" + title + \"' not found\";\n\t}\n\t$tw.utils.each(pluginData.tiddlers,function(tiddler) {\n\t\tself.commander.wiki.addTiddler(new $tw.Tiddler(tiddler));\n\t});\n\treturn null;\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/unpackplugin.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/verbose.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/verbose.js\ntype: application/javascript\nmodule-type: command\n\nVerbose command\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"verbose\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tthis.commander.verbose = true;\n\t// Output the boot message log\n\tthis.commander.streams.output.write(\"Boot log:\\n \" + $tw.boot.logMessages.join(\"\\n \") + \"\\n\");\n\treturn null; // No error\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/verbose.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/commands/version.js": {
"text": "/*\\\ntitle: $:/core/modules/commands/version.js\ntype: application/javascript\nmodule-type: command\n\nVersion command\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.info = {\n\tname: \"version\",\n\tsynchronous: true\n};\n\nvar Command = function(params,commander) {\n\tthis.params = params;\n\tthis.commander = commander;\n};\n\nCommand.prototype.execute = function() {\n\tthis.commander.streams.output.write($tw.version + \"\\n\");\n\treturn null; // No error\n};\n\nexports.Command = Command;\n\n})();\n",
"title": "$:/core/modules/commands/version.js",
"type": "application/javascript",
"module-type": "command"
},
"$:/core/modules/config.js": {
"text": "/*\\\ntitle: $:/core/modules/config.js\ntype: application/javascript\nmodule-type: config\n\nCore configuration constants\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.preferences = {};\n\nexports.preferences.notificationDuration = 3 * 1000;\nexports.preferences.jsonSpaces = 4;\n\nexports.textPrimitives = {\n\tupperLetter: \"[A-Z\\u00c0-\\u00d6\\u00d8-\\u00de\\u0150\\u0170]\",\n\tlowerLetter: \"[a-z\\u00df-\\u00f6\\u00f8-\\u00ff\\u0151\\u0171]\",\n\tanyLetter: \"[A-Za-z0-9\\u00c0-\\u00d6\\u00d8-\\u00de\\u00df-\\u00f6\\u00f8-\\u00ff\\u0150\\u0170\\u0151\\u0171]\",\n\tblockPrefixLetters:\t\"[A-Za-z0-9-_\\u00c0-\\u00d6\\u00d8-\\u00de\\u00df-\\u00f6\\u00f8-\\u00ff\\u0150\\u0170\\u0151\\u0171]\"\n};\n\nexports.textPrimitives.unWikiLink = \"~\";\nexports.textPrimitives.wikiLink = exports.textPrimitives.upperLetter + \"+\" +\n\texports.textPrimitives.lowerLetter + \"+\" +\n\texports.textPrimitives.upperLetter +\n\texports.textPrimitives.anyLetter + \"*\";\n\nexports.htmlEntities = {quot:34, amp:38, apos:39, lt:60, gt:62, nbsp:160, iexcl:161, cent:162, pound:163, curren:164, yen:165, brvbar:166, sect:167, uml:168, copy:169, ordf:170, laquo:171, not:172, shy:173, reg:174, macr:175, deg:176, plusmn:177, sup2:178, sup3:179, acute:180, micro:181, para:182, middot:183, cedil:184, sup1:185, ordm:186, raquo:187, frac14:188, frac12:189, frac34:190, iquest:191, Agrave:192, Aacute:193, Acirc:194, Atilde:195, Auml:196, Aring:197, AElig:198, Ccedil:199, Egrave:200, Eacute:201, Ecirc:202, Euml:203, Igrave:204, Iacute:205, Icirc:206, Iuml:207, ETH:208, Ntilde:209, Ograve:210, Oacute:211, Ocirc:212, Otilde:213, Ouml:214, times:215, Oslash:216, Ugrave:217, Uacute:218, Ucirc:219, Uuml:220, Yacute:221, THORN:222, szlig:223, agrave:224, aacute:225, acirc:226, atilde:227, auml:228, aring:229, aelig:230, ccedil:231, egrave:232, eacute:233, ecirc:234, euml:235, igrave:236, iacute:237, icirc:238, iuml:239, eth:240, ntilde:241, ograve:242, oacute:243, ocirc:244, otilde:245, ouml:246, divide:247, oslash:248, ugrave:249, uacute:250, ucirc:251, uuml:252, yacute:253, thorn:254, yuml:255, OElig:338, oelig:339, Scaron:352, scaron:353, Yuml:376, fnof:402, circ:710, tilde:732, Alpha:913, Beta:914, Gamma:915, Delta:916, Epsilon:917, Zeta:918, Eta:919, Theta:920, Iota:921, Kappa:922, Lambda:923, Mu:924, Nu:925, Xi:926, Omicron:927, Pi:928, Rho:929, Sigma:931, Tau:932, Upsilon:933, Phi:934, Chi:935, Psi:936, Omega:937, alpha:945, beta:946, gamma:947, delta:948, epsilon:949, zeta:950, eta:951, theta:952, iota:953, kappa:954, lambda:955, mu:956, nu:957, xi:958, omicron:959, pi:960, rho:961, sigmaf:962, sigma:963, tau:964, upsilon:965, phi:966, chi:967, psi:968, omega:969, thetasym:977, upsih:978, piv:982, ensp:8194, emsp:8195, thinsp:8201, zwnj:8204, zwj:8205, lrm:8206, rlm:8207, ndash:8211, mdash:8212, lsquo:8216, rsquo:8217, sbquo:8218, ldquo:8220, rdquo:8221, bdquo:8222, dagger:8224, Dagger:8225, bull:8226, hellip:8230, permil:8240, prime:8242, Prime:8243, lsaquo:8249, rsaquo:8250, oline:8254, frasl:8260, euro:8364, image:8465, weierp:8472, real:8476, trade:8482, alefsym:8501, larr:8592, uarr:8593, rarr:8594, darr:8595, harr:8596, crarr:8629, lArr:8656, uArr:8657, rArr:8658, dArr:8659, hArr:8660, forall:8704, part:8706, exist:8707, empty:8709, nabla:8711, isin:8712, notin:8713, ni:8715, prod:8719, sum:8721, minus:8722, lowast:8727, radic:8730, prop:8733, infin:8734, ang:8736, and:8743, or:8744, cap:8745, cup:8746, int:8747, there4:8756, sim:8764, cong:8773, asymp:8776, ne:8800, equiv:8801, le:8804, ge:8805, sub:8834, sup:8835, nsub:8836, sube:8838, supe:8839, oplus:8853, otimes:8855, perp:8869, sdot:8901, lceil:8968, rceil:8969, lfloor:8970, rfloor:8971, lang:9001, rang:9002, loz:9674, spades:9824, clubs:9827, hearts:9829, diams:9830 };\n\nexports.htmlVoidElements = \"area,base,br,col,command,embed,hr,img,input,keygen,link,meta,param,source,track,wbr\".split(\",\");\n\nexports.htmlBlockElements = \"address,article,aside,audio,blockquote,canvas,dd,div,dl,fieldset,figcaption,figure,footer,form,h1,h2,h3,h4,h5,h6,header,hgroup,hr,li,noscript,ol,output,p,pre,section,table,tfoot,ul,video\".split(\",\");\n\nexports.htmlUnsafeElements = \"script\".split(\",\");\n\n})();\n",
"title": "$:/core/modules/config.js",
"type": "application/javascript",
"module-type": "config"
},
"$:/core/modules/deserializers.js": {
"text": "/*\\\ntitle: $:/core/modules/deserializers.js\ntype: application/javascript\nmodule-type: tiddlerdeserializer\n\nFunctions to deserialise tiddlers from a block of text\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nUtility function to parse an old-style tiddler DIV in a *.tid file. It looks like this:\n\n<div title=\"Title\" creator=\"JoeBloggs\" modifier=\"JoeBloggs\" created=\"201102111106\" modified=\"201102111310\" tags=\"myTag [[my long tag]]\">\n<pre>The text of the tiddler (without the expected HTML encoding).\n</pre>\n</div>\n\nNote that the field attributes are HTML encoded, but that the body of the <PRE> tag is not encoded.\n\nWhen these tiddler DIVs are encountered within a TiddlyWiki HTML file then the body is encoded in the usual way.\n*/\nvar parseTiddlerDiv = function(text /* [,fields] */) {\n\t// Slot together the default results\n\tvar result = {};\n\tif(arguments.length > 1) {\n\t\tfor(var f=1; f<arguments.length; f++) {\n\t\t\tvar fields = arguments[f];\n\t\t\tfor(var t in fields) {\n\t\t\t\tresult[t] = fields[t];\t\t\n\t\t\t}\n\t\t}\n\t}\n\t// Parse the DIV body\n\tvar startRegExp = /^\\s*<div\\s+([^>]*)>(\\s*<pre>)?/gi,\n\t\tendRegExp,\n\t\tmatch = startRegExp.exec(text);\n\tif(match) {\n\t\t// Old-style DIVs don't have the <pre> tag\n\t\tif(match[2]) {\n\t\t\tendRegExp = /<\\/pre>\\s*<\\/div>\\s*$/gi;\n\t\t} else {\n\t\t\tendRegExp = /<\\/div>\\s*$/gi;\n\t\t}\n\t\tvar endMatch = endRegExp.exec(text);\n\t\tif(endMatch) {\n\t\t\t// Extract the text\n\t\t\tresult.text = text.substring(match.index + match[0].length,endMatch.index);\n\t\t\t// Process the attributes\n\t\t\tvar attrRegExp = /\\s*([^=\\s]+)\\s*=\\s*(?:\"([^\"]*)\"|'([^']*)')/gi,\n\t\t\t\tattrMatch;\n\t\t\tdo {\n\t\t\t\tattrMatch = attrRegExp.exec(match[1]);\n\t\t\t\tif(attrMatch) {\n\t\t\t\t\tvar name = attrMatch[1];\n\t\t\t\t\tvar value = attrMatch[2] !== undefined ? attrMatch[2] : attrMatch[3];\n\t\t\t\t\tresult[name] = value;\n\t\t\t\t}\n\t\t\t} while(attrMatch);\n\t\t\treturn result;\n\t\t}\n\t}\n\treturn undefined;\n};\n\nexports[\"application/x-tiddler-html-div\"] = function(text,fields) {\n\treturn [parseTiddlerDiv(text,fields)];\n};\n\nexports[\"application/json\"] = function(text,fields) {\n\tvar incoming,\n\t\tresults = [];\n\ttry {\n\t\tincoming = JSON.parse(text);\n\t} catch(e) {\n\t\tincoming = [{\n\t\t\ttitle: \"JSON error: \" + e,\n\t\t\ttext: \"\"\n\t\t}]\n\t}\n\tif(!$tw.utils.isArray(incoming)) {\n\t\tincoming = [incoming];\n\t}\n\tfor(var t=0; t<incoming.length; t++) {\n\t\tvar incomingFields = incoming[t],\n\t\t\tfields = {};\n\t\tfor(var f in incomingFields) {\n\t\t\tif(typeof incomingFields[f] === \"string\") {\n\t\t\t\tfields[f] = incomingFields[f];\n\t\t\t}\n\t\t}\n\t\tresults.push(fields);\n\t}\n\treturn results;\n};\n\n/*\nParse an HTML file into tiddlers. There are three possibilities:\n# A TiddlyWiki classic HTML file containing `text/x-tiddlywiki` tiddlers\n# A TiddlyWiki5 HTML file containing `text/vnd.tiddlywiki` tiddlers\n# An ordinary HTML file\n*/\nexports[\"text/html\"] = function(text,fields) {\n\t// Check if we've got a store area\n\tvar storeAreaMarkerRegExp = /<div id=[\"']?storeArea['\"]?( style=[\"']?display:none;[\"']?)?>/gi,\n\t\tmatch = storeAreaMarkerRegExp.exec(text);\n\tif(match) {\n\t\t// If so, it's either a classic TiddlyWiki file or an unencrypted TW5 file\n\t\t// First read the normal tiddlers\n\t\tvar results = deserializeTiddlyWikiFile(text,storeAreaMarkerRegExp.lastIndex,!!match[1],fields);\n\t\t// Then any system tiddlers\n\t\tvar systemAreaMarkerRegExp = /<div id=[\"']?systemArea['\"]?( style=[\"']?display:none;[\"']?)?>/gi,\n\t\t\tsysMatch = systemAreaMarkerRegExp.exec(text);\n\t\tif(sysMatch) {\n\t\t\tresults.push.apply(results,deserializeTiddlyWikiFile(text,systemAreaMarkerRegExp.lastIndex,!!sysMatch[1],fields));\n\t\t}\n\t\treturn results;\n\t} else {\n\t\t// Check whether we've got an encrypted file\n\t\tvar encryptedStoreArea = $tw.utils.extractEncryptedStoreArea(text);\n\t\tif(encryptedStoreArea) {\n\t\t\t// If so, attempt to decrypt it using the current password\n\t\t\treturn $tw.utils.decryptStoreArea(encryptedStoreArea);\n\t\t} else {\n\t\t\t// It's not a TiddlyWiki so we'll return the entire HTML file as a tiddler\n\t\t\treturn deserializeHtmlFile(text,fields);\n\t\t}\n\t}\n};\n\nfunction deserializeHtmlFile(text,fields) {\n\tvar result = {};\n\t$tw.utils.each(fields,function(value,name) {\n\t\tresult[name] = value;\n\t});\n\tresult.text = text;\n\tresult.type = \"text/html\";\n\treturn [result];\n}\n\nfunction deserializeTiddlyWikiFile(text,storeAreaEnd,isTiddlyWiki5,fields) {\n\tvar results = [],\n\t\tendOfDivRegExp = /(<\\/div>\\s*)/gi,\n\t\tstartPos = storeAreaEnd,\n\t\tdefaultType = isTiddlyWiki5 ? undefined : \"text/x-tiddlywiki\";\n\tendOfDivRegExp.lastIndex = startPos;\n\tvar match = endOfDivRegExp.exec(text);\n\twhile(match) {\n\t\tvar endPos = endOfDivRegExp.lastIndex,\n\t\t\ttiddlerFields = parseTiddlerDiv(text.substring(startPos,endPos),fields,{type: defaultType});\n\t\tif(!tiddlerFields) {\n\t\t\tbreak;\n\t\t}\n\t\t$tw.utils.each(tiddlerFields,function(value,name) {\n\t\t\tif(typeof value === \"string\") {\n\t\t\t\ttiddlerFields[name] = $tw.utils.htmlDecode(value);\n\t\t\t}\n\t\t});\n\t\tif(tiddlerFields.text !== null) {\n\t\t\tresults.push(tiddlerFields);\n\t\t}\n\t\tstartPos = endPos;\n\t\tmatch = endOfDivRegExp.exec(text);\n\t}\n\treturn results;\n}\n\n})();\n",
"title": "$:/core/modules/deserializers.js",
"type": "application/javascript",
"module-type": "tiddlerdeserializer"
},
"$:/core/modules/editor/engines/framed.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/engines/framed.js\ntype: application/javascript\nmodule-type: library\n\nText editor engine based on a simple input or textarea within an iframe. This is done so that the selection is preserved even when clicking away from the textarea\n\n\\*/\n(function(){\n\n/*jslint node: true,browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar HEIGHT_VALUE_TITLE = \"$:/config/TextEditor/EditorHeight/Height\";\n\nfunction FramedEngine(options) {\n\t// Save our options\n\toptions = options || {};\n\tthis.widget = options.widget;\n\tthis.value = options.value;\n\tthis.parentNode = options.parentNode;\n\tthis.nextSibling = options.nextSibling;\n\t// Create our hidden dummy text area for reading styles\n\tthis.dummyTextArea = this.widget.document.createElement(\"textarea\");\n\tif(this.widget.editClass) {\n\t\tthis.dummyTextArea.className = this.widget.editClass;\n\t}\n\tthis.dummyTextArea.setAttribute(\"hidden\",\"true\");\n\tthis.parentNode.insertBefore(this.dummyTextArea,this.nextSibling);\n\tthis.widget.domNodes.push(this.dummyTextArea);\n\t// Create the iframe\n\tthis.iframeNode = this.widget.document.createElement(\"iframe\");\n\tthis.parentNode.insertBefore(this.iframeNode,this.nextSibling);\n\tthis.iframeDoc = this.iframeNode.contentWindow.document;\n\t// (Firefox requires us to put some empty content in the iframe)\n\tthis.iframeDoc.open();\n\tthis.iframeDoc.write(\"\");\n\tthis.iframeDoc.close();\n\t// Style the iframe\n\tthis.iframeNode.className = this.dummyTextArea.className;\n\tthis.iframeNode.style.border = \"none\";\n\tthis.iframeNode.style.padding = \"0\";\n\tthis.iframeNode.style.resize = \"none\";\n\tthis.iframeDoc.body.style.margin = \"0\";\n\tthis.iframeDoc.body.style.padding = \"0\";\n\tthis.widget.domNodes.push(this.iframeNode);\n\t// Construct the textarea or input node\n\tvar tag = this.widget.editTag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"input\";\n\t}\n\tthis.domNode = this.iframeDoc.createElement(tag);\n\t// Set the text\n\tif(this.widget.editTag === \"textarea\") {\n\t\tthis.domNode.appendChild(this.iframeDoc.createTextNode(this.value));\n\t} else {\n\t\tthis.domNode.value = this.value;\n\t}\n\t// Set the attributes\n\tif(this.widget.editType) {\n\t\tthis.domNode.setAttribute(\"type\",this.widget.editType);\n\t}\n\tif(this.widget.editPlaceholder) {\n\t\tthis.domNode.setAttribute(\"placeholder\",this.widget.editPlaceholder);\n\t}\n\tif(this.widget.editSize) {\n\t\tthis.domNode.setAttribute(\"size\",this.widget.editSize);\n\t}\n\tif(this.widget.editRows) {\n\t\tthis.domNode.setAttribute(\"rows\",this.widget.editRows);\n\t}\n\t// Copy the styles from the dummy textarea\n\tthis.copyStyles();\n\t// Add event listeners\n\t$tw.utils.addEventListeners(this.domNode,[\n\t\t{name: \"input\",handlerObject: this,handlerMethod: \"handleInputEvent\"},\n\t\t{name: \"keydown\",handlerObject: this.widget,handlerMethod: \"handleKeydownEvent\"}\n\t]);\n\t// Insert the element into the DOM\n\tthis.iframeDoc.body.appendChild(this.domNode);\n}\n\n/*\nCopy styles from the dummy text area to the textarea in the iframe\n*/\nFramedEngine.prototype.copyStyles = function() {\n\t// Copy all styles\n\t$tw.utils.copyStyles(this.dummyTextArea,this.domNode);\n\t// Override the ones that should not be set the same as the dummy textarea\n\tthis.domNode.style.display = \"block\";\n\tthis.domNode.style.width = \"100%\";\n\tthis.domNode.style.margin = \"0\";\n\t// In Chrome setting -webkit-text-fill-color overrides the placeholder text colour\n\tthis.domNode.style[\"-webkit-text-fill-color\"] = \"currentcolor\";\n};\n\n/*\nSet the text of the engine if it doesn't currently have focus\n*/\nFramedEngine.prototype.setText = function(text,type) {\n\tif(!this.domNode.isTiddlyWikiFakeDom) {\n\t\tif(this.domNode.ownerDocument.activeElement !== this.domNode) {\n\t\t\tthis.domNode.value = text;\n\t\t}\n\t\t// Fix the height if needed\n\t\tthis.fixHeight();\n\t}\n};\n\n/*\nGet the text of the engine\n*/\nFramedEngine.prototype.getText = function() {\n\treturn this.domNode.value;\n};\n\n/*\nFix the height of textarea to fit content\n*/\nFramedEngine.prototype.fixHeight = function() {\n\t// Make sure styles are updated\n\tthis.copyStyles();\n\t// Adjust height\n\tif(this.widget.editTag === \"textarea\") {\n\t\tif(this.widget.editAutoHeight) {\n\t\t\tif(this.domNode && !this.domNode.isTiddlyWikiFakeDom) {\n\t\t\t\tvar newHeight = $tw.utils.resizeTextAreaToFit(this.domNode,this.widget.editMinHeight);\n\t\t\t\tthis.iframeNode.style.height = (newHeight + 14) + \"px\"; // +14 for the border on the textarea\n\t\t\t}\n\t\t} else {\n\t\t\tvar fixedHeight = parseInt(this.widget.wiki.getTiddlerText(HEIGHT_VALUE_TITLE,\"400px\"),10);\n\t\t\tfixedHeight = Math.max(fixedHeight,20);\n\t\t\tthis.domNode.style.height = fixedHeight + \"px\";\n\t\t\tthis.iframeNode.style.height = (fixedHeight + 14) + \"px\";\n\t\t}\n\t}\n};\n\n/*\nFocus the engine node\n*/\nFramedEngine.prototype.focus = function() {\n\tif(this.domNode.focus && this.domNode.select) {\n\t\tthis.domNode.focus();\n\t\tthis.domNode.select();\n\t}\n};\n\n/*\nHandle a dom \"input\" event which occurs when the text has changed\n*/\nFramedEngine.prototype.handleInputEvent = function(event) {\n\tthis.widget.saveChanges(this.getText());\n\tthis.fixHeight();\n\treturn true;\n};\n\n/*\nCreate a blank structure representing a text operation\n*/\nFramedEngine.prototype.createTextOperation = function() {\n\tvar operation = {\n\t\ttext: this.domNode.value,\n\t\tselStart: this.domNode.selectionStart,\n\t\tselEnd: this.domNode.selectionEnd,\n\t\tcutStart: null,\n\t\tcutEnd: null,\n\t\treplacement: null,\n\t\tnewSelStart: null,\n\t\tnewSelEnd: null\n\t};\n\toperation.selection = operation.text.substring(operation.selStart,operation.selEnd);\n\treturn operation;\n};\n\n/*\nExecute a text operation\n*/\nFramedEngine.prototype.executeTextOperation = function(operation) {\n\t// Perform the required changes to the text area and the underlying tiddler\n\tvar newText = operation.text;\n\tif(operation.replacement !== null) {\n\t\tnewText = operation.text.substring(0,operation.cutStart) + operation.replacement + operation.text.substring(operation.cutEnd);\n\t\t// Attempt to use a execCommand to modify the value of the control\n\t\tif(this.iframeDoc.queryCommandSupported(\"insertText\") && this.iframeDoc.queryCommandSupported(\"delete\") && !$tw.browser.isFirefox) {\n\t\t\tthis.domNode.focus();\n\t\t\tthis.domNode.setSelectionRange(operation.cutStart,operation.cutEnd);\n\t\t\tif(operation.replacement === \"\") {\n\t\t\t\tthis.iframeDoc.execCommand(\"delete\",false,\"\");\n\t\t\t} else {\n\t\t\t\tthis.iframeDoc.execCommand(\"insertText\",false,operation.replacement);\n\t\t\t}\n\t\t} else {\n\t\t\tthis.domNode.value = newText;\n\t\t}\n\t\tthis.domNode.focus();\n\t\tthis.domNode.setSelectionRange(operation.newSelStart,operation.newSelEnd);\n\t}\n\tthis.domNode.focus();\n\treturn newText;\n};\n\nexports.FramedEngine = FramedEngine;\n\n})();\n",
"title": "$:/core/modules/editor/engines/framed.js",
"type": "application/javascript",
"module-type": "library"
},
"$:/core/modules/editor/engines/simple.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/engines/simple.js\ntype: application/javascript\nmodule-type: library\n\nText editor engine based on a simple input or textarea tag\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar HEIGHT_VALUE_TITLE = \"$:/config/TextEditor/EditorHeight/Height\";\n\nfunction SimpleEngine(options) {\n\t// Save our options\n\toptions = options || {};\n\tthis.widget = options.widget;\n\tthis.value = options.value;\n\tthis.parentNode = options.parentNode;\n\tthis.nextSibling = options.nextSibling;\n\t// Construct the textarea or input node\n\tvar tag = this.widget.editTag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"input\";\n\t}\n\tthis.domNode = this.widget.document.createElement(tag);\n\t// Set the text\n\tif(this.widget.editTag === \"textarea\") {\n\t\tthis.domNode.appendChild(this.widget.document.createTextNode(this.value));\n\t} else {\n\t\tthis.domNode.value = this.value;\n\t}\n\t// Set the attributes\n\tif(this.widget.editType) {\n\t\tthis.domNode.setAttribute(\"type\",this.widget.editType);\n\t}\n\tif(this.widget.editPlaceholder) {\n\t\tthis.domNode.setAttribute(\"placeholder\",this.widget.editPlaceholder);\n\t}\n\tif(this.widget.editSize) {\n\t\tthis.domNode.setAttribute(\"size\",this.widget.editSize);\n\t}\n\tif(this.widget.editRows) {\n\t\tthis.domNode.setAttribute(\"rows\",this.widget.editRows);\n\t}\n\tif(this.widget.editClass) {\n\t\tthis.domNode.className = this.widget.editClass;\n\t}\n\t// Add an input event handler\n\t$tw.utils.addEventListeners(this.domNode,[\n\t\t{name: \"focus\", handlerObject: this, handlerMethod: \"handleFocusEvent\"},\n\t\t{name: \"input\", handlerObject: this, handlerMethod: \"handleInputEvent\"}\n\t]);\n\t// Insert the element into the DOM\n\tthis.parentNode.insertBefore(this.domNode,this.nextSibling);\n\tthis.widget.domNodes.push(this.domNode);\n}\n\n/*\nSet the text of the engine if it doesn't currently have focus\n*/\nSimpleEngine.prototype.setText = function(text,type) {\n\tif(!this.domNode.isTiddlyWikiFakeDom) {\n\t\tif(this.domNode.ownerDocument.activeElement !== this.domNode || text === \"\") {\n\t\t\tthis.domNode.value = text;\n\t\t}\n\t\t// Fix the height if needed\n\t\tthis.fixHeight();\n\t}\n};\n\n/*\nGet the text of the engine\n*/\nSimpleEngine.prototype.getText = function() {\n\treturn this.domNode.value;\n};\n\n/*\nFix the height of textarea to fit content\n*/\nSimpleEngine.prototype.fixHeight = function() {\n\tif(this.widget.editTag === \"textarea\") {\n\t\tif(this.widget.editAutoHeight) {\n\t\t\tif(this.domNode && !this.domNode.isTiddlyWikiFakeDom) {\n\t\t\t\t$tw.utils.resizeTextAreaToFit(this.domNode,this.widget.editMinHeight);\n\t\t\t}\n\t\t} else {\n\t\t\tvar fixedHeight = parseInt(this.widget.wiki.getTiddlerText(HEIGHT_VALUE_TITLE,\"400px\"),10);\n\t\t\tfixedHeight = Math.max(fixedHeight,20);\n\t\t\tthis.domNode.style.height = fixedHeight + \"px\";\n\t\t}\n\t}\n};\n\n/*\nFocus the engine node\n*/\nSimpleEngine.prototype.focus = function() {\n\tif(this.domNode.focus && this.domNode.select) {\n\t\tthis.domNode.focus();\n\t\tthis.domNode.select();\n\t}\n};\n\n/*\nHandle a dom \"input\" event which occurs when the text has changed\n*/\nSimpleEngine.prototype.handleInputEvent = function(event) {\n\tthis.widget.saveChanges(this.getText());\n\tthis.fixHeight();\n\treturn true;\n};\n\n/*\nHandle a dom \"focus\" event\n*/\nSimpleEngine.prototype.handleFocusEvent = function(event) {\n\tif(this.widget.editFocusPopup) {\n\t\t$tw.popup.triggerPopup({\n\t\t\tdomNode: this.domNode,\n\t\t\ttitle: this.widget.editFocusPopup,\n\t\t\twiki: this.widget.wiki,\n\t\t\tforce: true\n\t\t});\n\t}\n\treturn true;\n};\n\n/*\nCreate a blank structure representing a text operation\n*/\nSimpleEngine.prototype.createTextOperation = function() {\n\treturn null;\n};\n\n/*\nExecute a text operation\n*/\nSimpleEngine.prototype.executeTextOperation = function(operation) {\n};\n\nexports.SimpleEngine = SimpleEngine;\n\n})();\n",
"title": "$:/core/modules/editor/engines/simple.js",
"type": "application/javascript",
"module-type": "library"
},
"$:/core/modules/editor/factory.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/factory.js\ntype: application/javascript\nmodule-type: library\n\nFactory for constructing text editor widgets with specified engines for the toolbar and non-toolbar cases\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar DEFAULT_MIN_TEXT_AREA_HEIGHT = \"100px\"; // Minimum height of textareas in pixels\n\n// Configuration tiddlers\nvar HEIGHT_MODE_TITLE = \"$:/config/TextEditor/EditorHeight/Mode\";\nvar ENABLE_TOOLBAR_TITLE = \"$:/config/TextEditor/EnableToolbar\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nfunction editTextWidgetFactory(toolbarEngine,nonToolbarEngine) {\n\n\tvar EditTextWidget = function(parseTreeNode,options) {\n\t\t// Initialise the editor operations if they've not been done already\n\t\tif(!this.editorOperations) {\n\t\t\tEditTextWidget.prototype.editorOperations = {};\n\t\t\t$tw.modules.applyMethods(\"texteditoroperation\",this.editorOperations);\n\t\t}\n\t\tthis.initialise(parseTreeNode,options);\n\t};\n\n\t/*\n\tInherit from the base widget class\n\t*/\n\tEditTextWidget.prototype = new Widget();\n\n\t/*\n\tRender this widget into the DOM\n\t*/\n\tEditTextWidget.prototype.render = function(parent,nextSibling) {\n\t\t// Save the parent dom node\n\t\tthis.parentDomNode = parent;\n\t\t// Compute our attributes\n\t\tthis.computeAttributes();\n\t\t// Execute our logic\n\t\tthis.execute();\n\t\t// Create the wrapper for the toolbar and render its content\n\t\tif(this.editShowToolbar) {\n\t\t\tthis.toolbarNode = this.document.createElement(\"div\");\n\t\t\tthis.toolbarNode.className = \"tc-editor-toolbar\";\n\t\t\tparent.insertBefore(this.toolbarNode,nextSibling);\n\t\t\tthis.renderChildren(this.toolbarNode,null);\n\t\t\tthis.domNodes.push(this.toolbarNode);\n\t\t}\n\t\t// Create our element\n\t\tvar editInfo = this.getEditInfo(),\n\t\t\tEngine = this.editShowToolbar ? toolbarEngine : nonToolbarEngine;\n\t\tthis.engine = new Engine({\n\t\t\t\twidget: this,\n\t\t\t\tvalue: editInfo.value,\n\t\t\t\ttype: editInfo.type,\n\t\t\t\tparentNode: parent,\n\t\t\t\tnextSibling: nextSibling\n\t\t\t});\n\t\t// Call the postRender hook\n\t\tif(this.postRender) {\n\t\t\tthis.postRender();\n\t\t}\n\t\t// Fix height\n\t\tthis.engine.fixHeight();\n\t\t// Focus if required\n\t\tif(this.editFocus === \"true\" || this.editFocus === \"yes\") {\n\t\t\tthis.engine.focus();\n\t\t}\n\t\t// Add widget message listeners\n\t\tthis.addEventListeners([\n\t\t\t{type: \"tm-edit-text-operation\", handler: \"handleEditTextOperationMessage\"}\n\t\t]);\n\t};\n\n\t/*\n\tGet the tiddler being edited and current value\n\t*/\n\tEditTextWidget.prototype.getEditInfo = function() {\n\t\t// Get the edit value\n\t\tvar self = this,\n\t\t\tvalue,\n\t\t\ttype = \"text/plain\",\n\t\t\tupdate;\n\t\tif(this.editIndex) {\n\t\t\tvalue = this.wiki.extractTiddlerDataItem(this.editTitle,this.editIndex,this.editDefault);\n\t\t\tupdate = function(value) {\n\t\t\t\tvar data = self.wiki.getTiddlerData(self.editTitle,{});\n\t\t\t\tif(data[self.editIndex] !== value) {\n\t\t\t\t\tdata[self.editIndex] = value;\n\t\t\t\t\tself.wiki.setTiddlerData(self.editTitle,data);\n\t\t\t\t}\n\t\t\t};\n\t\t} else {\n\t\t\t// Get the current tiddler and the field name\n\t\t\tvar tiddler = this.wiki.getTiddler(this.editTitle);\n\t\t\tif(tiddler) {\n\t\t\t\t// If we've got a tiddler, the value to display is the field string value\n\t\t\t\tvalue = tiddler.getFieldString(this.editField);\n\t\t\t\tif(this.editField === \"text\") {\n\t\t\t\t\ttype = tiddler.fields.type || \"text/vnd.tiddlywiki\";\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// Otherwise, we need to construct a default value for the editor\n\t\t\t\tswitch(this.editField) {\n\t\t\t\t\tcase \"text\":\n\t\t\t\t\t\tvalue = \"Type the text for the tiddler '\" + this.editTitle + \"'\";\n\t\t\t\t\t\ttype = \"text/vnd.tiddlywiki\";\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"title\":\n\t\t\t\t\t\tvalue = this.editTitle;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tdefault:\n\t\t\t\t\t\tvalue = \"\";\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t\tif(this.editDefault !== undefined) {\n\t\t\t\t\tvalue = this.editDefault;\n\t\t\t\t}\n\t\t\t}\n\t\t\tupdate = function(value) {\n\t\t\t\tvar tiddler = self.wiki.getTiddler(self.editTitle),\n\t\t\t\t\tupdateFields = {\n\t\t\t\t\t\ttitle: self.editTitle\n\t\t\t\t\t};\n\t\t\t\tupdateFields[self.editField] = value;\n\t\t\t\tself.wiki.addTiddler(new $tw.Tiddler(self.wiki.getCreationFields(),tiddler,updateFields,self.wiki.getModificationFields()));\n\t\t\t};\n\t\t}\n\t\tif(this.editType) {\n\t\t\ttype = this.editType;\n\t\t}\n\t\treturn {value: value || \"\", type: type, update: update};\n\t};\n\n\t/*\n\tHandle an edit text operation message from the toolbar\n\t*/\n\tEditTextWidget.prototype.handleEditTextOperationMessage = function(event) {\n\t\t// Prepare information about the operation\n\t\tvar operation = this.engine.createTextOperation();\n\t\t// Invoke the handler for the selected operation\n\t\tvar handler = this.editorOperations[event.param];\n\t\tif(handler) {\n\t\t\thandler.call(this,event,operation);\n\t\t}\n\t\t// Execute the operation via the engine\n\t\tvar newText = this.engine.executeTextOperation(operation);\n\t\t// Fix the tiddler height and save changes\n\t\tthis.engine.fixHeight();\n\t\tthis.saveChanges(newText);\n\t};\n\n\t/*\n\tCompute the internal state of the widget\n\t*/\n\tEditTextWidget.prototype.execute = function() {\n\t\t// Get our parameters\n\t\tthis.editTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\t\tthis.editField = this.getAttribute(\"field\",\"text\");\n\t\tthis.editIndex = this.getAttribute(\"index\");\n\t\tthis.editDefault = this.getAttribute(\"default\");\n\t\tthis.editClass = this.getAttribute(\"class\");\n\t\tthis.editPlaceholder = this.getAttribute(\"placeholder\");\n\t\tthis.editSize = this.getAttribute(\"size\");\n\t\tthis.editRows = this.getAttribute(\"rows\");\n\t\tthis.editAutoHeight = this.wiki.getTiddlerText(HEIGHT_MODE_TITLE,\"auto\");\n\t\tthis.editAutoHeight = this.getAttribute(\"autoHeight\",this.editAutoHeight === \"auto\" ? \"yes\" : \"no\") === \"yes\";\n\t\tthis.editMinHeight = this.getAttribute(\"minHeight\",DEFAULT_MIN_TEXT_AREA_HEIGHT);\n\t\tthis.editFocusPopup = this.getAttribute(\"focusPopup\");\n\t\tthis.editFocus = this.getAttribute(\"focus\");\n\t\t// Get the default editor element tag and type\n\t\tvar tag,type;\n\t\tif(this.editField === \"text\") {\n\t\t\ttag = \"textarea\";\n\t\t} else {\n\t\t\ttag = \"input\";\n\t\t\tvar fieldModule = $tw.Tiddler.fieldModules[this.editField];\n\t\t\tif(fieldModule && fieldModule.editTag) {\n\t\t\t\ttag = fieldModule.editTag;\n\t\t\t}\n\t\t\tif(fieldModule && fieldModule.editType) {\n\t\t\t\ttype = fieldModule.editType;\n\t\t\t}\n\t\t\ttype = type || \"text\";\n\t\t}\n\t\t// Get the rest of our parameters\n\t\tthis.editTag = this.getAttribute(\"tag\",tag);\n\t\tthis.editType = this.getAttribute(\"type\",type);\n\t\t// Make the child widgets\n\t\tthis.makeChildWidgets();\n\t\t// Determine whether to show the toolbar\n\t\tthis.editShowToolbar = this.wiki.getTiddlerText(ENABLE_TOOLBAR_TITLE,\"yes\");\n\t\tthis.editShowToolbar = (this.editShowToolbar === \"yes\") && !!(this.children && this.children.length > 0);\n\t};\n\n\t/*\n\tSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n\t*/\n\tEditTextWidget.prototype.refresh = function(changedTiddlers) {\n\t\tvar changedAttributes = this.computeAttributes();\n\t\t// Completely rerender if any of our attributes have changed\n\t\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes[\"default\"] || changedAttributes[\"class\"] || changedAttributes.placeholder || changedAttributes.size || changedAttributes.autoHeight || changedAttributes.minHeight || changedAttributes.focusPopup || changedAttributes.rows || changedTiddlers[HEIGHT_MODE_TITLE] || changedTiddlers[ENABLE_TOOLBAR_TITLE]) {\n\t\t\tthis.refreshSelf();\n\t\t\treturn true;\n\t\t} else if(changedTiddlers[this.editTitle]) {\n\t\t\tvar editInfo = this.getEditInfo();\n\t\t\tthis.updateEditor(editInfo.value,editInfo.type);\n\t\t}\n\t\tthis.engine.fixHeight();\n\t\tif(this.editShowToolbar) {\n\t\t\treturn this.refreshChildren(changedTiddlers);\t\t\t\n\t\t} else {\n\t\t\treturn false;\n\t\t}\n\t};\n\n\t/*\n\tUpdate the editor with new text. This method is separate from updateEditorDomNode()\n\tso that subclasses can override updateEditor() and still use updateEditorDomNode()\n\t*/\n\tEditTextWidget.prototype.updateEditor = function(text,type) {\n\t\tthis.updateEditorDomNode(text,type);\n\t};\n\n\t/*\n\tUpdate the editor dom node with new text\n\t*/\n\tEditTextWidget.prototype.updateEditorDomNode = function(text,type) {\n\t\tthis.engine.setText(text,type);\n\t};\n\n\t/*\n\tSave changes back to the tiddler store\n\t*/\n\tEditTextWidget.prototype.saveChanges = function(text) {\n\t\tvar editInfo = this.getEditInfo();\n\t\tif(text !== editInfo.value) {\n\t\t\teditInfo.update(text);\n\t\t}\n\t};\n\n\t/*\n\tHandle a dom \"keydown\" event, which we'll bubble up to our container for the keyboard widgets benefit\n\t*/\n\tEditTextWidget.prototype.handleKeydownEvent = function(event) {\n\t\t// Check for a keyboard shortcut\n\t\tif(this.toolbarNode) {\n\t\t\tvar shortcutElements = this.toolbarNode.querySelectorAll(\"[data-tw-keyboard-shortcut]\");\n\t\t\tfor(var index=0; index<shortcutElements.length; index++) {\n\t\t\t\tvar el = shortcutElements[index],\n\t\t\t\t\tshortcutData = el.getAttribute(\"data-tw-keyboard-shortcut\"),\n\t\t\t\t\tkeyInfoArray = $tw.keyboardManager.parseKeyDescriptors(shortcutData,{\n\t\t\t\t\t\twiki: this.wiki\n\t\t\t\t\t});\n\t\t\t\tif($tw.keyboardManager.checkKeyDescriptors(event,keyInfoArray)) {\n\t\t\t\t\tvar clickEvent = this.document.createEvent(\"Events\");\n\t\t\t\t clickEvent.initEvent(\"click\",true,false);\n\t\t\t\t el.dispatchEvent(clickEvent);\n\t\t\t\t\tevent.preventDefault();\n\t\t\t\t\tevent.stopPropagation();\n\t\t\t\t\treturn true;\t\t\t\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\t// Propogate the event to the container\n\t\tif(this.propogateKeydownEvent(event)) {\n\t\t\t// Ignore the keydown if it was already handled\n\t\t\tevent.preventDefault();\n\t\t\tevent.stopPropagation();\n\t\t\treturn true;\n\t\t}\n\t\t// Otherwise, process the keydown normally\n\t\treturn false;\n\t};\n\n\t/*\n\tPropogate keydown events to our container for the keyboard widgets benefit\n\t*/\n\tEditTextWidget.prototype.propogateKeydownEvent = function(event) {\n\t\tvar newEvent = this.document.createEventObject ? this.document.createEventObject() : this.document.createEvent(\"Events\");\n\t\tif(newEvent.initEvent) {\n\t\t\tnewEvent.initEvent(\"keydown\", true, true);\n\t\t}\n\t\tnewEvent.keyCode = event.keyCode;\n\t\tnewEvent.which = event.which;\n\t\tnewEvent.metaKey = event.metaKey;\n\t\tnewEvent.ctrlKey = event.ctrlKey;\n\t\tnewEvent.altKey = event.altKey;\n\t\tnewEvent.shiftKey = event.shiftKey;\n\t\treturn !this.parentDomNode.dispatchEvent(newEvent);\n\t};\n\n\treturn EditTextWidget;\n\n}\n\nexports.editTextWidgetFactory = editTextWidgetFactory;\n\n})();\n",
"title": "$:/core/modules/editor/factory.js",
"type": "application/javascript",
"module-type": "library"
},
"$:/core/modules/editor/operations/bitmap/clear.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/bitmap/clear.js\ntype: application/javascript\nmodule-type: bitmapeditoroperation\n\nBitmap editor operation to clear the image\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"clear\"] = function(event) {\n\tvar ctx = this.canvasDomNode.getContext(\"2d\");\n\tctx.globalAlpha = 1;\n\tctx.fillStyle = event.paramObject.colour || \"white\";\n\tctx.fillRect(0,0,this.canvasDomNode.width,this.canvasDomNode.height);\n\t// Save changes\n\tthis.strokeEnd();\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/bitmap/clear.js",
"type": "application/javascript",
"module-type": "bitmapeditoroperation"
},
"$:/core/modules/editor/operations/bitmap/resize.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/bitmap/resize.js\ntype: application/javascript\nmodule-type: bitmapeditoroperation\n\nBitmap editor operation to resize the image\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"resize\"] = function(event) {\n\t// Get the new width\n\tvar newWidth = parseInt(event.paramObject.width || this.canvasDomNode.width,10),\n\t\tnewHeight = parseInt(event.paramObject.height || this.canvasDomNode.height,10);\n\t// Update if necessary\n\tif(newWidth > 0 && newHeight > 0 && !(newWidth === this.currCanvas.width && newHeight === this.currCanvas.height)) {\n\t\tthis.changeCanvasSize(newWidth,newHeight);\n\t}\n\t// Update the input controls\n\tthis.refreshToolbar();\n\t// Save the image into the tiddler\n\tthis.saveChanges();\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/bitmap/resize.js",
"type": "application/javascript",
"module-type": "bitmapeditoroperation"
},
"$:/core/modules/editor/operations/text/excise.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/excise.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to excise the selection to a new tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"excise\"] = function(event,operation) {\n\tvar editTiddler = this.wiki.getTiddler(this.editTitle),\n\t\teditTiddlerTitle = this.editTitle;\n\tif(editTiddler && editTiddler.fields[\"draft.of\"]) {\n\t\teditTiddlerTitle = editTiddler.fields[\"draft.of\"];\n\t}\n\tvar excisionTitle = event.paramObject.title || this.wiki.generateNewTitle(\"New Excision\");\n\tthis.wiki.addTiddler(new $tw.Tiddler(\n\t\tthis.wiki.getCreationFields(),\n\t\tthis.wiki.getModificationFields(),\n\t\t{\n\t\t\ttitle: excisionTitle,\n\t\t\ttext: operation.selection,\n\t\t\ttags: event.paramObject.tagnew === \"yes\" ? [editTiddlerTitle] : []\n\t\t}\n\t));\n\toperation.replacement = excisionTitle;\n\tswitch(event.paramObject.type || \"transclude\") {\n\t\tcase \"transclude\":\n\t\t\toperation.replacement = \"{{\" + operation.replacement+ \"}}\";\n\t\t\tbreak;\n\t\tcase \"link\":\n\t\t\toperation.replacement = \"[[\" + operation.replacement+ \"]]\";\n\t\t\tbreak;\n\t\tcase \"macro\":\n\t\t\toperation.replacement = \"<<\" + (event.paramObject.macro || \"translink\") + \" \\\"\\\"\\\"\" + operation.replacement + \"\\\"\\\"\\\">>\";\n\t\t\tbreak;\n\t}\n\toperation.cutStart = operation.selStart;\n\toperation.cutEnd = operation.selEnd;\n\toperation.newSelStart = operation.selStart;\n\toperation.newSelEnd = operation.selStart + operation.replacement.length;\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/text/excise.js",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/make-link.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/make-link.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to make a link\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"make-link\"] = function(event,operation) {\n\tif(operation.selection) {\n\t\toperation.replacement = \"[[\" + operation.selection + \"|\" + event.paramObject.text + \"]]\";\n\t\toperation.cutStart = operation.selStart;\n\t\toperation.cutEnd = operation.selEnd;\n\t} else {\n\t\toperation.replacement = \"[[\" + event.paramObject.text + \"]]\";\n\t\toperation.cutStart = operation.selStart;\n\t\toperation.cutEnd = operation.selEnd;\n\t}\n\toperation.newSelStart = operation.selStart + operation.replacement.length;\n\toperation.newSelEnd = operation.newSelStart;\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/text/make-link.js",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/prefix-lines.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/prefix-lines.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to add a prefix to the selected lines\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"prefix-lines\"] = function(event,operation) {\n\t// Cut just past the preceding line break, or the start of the text\n\toperation.cutStart = $tw.utils.findPrecedingLineBreak(operation.text,operation.selStart);\n\t// Cut to just past the following line break, or to the end of the text\n\toperation.cutEnd = $tw.utils.findFollowingLineBreak(operation.text,operation.selEnd);\n\t// Compose the required prefix\n\tvar prefix = $tw.utils.repeat(event.paramObject.character,event.paramObject.count);\n\t// Process each line\n\tvar lines = operation.text.substring(operation.cutStart,operation.cutEnd).split(/\\r?\\n/mg);\n\t$tw.utils.each(lines,function(line,index) {\n\t\t// Remove and count any existing prefix characters\n\t\tvar count = 0;\n\t\twhile(line.charAt(0) === event.paramObject.character) {\n\t\t\tline = line.substring(1);\n\t\t\tcount++;\n\t\t}\n\t\t// Remove any whitespace\n\t\twhile(line.charAt(0) === \" \") {\n\t\t\tline = line.substring(1);\n\t\t}\n\t\t// We're done if we removed the exact required prefix, otherwise add it\n\t\tif(count !== event.paramObject.count) {\n\t\t\t// Apply the prefix\n\t\t\tline = prefix + \" \" + line;\n\t\t}\n\t\t// Save the modified line\n\t\tlines[index] = line;\n\t});\n\t// Stitch the replacement text together and set the selection\n\toperation.replacement = lines.join(\"\\n\");\n\tif(lines.length === 1) {\n\t\toperation.newSelStart = operation.cutStart + operation.replacement.length;\n\t\toperation.newSelEnd = operation.newSelStart;\n\t} else {\n\t\toperation.newSelStart = operation.cutStart;\n\t\toperation.newSelEnd = operation.newSelStart + operation.replacement.length;\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/text/prefix-lines.js",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/replace-all.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/replace-all.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to replace the entire text\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"replace-all\"] = function(event,operation) {\n\toperation.cutStart = 0;\n\toperation.cutEnd = operation.text.length;\n\toperation.replacement = event.paramObject.text;\n\toperation.newSelStart = 0;\n\toperation.newSelEnd = operation.replacement.length;\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/text/replace-all.js",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/replace-selection.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/replace-selection.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to replace the selection\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"replace-selection\"] = function(event,operation) {\n\toperation.replacement = event.paramObject.text;\n\toperation.cutStart = operation.selStart;\n\toperation.cutEnd = operation.selEnd;\n\toperation.newSelStart = operation.selStart;\n\toperation.newSelEnd = operation.selStart + operation.replacement.length;\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/text/replace-selection.js",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/wrap-lines.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/wrap-lines.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to wrap the selected lines with a prefix and suffix\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"wrap-lines\"] = function(event,operation) {\n\t// Cut just past the preceding line break, or the start of the text\n\toperation.cutStart = $tw.utils.findPrecedingLineBreak(operation.text,operation.selStart);\n\t// Cut to just past the following line break, or to the end of the text\n\toperation.cutEnd = $tw.utils.findFollowingLineBreak(operation.text,operation.selEnd);\n\t// Add the prefix and suffix\n\toperation.replacement = event.paramObject.prefix + \"\\n\" +\n\t\t\t\toperation.text.substring(operation.cutStart,operation.cutEnd) + \"\\n\" +\n\t\t\t\tevent.paramObject.suffix + \"\\n\";\n\toperation.newSelStart = operation.cutStart + event.paramObject.prefix.length + 1;\n\toperation.newSelEnd = operation.newSelStart + (operation.cutEnd - operation.cutStart);\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/text/wrap-lines.js",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/editor/operations/text/wrap-selection.js": {
"text": "/*\\\ntitle: $:/core/modules/editor/operations/text/wrap-selection.js\ntype: application/javascript\nmodule-type: texteditoroperation\n\nText editor operation to wrap the selection with the specified prefix and suffix\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports[\"wrap-selection\"] = function(event,operation) {\n\tif(operation.selStart === operation.selEnd) {\n\t\t// No selection; check if we're within the prefix/suffix\n\t\tif(operation.text.substring(operation.selStart - event.paramObject.prefix.length,operation.selStart + event.paramObject.suffix.length) === event.paramObject.prefix + event.paramObject.suffix) {\n\t\t\t// Remove the prefix and suffix unless they comprise the entire text\n\t\t\tif(operation.selStart > event.paramObject.prefix.length || (operation.selEnd + event.paramObject.suffix.length) < operation.text.length ) {\n\t\t\t\toperation.cutStart = operation.selStart - event.paramObject.prefix.length;\n\t\t\t\toperation.cutEnd = operation.selEnd + event.paramObject.suffix.length;\n\t\t\t\toperation.replacement = \"\";\n\t\t\t\toperation.newSelStart = operation.cutStart;\n\t\t\t\toperation.newSelEnd = operation.newSelStart;\n\t\t\t}\n\t\t} else {\n\t\t\t// Wrap the cursor instead\n\t\t\toperation.cutStart = operation.selStart;\n\t\t\toperation.cutEnd = operation.selEnd;\n\t\t\toperation.replacement = event.paramObject.prefix + event.paramObject.suffix;\n\t\t\toperation.newSelStart = operation.selStart + event.paramObject.prefix.length;\n\t\t\toperation.newSelEnd = operation.newSelStart;\n\t\t}\n\t} else if(operation.text.substring(operation.selStart,operation.selStart + event.paramObject.prefix.length) === event.paramObject.prefix && operation.text.substring(operation.selEnd - event.paramObject.suffix.length,operation.selEnd) === event.paramObject.suffix) {\n\t\t// Prefix and suffix are already present, so remove them\n\t\toperation.cutStart = operation.selStart;\n\t\toperation.cutEnd = operation.selEnd;\n\t\toperation.replacement = operation.selection.substring(event.paramObject.prefix.length,operation.selection.length - event.paramObject.suffix.length);\n\t\toperation.newSelStart = operation.selStart;\n\t\toperation.newSelEnd = operation.selStart + operation.replacement.length;\n\t} else {\n\t\t// Add the prefix and suffix\n\t\toperation.cutStart = operation.selStart;\n\t\toperation.cutEnd = operation.selEnd;\n\t\toperation.replacement = event.paramObject.prefix + operation.selection + event.paramObject.suffix;\n\t\toperation.newSelStart = operation.selStart;\n\t\toperation.newSelEnd = operation.selStart + operation.replacement.length;\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/editor/operations/text/wrap-selection.js",
"type": "application/javascript",
"module-type": "texteditoroperation"
},
"$:/core/modules/filters/addprefix.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/addprefix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for adding a prefix to each title in the list. This is\nespecially useful in contexts where only a filter expression is allowed\nand macro substitution isn't available.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.addprefix = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(operator.operand + title);\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/addprefix.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/addsuffix.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/addsuffix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for adding a suffix to each title in the list. This is\nespecially useful in contexts where only a filter expression is allowed\nand macro substitution isn't available.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.addsuffix = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title + operator.operand);\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/addsuffix.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/after.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/after.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddler from the current list that is after the tiddler named in the operand.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.after = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\tvar index = results.indexOf(operator.operand);\n\tif(index === -1 || index > (results.length - 2)) {\n\t\treturn [];\n\t} else {\n\t\treturn [results[index + 1]];\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/filters/after.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/all/current.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/all/current.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[current]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.current = function(source,prefix,options) {\n\tvar currTiddlerTitle = options.widget && options.widget.getVariable(\"currentTiddler\");\n\tif(currTiddlerTitle) {\n\t\treturn [currTiddlerTitle];\n\t} else {\n\t\treturn [];\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/filters/all/current.js",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/missing.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/all/missing.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[missing]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.missing = function(source,prefix,options) {\n\treturn options.wiki.getMissingTitles();\n};\n\n})();\n",
"title": "$:/core/modules/filters/all/missing.js",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/orphans.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/all/orphans.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[orphans]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.orphans = function(source,prefix,options) {\n\treturn options.wiki.getOrphanTitles();\n};\n\n})();\n",
"title": "$:/core/modules/filters/all/orphans.js",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/shadows.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/all/shadows.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[shadows]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.shadows = function(source,prefix,options) {\n\treturn options.wiki.allShadowTitles();\n};\n\n})();\n",
"title": "$:/core/modules/filters/all/shadows.js",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/tags.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/all/tags.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[tags]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tags = function(source,prefix,options) {\n\treturn Object.keys(options.wiki.getTagMap());\n};\n\n})();\n",
"title": "$:/core/modules/filters/all/tags.js",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all/tiddlers.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/all/tiddlers.js\ntype: application/javascript\nmodule-type: allfilteroperator\n\nFilter function for [all[tiddlers]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tiddlers = function(source,prefix,options) {\n\treturn options.wiki.allTitles();\n};\n\n})();\n",
"title": "$:/core/modules/filters/all/tiddlers.js",
"type": "application/javascript",
"module-type": "allfilteroperator"
},
"$:/core/modules/filters/all.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/all.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for selecting tiddlers\n\n[all[shadows+tiddlers]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar allFilterOperators;\n\nfunction getAllFilterOperators() {\n\tif(!allFilterOperators) {\n\t\tallFilterOperators = {};\n\t\t$tw.modules.applyMethods(\"allfilteroperator\",allFilterOperators);\n\t}\n\treturn allFilterOperators;\n}\n\n/*\nExport our filter function\n*/\nexports.all = function(source,operator,options) {\n\t// Get our suboperators\n\tvar allFilterOperators = getAllFilterOperators();\n\t// Cycle through the suboperators accumulating their results\n\tvar results = [],\n\t\tsubops = operator.operand.split(\"+\");\n\t// Check for common optimisations\n\tif(subops.length === 1 && subops[0] === \"\") {\n\t\treturn source;\n\t} else if(subops.length === 1 && subops[0] === \"tiddlers\") {\n\t\treturn options.wiki.each;\n\t} else if(subops.length === 1 && subops[0] === \"shadows\") {\n\t\treturn options.wiki.eachShadow;\n\t} else if(subops.length === 2 && subops[0] === \"tiddlers\" && subops[1] === \"shadows\") {\n\t\treturn options.wiki.eachTiddlerPlusShadows;\n\t} else if(subops.length === 2 && subops[0] === \"shadows\" && subops[1] === \"tiddlers\") {\n\t\treturn options.wiki.eachShadowPlusTiddlers;\n\t}\n\t// Do it the hard way\n\tfor(var t=0; t<subops.length; t++) {\n\t\tvar subop = allFilterOperators[subops[t]];\n\t\tif(subop) {\n\t\t\t$tw.utils.pushTop(results,subop(source,operator.prefix,options));\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/all.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/backlinks.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/backlinks.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning all the backlinks from a tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.backlinks = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.pushTop(results,options.wiki.getTiddlerBacklinks(title));\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/backlinks.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/before.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/before.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddler from the current list that is before the tiddler named in the operand.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.before = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\tvar index = results.indexOf(operator.operand);\n\tif(index <= 0) {\n\t\treturn [];\n\t} else {\n\t\treturn [results[index - 1]];\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/filters/before.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/commands.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/commands.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the commands available in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.commands = function(source,operator,options) {\n\tvar results = [];\n\t$tw.utils.each($tw.commands,function(commandInfo,name) {\n\t\tresults.push(name);\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/commands.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/count.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/count.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the number of entries in the current list.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.count = function(source,operator,options) {\n\tvar count = 0;\n\tsource(function(tiddler,title) {\n\t\tcount++;\n\t});\n\treturn [count + \"\"];\n};\n\n})();\n",
"title": "$:/core/modules/filters/count.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/days.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/days.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that selects tiddlers with a specified date field within a specified date interval.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.days = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldName = operator.suffix || \"modified\",\n\t\tdayInterval = (parseInt(operator.operand,10)||0),\n\t\tdayIntervalSign = $tw.utils.sign(dayInterval),\n\t\ttargetTimeStamp = (new Date()).setHours(0,0,0,0) + 1000*60*60*24*dayInterval,\n\t\tisWithinDays = function(dateField) {\n\t\t\tvar sign = $tw.utils.sign(targetTimeStamp - (new Date(dateField)).setHours(0,0,0,0));\n\t\t\treturn sign === 0 || sign === dayIntervalSign;\n\t\t};\n\n\tif(operator.prefix === \"!\") {\n\t\ttargetTimeStamp = targetTimeStamp - 1000*60*60*24*dayIntervalSign;\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler && tiddler.fields[fieldName]) {\n\t\t\t\tif(!isWithinDays($tw.utils.parseDate(tiddler.fields[fieldName]))) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler && tiddler.fields[fieldName]) {\n\t\t\t\tif(isWithinDays($tw.utils.parseDate(tiddler.fields[fieldName]))) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/days.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/each.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/each.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that selects one tiddler for each unique value of the specified field.\nWith suffix \"list\", selects all tiddlers that are values in a specified list field.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.each = function(source,operator,options) {\n\tvar results =[] ,\n\t\tvalue,values = {},\n\t\tfield = operator.operand || \"title\";\n\tif(operator.suffix !== \"list-item\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler) {\n\t\t\t\tvalue = (field === \"title\") ? title : tiddler.getFieldString(field);\n\t\t\t\tif(!$tw.utils.hop(values,value)) {\n\t\t\t\t\tvalues[value] = true;\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler) {\n\t\t\t\t$tw.utils.each(\n\t\t\t\t\toptions.wiki.getTiddlerList(title,field),\n\t\t\t\t\tfunction(value) {\n\t\t\t\t\t\tif(!$tw.utils.hop(values,value)) {\n\t\t\t\t\t\t\tvalues[value] = true;\n\t\t\t\t\t\t\tresults.push(value);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/each.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/eachday.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/eachday.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that selects one tiddler for each unique day covered by the specified date field\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.eachday = function(source,operator,options) {\n\tvar results = [],\n\t\tvalues = [],\n\t\tfieldName = operator.operand || \"modified\";\n\t// Function to convert a date/time to a date integer\n\tvar toDate = function(value) {\n\t\tvalue = (new Date(value)).setHours(0,0,0,0);\n\t\treturn value+0;\n\t};\n\tsource(function(tiddler,title) {\n\t\tif(tiddler && tiddler.fields[fieldName]) {\n\t\t\tvar value = toDate($tw.utils.parseDate(tiddler.fields[fieldName]));\n\t\t\tif(values.indexOf(value) === -1) {\n\t\t\t\tvalues.push(value);\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/eachday.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/editiondescription.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/editiondescription.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the descriptions of the specified edition names\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.editiondescription = function(source,operator,options) {\n\tvar results = [],\n\t\teditionInfo = $tw.utils.getEditionInfo();\n\tif(editionInfo) {\n\t\tsource(function(tiddler,title) {\n\t\t\tif($tw.utils.hop(editionInfo,title)) {\n\t\t\t\tresults.push(editionInfo[title].description || \"\");\t\t\t\t\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/editiondescription.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/editions.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/editions.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the available editions in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.editions = function(source,operator,options) {\n\tvar results = [],\n\t\teditionInfo = $tw.utils.getEditionInfo();\n\tif(editionInfo) {\n\t\t$tw.utils.each(editionInfo,function(info,name) {\n\t\t\tresults.push(name);\n\t\t});\n\t}\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/editions.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/decodeuricomponent.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/decodeuricomponent.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for applying decodeURIComponent() to each item.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter functions\n*/\n\nexports.decodeuricomponent = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(decodeURIComponent(title));\n\t});\n\treturn results;\n};\n\nexports.encodeuricomponent = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(encodeURIComponent(title));\n\t});\n\treturn results;\n};\n\nexports.decodeuri = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(decodeURI(title));\n\t});\n\treturn results;\n};\n\nexports.encodeuri = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(encodeURI(title));\n\t});\n\treturn results;\n};\n\nexports.decodehtml = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.htmlDecode(title));\n\t});\n\treturn results;\n};\n\nexports.encodehtml = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.htmlEncode(title));\n\t});\n\treturn results;\n};\n\nexports.stringify = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.stringify(title));\n\t});\n\treturn results;\n};\n\nexports.escaperegexp = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push($tw.utils.escapeRegExp(title));\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/decodeuricomponent.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/enlist.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/enlist.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning its operand parsed as a list\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.enlist = function(source,operator,options) {\n\tvar list = $tw.utils.parseStringArray(operator.operand);\n\tif(operator.prefix === \"!\") {\n\t\tvar results = [];\n\t\tsource(function(tiddler,title) {\n\t\t\tif(list.indexOf(title) === -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t\treturn results;\n\t} else {\n\t\treturn list;\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/filters/enlist.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/field.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/field.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for comparing fields for equality\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.field = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldname = (operator.suffix || operator.operator || \"title\").toLowerCase();\n\tif(operator.prefix === \"!\") {\n\t\tif(operator.regexp) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvar text = tiddler.getFieldString(fieldname);\n\t\t\t\t\tif(text !== null && !operator.regexp.exec(text)) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvar text = tiddler.getFieldString(fieldname);\n\t\t\t\t\tif(text !== null && text !== operator.operand) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t} else {\n\t\tif(operator.regexp) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvar text = tiddler.getFieldString(fieldname);\n\t\t\t\t\tif(text !== null && !!operator.regexp.exec(text)) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler) {\n\t\t\t\t\tvar text = tiddler.getFieldString(fieldname);\n\t\t\t\t\tif(text !== null && text === operator.operand) {\n\t\t\t\t\t\tresults.push(title);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/field.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/fields.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/fields.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the fields on the selected tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.fields = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tif(tiddler) {\n\t\t\tfor(var fieldName in tiddler.fields) {\n\t\t\t\t$tw.utils.pushTop(results,fieldName);\n\t\t\t}\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/fields.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/get.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/get.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for replacing tiddler titles by the value of the field specified in the operand.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.get = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tif(tiddler) {\n\t\t\tvar value = tiddler.getFieldString(operator.operand);\n\t\t\tif(value) {\n\t\t\t\tresults.push(value);\n\t\t\t}\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/get.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/getindex.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/getindex.js\ntype: application/javascript\nmodule-type: filteroperator\n\nreturns the value at a given index of datatiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.getindex = function(source,operator,options) {\n\tvar data,title,results = [];\n\tif(operator.operand){\n\t\tsource(function(tiddler,title) {\n\t\t\ttitle = tiddler ? tiddler.fields.title : title;\n\t\t\tdata = options.wiki.extractTiddlerDataItem(tiddler,operator.operand);\n\t\t\tif(data) {\n\t\t\t\tresults.push(data);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/getindex.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/has.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/has.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking if a tiddler has the specified field\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.has = function(source,operator,options) {\n\tvar results = [],\n\t\tinvert = operator.prefix === \"!\";\n\n\tif(operator.suffix === \"field\") {\n\t\tif(invert) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(!tiddler || (tiddler && (!$tw.utils.hop(tiddler.fields,operator.operand)))) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler && $tw.utils.hop(tiddler.fields,operator.operand)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t} else {\n\t\tif(invert) {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(!tiddler || !$tw.utils.hop(tiddler.fields,operator.operand) || (tiddler.fields[operator.operand] === \"\")) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler && $tw.utils.hop(tiddler.fields,operator.operand) && !(tiddler.fields[operator.operand] === \"\" || tiddler.fields[operator.operand].length === 0)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/has.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/haschanged.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/haschanged.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returns tiddlers from the list that have a non-zero changecount.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.haschanged = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.getChangeCount(title) === 0) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.getChangeCount(title) > 0) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/haschanged.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/indexes.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/indexes.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the indexes of a data tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.indexes = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar data = options.wiki.getTiddlerDataCached(title);\n\t\tif(data) {\n\t\t\t$tw.utils.pushTop(results,Object.keys(data));\n\t\t}\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/indexes.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/insertbefore.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/insertbefore.js\ntype: application/javascript\nmodule-type: filteroperator\n\nInsert an item before another item in a list\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nOrder a list\n*/\nexports.insertbefore = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\tvar target = options.widget && options.widget.getVariable(operator.suffix || \"currentTiddler\");\n\tif(target !== operator.operand) {\n\t\t// Remove the entry from the list if it is present\n\t\tvar pos = results.indexOf(operator.operand);\n\t\tif(pos !== -1) {\n\t\t\tresults.splice(pos,1);\n\t\t}\n\t\t// Insert the entry before the target marker\n\t\tpos = results.indexOf(target);\n\t\tif(pos !== -1) {\n\t\t\tresults.splice(pos,0,operator.operand);\n\t\t} else {\n\t\t\tresults.push(operator.operand);\n\t\t}\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/insertbefore.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/is/current.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is/current.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[current]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.current = function(source,prefix,options) {\n\tvar results = [],\n\t\tcurrTiddlerTitle = options.widget && options.widget.getVariable(\"currentTiddler\");\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title !== currTiddlerTitle) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title === currTiddlerTitle) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/is/current.js",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/image.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is/image.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[image]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.image = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.isImageTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.isImageTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/is/image.js",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/missing.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is/missing.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[missing]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.missing = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.tiddlerExists(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.tiddlerExists(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/is/missing.js",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/orphan.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is/orphan.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[orphan]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.orphan = function(source,prefix,options) {\n\tvar results = [],\n\t\torphanTitles = options.wiki.getOrphanTitles();\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(orphanTitles.indexOf(title) === -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(orphanTitles.indexOf(title) !== -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/is/orphan.js",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/shadow.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is/shadow.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[shadow]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.shadow = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.isShadowTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.isShadowTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/is/shadow.js",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/system.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is/system.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[system]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.system = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.isSystemTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.isSystemTiddler(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/is/system.js",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/tag.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is/tag.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[tag]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tag = function(source,prefix,options) {\n\tvar results = [],\n\t\ttagMap = options.wiki.getTagMap();\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!$tw.utils.hop(tagMap,title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif($tw.utils.hop(tagMap,title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/is/tag.js",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is/tiddler.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is/tiddler.js\ntype: application/javascript\nmodule-type: isfilteroperator\n\nFilter function for [is[tiddler]]\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tiddler = function(source,prefix,options) {\n\tvar results = [];\n\tif(prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!options.wiki.tiddlerExists(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(options.wiki.tiddlerExists(title)) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/is/tiddler.js",
"type": "application/javascript",
"module-type": "isfilteroperator"
},
"$:/core/modules/filters/is.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/is.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking tiddler properties\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar isFilterOperators;\n\nfunction getIsFilterOperators() {\n\tif(!isFilterOperators) {\n\t\tisFilterOperators = {};\n\t\t$tw.modules.applyMethods(\"isfilteroperator\",isFilterOperators);\n\t}\n\treturn isFilterOperators;\n}\n\n/*\nExport our filter function\n*/\nexports.is = function(source,operator,options) {\n\t// Dispatch to the correct isfilteroperator\n\tvar isFilterOperators = getIsFilterOperators();\n\tif(operator.operand) {\n\t\tvar isFilterOperator = isFilterOperators[operator.operand];\n\t\tif(isFilterOperator) {\n\t\t\treturn isFilterOperator(source,operator.prefix,options);\n\t\t} else {\n\t\t\treturn [$tw.language.getString(\"Error/IsFilterOperator\")];\n\t\t}\n\t} else {\n\t\t// Return all tiddlers if the operand is missing\n\t\tvar results = [];\n\t\tsource(function(tiddler,title) {\n\t\t\tresults.push(title);\n\t\t});\n\t\treturn results;\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/filters/is.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/limit.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/limit.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for chopping the results to a specified maximum number of entries\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.limit = function(source,operator,options) {\n\tvar results = [];\n\t// Convert to an array\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\t// Slice the array if necessary\n\tvar limit = Math.min(results.length,parseInt(operator.operand,10));\n\tif(operator.prefix === \"!\") {\n\t\tresults = results.slice(-limit);\n\t} else {\n\t\tresults = results.slice(0,limit);\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/limit.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/links.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/links.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning all the links from a tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.links = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.pushTop(results,options.wiki.getTiddlerLinks(title));\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/links.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/list.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/list.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddlers whose title is listed in the operand tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.list = function(source,operator,options) {\n\tvar results = [],\n\t\ttr = $tw.utils.parseTextReference(operator.operand),\n\t\tcurrTiddlerTitle = options.widget && options.widget.getVariable(\"currentTiddler\"),\n\t\tlist = options.wiki.getTiddlerList(tr.title || currTiddlerTitle,tr.field,tr.index);\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(list.indexOf(title) === -1) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tresults = list;\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/list.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/listed.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/listed.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning all tiddlers that have the selected tiddlers in a list\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.listed = function(source,operator,options) {\n\tvar field = operator.operand || \"list\",\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.pushTop(results,options.wiki.findListingsOfTiddler(title,field));\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/listed.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/listops.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/listops.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operators for manipulating the current selection list\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nOrder a list\n*/\nexports.order = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.operand.toLowerCase() === \"reverse\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tresults.unshift(title);\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tresults.push(title);\n\t\t});\t\t\n\t}\n\treturn results;\n};\n\n/*\nReverse list\n*/\nexports.reverse = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.unshift(title);\n\t});\n\treturn results;\n};\n\n/*\nFirst entry/entries in list\n*/\nexports.first = function(source,operator,options) {\n\tvar count = parseInt(operator.operand) || 1,\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(0,count);\n};\n\n/*\nLast entry/entries in list\n*/\nexports.last = function(source,operator,options) {\n\tvar count = parseInt(operator.operand) || 1,\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(-count);\n};\n\n/*\nAll but the first entry/entries of the list\n*/\nexports.rest = function(source,operator,options) {\n\tvar count = parseInt(operator.operand) || 1,\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(count);\n};\nexports.butfirst = exports.rest;\nexports.bf = exports.rest;\n\n/*\nAll but the last entry/entries of the list\n*/\nexports.butlast = function(source,operator,options) {\n\tvar count = parseInt(operator.operand) || 1,\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(0,-count);\n};\nexports.bl = exports.butlast;\n\n/*\nThe nth member of the list\n*/\nexports.nth = function(source,operator,options) {\n\tvar count = parseInt(operator.operand) || 1,\n\t\tresults = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results.slice(count - 1,count);\n};\n\n})();\n",
"title": "$:/core/modules/filters/listops.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/minlength.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/minlength.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for filtering out titles that don't meet the minimum length in the operand\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.minlength = function(source,operator,options) {\n\tvar results = [],\n\t\tminLength = parseInt(operator.operand || \"\",10) || 0;\n\tsource(function(tiddler,title) {\n\t\tif(title.length >= minLength) {\n\t\t\tresults.push(title);\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/minlength.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/modules.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/modules.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the titles of the modules of a given type in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.modules = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.each($tw.modules.types[title],function(moduleInfo,moduleName) {\n\t\t\tresults.push(moduleName);\n\t\t});\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/modules.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/moduletypes.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/moduletypes.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the module types in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.moduletypes = function(source,operator,options) {\n\tvar results = [];\n\t$tw.utils.each($tw.modules.types,function(moduleInfo,type) {\n\t\tresults.push(type);\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/moduletypes.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/next.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/next.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddler whose title occurs next in the list supplied in the operand tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.next = function(source,operator,options) {\n\tvar results = [],\n\t\tlist = options.wiki.getTiddlerList(operator.operand);\n\tsource(function(tiddler,title) {\n\t\tvar match = list.indexOf(title);\n\t\t// increment match and then test if result is in range\n\t\tmatch++;\n\t\tif(match > 0 && match < list.length) {\n\t\t\tresults.push(list[match]);\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/next.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/plugintiddlers.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/plugintiddlers.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the titles of the shadow tiddlers within a plugin\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.plugintiddlers = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar pluginInfo = options.wiki.getPluginInfo(title) || options.wiki.getTiddlerDataCached(title,{tiddlers:[]});\n\t\tif(pluginInfo && pluginInfo.tiddlers) {\n\t\t\t$tw.utils.each(pluginInfo.tiddlers,function(fields,title) {\n\t\t\t\tresults.push(title);\n\t\t\t});\n\t\t}\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/plugintiddlers.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/prefix.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/prefix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking if a title starts with a prefix\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.prefix = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title.substr(0,operator.operand.length) !== operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title.substr(0,operator.operand.length) === operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/prefix.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/previous.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/previous.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning the tiddler whose title occurs immediately prior in the list supplied in the operand tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.previous = function(source,operator,options) {\n\tvar results = [],\n\t\tlist = options.wiki.getTiddlerList(operator.operand);\n\tsource(function(tiddler,title) {\n\t\tvar match = list.indexOf(title);\n\t\t// increment match and then test if result is in range\n\t\tmatch--;\n\t\tif(match >= 0) {\n\t\t\tresults.push(list[match]);\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/previous.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/regexp.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/regexp.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for regexp matching\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.regexp = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldname = (operator.suffix || \"title\").toLowerCase(),\n\t\tregexpString, regexp, flags = \"\", match,\n\t\tgetFieldString = function(tiddler,title) {\n\t\t\tif(tiddler) {\n\t\t\t\treturn tiddler.getFieldString(fieldname);\n\t\t\t} else if(fieldname === \"title\") {\n\t\t\t\treturn title;\n\t\t\t} else {\n\t\t\t\treturn null;\n\t\t\t}\n\t\t};\n\t// Process flags and construct regexp\n\tregexpString = operator.operand;\n\tmatch = /^\\(\\?([gim]+)\\)/.exec(regexpString);\n\tif(match) {\n\t\tflags = match[1];\n\t\tregexpString = regexpString.substr(match[0].length);\n\t} else {\n\t\tmatch = /\\(\\?([gim]+)\\)$/.exec(regexpString);\n\t\tif(match) {\n\t\t\tflags = match[1];\n\t\t\tregexpString = regexpString.substr(0,regexpString.length - match[0].length);\n\t\t}\n\t}\n\ttry {\n\t\tregexp = new RegExp(regexpString,flags);\n\t} catch(e) {\n\t\treturn [\"\" + e];\n\t}\n\t// Process the incoming tiddlers\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tvar text = getFieldString(tiddler,title);\n\t\t\tif(text !== null) {\n\t\t\t\tif(!regexp.exec(text)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tvar text = getFieldString(tiddler,title);\n\t\t\tif(text !== null) {\n\t\t\t\tif(!!regexp.exec(text)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/regexp.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/removeprefix.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/removeprefix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for removing a prefix from each title in the list. Titles that do not start with the prefix are removed.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.removeprefix = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tif(title.substr(0,operator.operand.length) === operator.operand) {\n\t\t\tresults.push(title.substr(operator.operand.length));\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/removeprefix.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/removesuffix.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/removesuffix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for removing a suffix from each title in the list. Titles that do not end with the suffix are removed.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.removesuffix = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tif(title.substr(-operator.operand.length) === operator.operand) {\n\t\t\tresults.push(title.substr(0,title.length - operator.operand.length));\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/removesuffix.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/sameday.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/sameday.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that selects tiddlers with a modified date field on the same day as the provided value.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.sameday = function(source,operator,options) {\n\tvar results = [],\n\t\tfieldName = operator.suffix || \"modified\",\n\t\ttargetDate = (new Date($tw.utils.parseDate(operator.operand))).setHours(0,0,0,0);\n\t// Function to convert a date/time to a date integer\n\tsource(function(tiddler,title) {\n\t\tif(tiddler) {\n\t\t\tif(tiddler.getFieldDay(fieldName) === targetDate) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/sameday.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/search.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/search.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for searching for the text in the operand tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.search = function(source,operator,options) {\n\tvar invert = operator.prefix === \"!\";\n\tif(operator.suffix) {\n\t\treturn options.wiki.search(operator.operand,{\n\t\t\tsource: source,\n\t\t\tinvert: invert,\n\t\t\tfield: operator.suffix\n\t\t});\n\t} else {\n\t\treturn options.wiki.search(operator.operand,{\n\t\t\tsource: source,\n\t\t\tinvert: invert\n\t\t});\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/filters/search.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/shadowsource.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/shadowsource.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the source plugins for shadow tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.shadowsource = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar source = options.wiki.getShadowSource(title);\n\t\tif(source) {\n\t\t\t$tw.utils.pushTop(results,source);\n\t\t}\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/shadowsource.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/sort.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/sort.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for sorting\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.sort = function(source,operator,options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results,operator.operand || \"title\",operator.prefix === \"!\",false,false);\n\treturn results;\n};\n\nexports.nsort = function(source,operator,options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results,operator.operand || \"title\",operator.prefix === \"!\",false,true);\n\treturn results;\n};\n\nexports.sortcs = function(source,operator,options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results,operator.operand || \"title\",operator.prefix === \"!\",true,false);\n\treturn results;\n};\n\nexports.nsortcs = function(source,operator,options) {\n\tvar results = prepare_results(source);\n\toptions.wiki.sortTiddlers(results,operator.operand || \"title\",operator.prefix === \"!\",true,true);\n\treturn results;\n};\n\nvar prepare_results = function (source) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tresults.push(title);\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/sort.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/splitbefore.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/splitbefore.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator that splits each result on the first occurance of the specified separator and returns the unique values.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.splitbefore = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\tvar parts = title.split(operator.operand);\n\t\tif(parts.length === 1) {\n\t\t\t$tw.utils.pushTop(results,parts[0]);\n\t\t} else {\n\t\t\t$tw.utils.pushTop(results,parts[0] + operator.operand);\n\t\t}\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/splitbefore.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/storyviews.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/storyviews.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the story views in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.storyviews = function(source,operator,options) {\n\tvar results = [],\n\t\tstoryviews = {};\n\t$tw.modules.applyMethods(\"storyview\",storyviews);\n\t$tw.utils.each(storyviews,function(info,name) {\n\t\tresults.push(name);\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/storyviews.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/suffix.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/suffix.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking if a title ends with a suffix\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.suffix = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title.substr(-operator.operand.length) !== operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(title.substr(-operator.operand.length) === operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/suffix.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/tag.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/tag.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for checking for the presence of a tag\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tag = function(source,operator,options) {\n\tvar results = [];\n\tif((operator.suffix || \"\").toLowerCase() === \"strict\" && !operator.operand) {\n\t\t// New semantics:\n\t\t// Always return copy of input if operator.operand is missing\n\t\tsource(function(tiddler,title) {\n\t\t\tresults.push(title);\n\t\t});\n\t} else {\n\t\t// Old semantics:\n\t\tif(operator.prefix === \"!\") {\n\t\t\t// Returns a copy of the input if operator.operand is missing\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler && !tiddler.hasTag(operator.operand)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t} else {\n\t\t\t// Returns empty results if operator.operand is missing\n\t\t\tsource(function(tiddler,title) {\n\t\t\t\tif(tiddler && tiddler.hasTag(operator.operand)) {\n\t\t\t\t\tresults.push(title);\n\t\t\t\t}\n\t\t\t});\n\t\t\tresults = options.wiki.sortByList(results,operator.operand);\n\t\t}\t\t\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/tag.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/tagging.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/tagging.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning all tiddlers that are tagged with the selected tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tagging = function(source,operator,options) {\n\tvar results = [];\n\tsource(function(tiddler,title) {\n\t\t$tw.utils.pushTop(results,options.wiki.getTiddlersWithTag(title));\n\t});\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/tagging.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/tags.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/tags.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning all the tags of the selected tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.tags = function(source,operator,options) {\n\tvar tags = {};\n\tsource(function(tiddler,title) {\n\t\tvar t, length;\n\t\tif(tiddler && tiddler.fields.tags) {\n\t\t\tfor(t=0, length=tiddler.fields.tags.length; t<length; t++) {\n\t\t\t\ttags[tiddler.fields.tags[t]] = true;\n\t\t\t}\n\t\t}\n\t});\n\treturn Object.keys(tags);\n};\n\n})();\n",
"title": "$:/core/modules/filters/tags.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/title.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/title.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for comparing title fields for equality\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.title = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler && tiddler.fields.title !== operator.operand) {\n\t\t\t\tresults.push(title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tresults.push(operator.operand);\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/title.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/untagged.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/untagged.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator returning all the selected tiddlers that are untagged\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.untagged = function(source,operator,options) {\n\tvar results = [];\n\tif(operator.prefix === \"!\") {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(tiddler && $tw.utils.isArray(tiddler.fields.tags) && tiddler.fields.tags.length > 0) {\n\t\t\t\t$tw.utils.pushTop(results,title);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tsource(function(tiddler,title) {\n\t\t\tif(!tiddler || !tiddler.hasField(\"tags\") || ($tw.utils.isArray(tiddler.fields.tags) && tiddler.fields.tags.length === 0)) {\n\t\t\t\t$tw.utils.pushTop(results,title);\n\t\t\t}\n\t\t});\n\t}\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/untagged.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/wikiparserrules.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/wikiparserrules.js\ntype: application/javascript\nmodule-type: filteroperator\n\nFilter operator for returning the names of the wiki parser rules in this wiki\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nExport our filter function\n*/\nexports.wikiparserrules = function(source,operator,options) {\n\tvar results = [],\n\t\toperand = operator.operand;\n\t$tw.utils.each($tw.modules.types.wikirule,function(mod) {\n\t\tvar exp = mod.exports;\n\t\tif(!operand || exp.types[operand]) {\n\t\t\tresults.push(exp.name);\n\t\t}\n\t});\n\tresults.sort();\n\treturn results;\n};\n\n})();\n",
"title": "$:/core/modules/filters/wikiparserrules.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters/x-listops.js": {
"text": "/*\\\ntitle: $:/core/modules/filters/x-listops.js\ntype: application/javascript\nmodule-type: filteroperator\n\nExtended filter operators to manipulate the current list.\n\n\\*/\n(function () {\n\n /*jslint node: true, browser: true */\n /*global $tw: false */\n \"use strict\";\n\n /*\n Fetch titles from the current list\n */\n var prepare_results = function (source) {\n var results = [];\n source(function (tiddler, title) {\n results.push(title);\n });\n return results;\n };\n\n /*\n Moves a number of items from the tail of the current list before the item named in the operand\n */\n exports.putbefore = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand),\n count = parseInt(operator.suffix) || 1;\n return (index === -1) ?\n results.slice(0, -1) :\n results.slice(0, index).concat(results.slice(-count)).concat(results.slice(index, -count));\n };\n\n /*\n Moves a number of items from the tail of the current list after the item named in the operand\n */\n exports.putafter = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand),\n count = parseInt(operator.suffix) || 1;\n return (index === -1) ?\n results.slice(0, -1) :\n results.slice(0, index + 1).concat(results.slice(-count)).concat(results.slice(index + 1, -count));\n };\n\n /*\n Replaces the item named in the operand with a number of items from the tail of the current list\n */\n exports.replace = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand),\n count = parseInt(operator.suffix) || 1;\n return (index === -1) ?\n results.slice(0, -count) :\n results.slice(0, index).concat(results.slice(-count)).concat(results.slice(index + 1, -count));\n };\n\n /*\n Moves a number of items from the tail of the current list to the head of the list\n */\n exports.putfirst = function (source, operator) {\n var results = prepare_results(source),\n count = parseInt(operator.suffix) || 1;\n return results.slice(-count).concat(results.slice(0, -count));\n };\n\n /*\n Moves a number of items from the head of the current list to the tail of the list\n */\n exports.putlast = function (source, operator) {\n var results = prepare_results(source),\n count = parseInt(operator.suffix) || 1;\n return results.slice(count).concat(results.slice(0, count));\n };\n\n /*\n Moves the item named in the operand a number of places forward or backward in the list\n */\n exports.move = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand),\n count = parseInt(operator.suffix) || 1,\n marker = results.splice(index, 1),\n offset = (index + count) > 0 ? index + count : 0;\n return results.slice(0, offset).concat(marker).concat(results.slice(offset));\n };\n\n /*\n Returns the items from the current list that are after the item named in the operand\n */\n exports.allafter = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand);\n return (index === -1 || index > (results.length - 2)) ? [] :\n (operator.suffix) ? results.slice(index) :\n results.slice(index + 1);\n };\n\n /*\n Returns the items from the current list that are before the item named in the operand\n */\n exports.allbefore = function (source, operator) {\n var results = prepare_results(source),\n index = results.indexOf(operator.operand);\n return (index <= 0) ? [] :\n (operator.suffix) ? results.slice(0, index + 1) :\n results.slice(0, index);\n };\n\n /*\n Appends the items listed in the operand array to the tail of the current list\n */\n exports.append = function (source, operator) {\n var append = $tw.utils.parseStringArray(operator.operand, \"true\"),\n results = prepare_results(source),\n count = parseInt(operator.suffix) || append.length;\n return (append.length === 0) ? results :\n (operator.prefix) ? results.concat(append.slice(-count)) :\n results.concat(append.slice(0, count));\n };\n\n /*\n Prepends the items listed in the operand array to the head of the current list\n */\n exports.prepend = function (source, operator) {\n var prepend = $tw.utils.parseStringArray(operator.operand, \"true\"),\n results = prepare_results(source),\n count = parseInt(operator.suffix) || prepend.length;\n return (prepend.length === 0) ? results :\n (operator.prefix) ? prepend.slice(-count).concat(results) :\n prepend.slice(0, count).concat(results);\n };\n\n /*\n Returns all items from the current list except the items listed in the operand array\n */\n exports.remove = function (source, operator) {\n var array = $tw.utils.parseStringArray(operator.operand, \"true\"),\n results = prepare_results(source),\n count = parseInt(operator.suffix) || array.length,\n p,\n len,\n index;\n len = array.length - 1;\n for (p = 0; p < count; ++p) {\n if (operator.prefix) {\n index = results.indexOf(array[len - p]);\n } else {\n index = results.indexOf(array[p]);\n }\n if (index !== -1) {\n results.splice(index, 1);\n }\n }\n return results;\n };\n\n /*\n Returns all items from the current list sorted in the order of the items in the operand array\n */\n exports.sortby = function (source, operator) {\n var results = prepare_results(source);\n if (!results || results.length < 2) {\n return results;\n }\n var lookup = $tw.utils.parseStringArray(operator.operand, \"true\");\n results.sort(function (a, b) {\n return lookup.indexOf(a) - lookup.indexOf(b);\n });\n return results;\n };\n\n /*\n Removes all duplicate items from the current list\n */\n exports.unique = function (source, operator) {\n var results = prepare_results(source);\n var set = results.reduce(function (a, b) {\n if (a.indexOf(b) < 0) {\n a.push(b);\n }\n return a;\n }, []);\n return set;\n };\n})();\n",
"title": "$:/core/modules/filters/x-listops.js",
"type": "application/javascript",
"module-type": "filteroperator"
},
"$:/core/modules/filters.js": {
"text": "/*\\\ntitle: $:/core/modules/filters.js\ntype: application/javascript\nmodule-type: wikimethod\n\nAdds tiddler filtering methods to the $tw.Wiki object.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nParses an operation (i.e. a run) within a filter string\n\toperators: Array of array of operator nodes into which results should be inserted\n\tfilterString: filter string\n\tp: start position within the string\nReturns the new start position, after the parsed operation\n*/\nfunction parseFilterOperation(operators,filterString,p) {\n\tvar nextBracketPos, operator;\n\t// Skip the starting square bracket\n\tif(filterString.charAt(p++) !== \"[\") {\n\t\tthrow \"Missing [ in filter expression\";\n\t}\n\t// Process each operator in turn\n\tdo {\n\t\toperator = {};\n\t\t// Check for an operator prefix\n\t\tif(filterString.charAt(p) === \"!\") {\n\t\t\toperator.prefix = filterString.charAt(p++);\n\t\t}\n\t\t// Get the operator name\n\t\tnextBracketPos = filterString.substring(p).search(/[\\[\\{<\\/]/);\n\t\tif(nextBracketPos === -1) {\n\t\t\tthrow \"Missing [ in filter expression\";\n\t\t}\n\t\tnextBracketPos += p;\n\t\tvar bracket = filterString.charAt(nextBracketPos);\n\t\toperator.operator = filterString.substring(p,nextBracketPos);\n\n\t\t// Any suffix?\n\t\tvar colon = operator.operator.indexOf(':');\n\t\tif(colon > -1) {\n\t\t\toperator.suffix = operator.operator.substring(colon + 1);\n\t\t\toperator.operator = operator.operator.substring(0,colon) || \"field\";\n\t\t}\n\t\t// Empty operator means: title\n\t\telse if(operator.operator === \"\") {\n\t\t\toperator.operator = \"title\";\n\t\t}\n\n\t\tp = nextBracketPos + 1;\n\t\tswitch (bracket) {\n\t\t\tcase \"{\": // Curly brackets\n\t\t\t\toperator.indirect = true;\n\t\t\t\tnextBracketPos = filterString.indexOf(\"}\",p);\n\t\t\t\tbreak;\n\t\t\tcase \"[\": // Square brackets\n\t\t\t\tnextBracketPos = filterString.indexOf(\"]\",p);\n\t\t\t\tbreak;\n\t\t\tcase \"<\": // Angle brackets\n\t\t\t\toperator.variable = true;\n\t\t\t\tnextBracketPos = filterString.indexOf(\">\",p);\n\t\t\t\tbreak;\n\t\t\tcase \"/\": // regexp brackets\n\t\t\t\tvar rex = /^((?:[^\\\\\\/]*|\\\\.)*)\\/(?:\\(([mygi]+)\\))?/g,\n\t\t\t\t\trexMatch = rex.exec(filterString.substring(p));\n\t\t\t\tif(rexMatch) {\n\t\t\t\t\toperator.regexp = new RegExp(rexMatch[1], rexMatch[2]);\n// DEPRECATION WARNING\nconsole.log(\"WARNING: Filter\",operator.operator,\"has a deprecated regexp operand\",operator.regexp);\n\t\t\t\t\tnextBracketPos = p + rex.lastIndex - 1;\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tthrow \"Unterminated regular expression in filter expression\";\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t}\n\n\t\tif(nextBracketPos === -1) {\n\t\t\tthrow \"Missing closing bracket in filter expression\";\n\t\t}\n\t\tif(!operator.regexp) {\n\t\t\toperator.operand = filterString.substring(p,nextBracketPos);\n\t\t}\n\t\tp = nextBracketPos + 1;\n\n\t\t// Push this operator\n\t\toperators.push(operator);\n\t} while(filterString.charAt(p) !== \"]\");\n\t// Skip the ending square bracket\n\tif(filterString.charAt(p++) !== \"]\") {\n\t\tthrow \"Missing ] in filter expression\";\n\t}\n\t// Return the parsing position\n\treturn p;\n}\n\n/*\nParse a filter string\n*/\nexports.parseFilter = function(filterString) {\n\tfilterString = filterString || \"\";\n\tvar results = [], // Array of arrays of operator nodes {operator:,operand:}\n\t\tp = 0, // Current position in the filter string\n\t\tmatch;\n\tvar whitespaceRegExp = /(\\s+)/mg,\n\t\toperandRegExp = /((?:\\+|\\-)?)(?:(\\[)|(?:\"([^\"]*)\")|(?:'([^']*)')|([^\\s\\[\\]]+))/mg;\n\twhile(p < filterString.length) {\n\t\t// Skip any whitespace\n\t\twhitespaceRegExp.lastIndex = p;\n\t\tmatch = whitespaceRegExp.exec(filterString);\n\t\tif(match && match.index === p) {\n\t\t\tp = p + match[0].length;\n\t\t}\n\t\t// Match the start of the operation\n\t\tif(p < filterString.length) {\n\t\t\toperandRegExp.lastIndex = p;\n\t\t\tmatch = operandRegExp.exec(filterString);\n\t\t\tif(!match || match.index !== p) {\n\t\t\t\tthrow $tw.language.getString(\"Error/FilterSyntax\");\n\t\t\t}\n\t\t\tvar operation = {\n\t\t\t\tprefix: \"\",\n\t\t\t\toperators: []\n\t\t\t};\n\t\t\tif(match[1]) {\n\t\t\t\toperation.prefix = match[1];\n\t\t\t\tp++;\n\t\t\t}\n\t\t\tif(match[2]) { // Opening square bracket\n\t\t\t\tp = parseFilterOperation(operation.operators,filterString,p);\n\t\t\t} else {\n\t\t\t\tp = match.index + match[0].length;\n\t\t\t}\n\t\t\tif(match[3] || match[4] || match[5]) { // Double quoted string, single quoted string or unquoted title\n\t\t\t\toperation.operators.push(\n\t\t\t\t\t{operator: \"title\", operand: match[3] || match[4] || match[5]}\n\t\t\t\t);\n\t\t\t}\n\t\t\tresults.push(operation);\n\t\t}\n\t}\n\treturn results;\n};\n\nexports.getFilterOperators = function() {\n\tif(!this.filterOperators) {\n\t\t$tw.Wiki.prototype.filterOperators = {};\n\t\t$tw.modules.applyMethods(\"filteroperator\",this.filterOperators);\n\t}\n\treturn this.filterOperators;\n};\n\nexports.filterTiddlers = function(filterString,widget,source) {\n\tvar fn = this.compileFilter(filterString);\n\treturn fn.call(this,source,widget);\n};\n\n/*\nCompile a filter into a function with the signature fn(source,widget) where:\nsource: an iterator function for the source tiddlers, called source(iterator), where iterator is called as iterator(tiddler,title)\nwidget: an optional widget node for retrieving the current tiddler etc.\n*/\nexports.compileFilter = function(filterString) {\n\tvar filterParseTree;\n\ttry {\n\t\tfilterParseTree = this.parseFilter(filterString);\n\t} catch(e) {\n\t\treturn function(source,widget) {\n\t\t\treturn [$tw.language.getString(\"Error/Filter\") + \": \" + e];\n\t\t};\n\t}\n\t// Get the hashmap of filter operator functions\n\tvar filterOperators = this.getFilterOperators();\n\t// Assemble array of functions, one for each operation\n\tvar operationFunctions = [];\n\t// Step through the operations\n\tvar self = this;\n\t$tw.utils.each(filterParseTree,function(operation) {\n\t\t// Create a function for the chain of operators in the operation\n\t\tvar operationSubFunction = function(source,widget) {\n\t\t\tvar accumulator = source,\n\t\t\t\tresults = [],\n\t\t\t\tcurrTiddlerTitle = widget && widget.getVariable(\"currentTiddler\");\n\t\t\t$tw.utils.each(operation.operators,function(operator) {\n\t\t\t\tvar operand = operator.operand,\n\t\t\t\t\toperatorFunction;\n\t\t\t\tif(!operator.operator) {\n\t\t\t\t\toperatorFunction = filterOperators.title;\n\t\t\t\t} else if(!filterOperators[operator.operator]) {\n\t\t\t\t\toperatorFunction = filterOperators.field;\n\t\t\t\t} else {\n\t\t\t\t\toperatorFunction = filterOperators[operator.operator];\n\t\t\t\t}\n\t\t\t\tif(operator.indirect) {\n\t\t\t\t\toperand = self.getTextReference(operator.operand,\"\",currTiddlerTitle);\n\t\t\t\t}\n\t\t\t\tif(operator.variable) {\n\t\t\t\t\toperand = widget.getVariable(operator.operand,{defaultValue: \"\"});\n\t\t\t\t}\n\t\t\t\t// Invoke the appropriate filteroperator module\n\t\t\t\tresults = operatorFunction(accumulator,{\n\t\t\t\t\t\t\toperator: operator.operator,\n\t\t\t\t\t\t\toperand: operand,\n\t\t\t\t\t\t\tprefix: operator.prefix,\n\t\t\t\t\t\t\tsuffix: operator.suffix,\n\t\t\t\t\t\t\tregexp: operator.regexp\n\t\t\t\t\t\t},{\n\t\t\t\t\t\t\twiki: self,\n\t\t\t\t\t\t\twidget: widget\n\t\t\t\t\t\t});\n\t\t\t\tif($tw.utils.isArray(results)) {\n\t\t\t\t\taccumulator = self.makeTiddlerIterator(results);\n\t\t\t\t} else {\n\t\t\t\t\taccumulator = results;\n\t\t\t\t}\n\t\t\t});\n\t\t\tif($tw.utils.isArray(results)) {\n\t\t\t\treturn results;\n\t\t\t} else {\n\t\t\t\tvar resultArray = [];\n\t\t\t\tresults(function(tiddler,title) {\n\t\t\t\t\tresultArray.push(title);\n\t\t\t\t});\n\t\t\t\treturn resultArray;\n\t\t\t}\n\t\t};\n\t\t// Wrap the operator functions in a wrapper function that depends on the prefix\n\t\toperationFunctions.push((function() {\n\t\t\tswitch(operation.prefix || \"\") {\n\t\t\t\tcase \"\": // No prefix means that the operation is unioned into the result\n\t\t\t\t\treturn function(results,source,widget) {\n\t\t\t\t\t\t$tw.utils.pushTop(results,operationSubFunction(source,widget));\n\t\t\t\t\t};\n\t\t\t\tcase \"-\": // The results of this operation are removed from the main result\n\t\t\t\t\treturn function(results,source,widget) {\n\t\t\t\t\t\t$tw.utils.removeArrayEntries(results,operationSubFunction(source,widget));\n\t\t\t\t\t};\n\t\t\t\tcase \"+\": // This operation is applied to the main results so far\n\t\t\t\t\treturn function(results,source,widget) {\n\t\t\t\t\t\t// This replaces all the elements of the array, but keeps the actual array so that references to it are preserved\n\t\t\t\t\t\tsource = self.makeTiddlerIterator(results);\n\t\t\t\t\t\tresults.splice(0,results.length);\n\t\t\t\t\t\t$tw.utils.pushTop(results,operationSubFunction(source,widget));\n\t\t\t\t\t};\n\t\t\t}\n\t\t})());\n\t});\n\t// Return a function that applies the operations to a source iterator of tiddler titles\n\treturn $tw.perf.measure(\"filter\",function filterFunction(source,widget) {\n\t\tif(!source) {\n\t\t\tsource = self.each;\n\t\t} else if(typeof source === \"object\") { // Array or hashmap\n\t\t\tsource = self.makeTiddlerIterator(source);\n\t\t}\n\t\tvar results = [];\n\t\t$tw.utils.each(operationFunctions,function(operationFunction) {\n\t\t\toperationFunction(results,source,widget);\n\t\t});\n\t\treturn results;\n\t});\n};\n\n})();\n",
"title": "$:/core/modules/filters.js",
"type": "application/javascript",
"module-type": "wikimethod"
},
"$:/core/modules/info/platform.js": {
"text": "/*\\\ntitle: $:/core/modules/info/platform.js\ntype: application/javascript\nmodule-type: info\n\nInitialise basic platform $:/info/ tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.getInfoTiddlerFields = function() {\n\tvar mapBoolean = function(value) {return value ? \"yes\" : \"no\";},\n\t\tinfoTiddlerFields = [];\n\t// Basics\n\tinfoTiddlerFields.push({title: \"$:/info/browser\", text: mapBoolean(!!$tw.browser)});\n\tinfoTiddlerFields.push({title: \"$:/info/node\", text: mapBoolean(!!$tw.node)});\n\t// Document location\n\tif($tw.browser) {\n\t\tvar setLocationProperty = function(name,value) {\n\t\t\t\tinfoTiddlerFields.push({title: \"$:/info/url/\" + name, text: value});\t\t\t\n\t\t\t},\n\t\t\tlocation = document.location;\n\t\tsetLocationProperty(\"full\", (location.toString()).split(\"#\")[0]);\n\t\tsetLocationProperty(\"host\", location.host);\n\t\tsetLocationProperty(\"hostname\", location.hostname);\n\t\tsetLocationProperty(\"protocol\", location.protocol);\n\t\tsetLocationProperty(\"port\", location.port);\n\t\tsetLocationProperty(\"pathname\", location.pathname);\n\t\tsetLocationProperty(\"search\", location.search);\n\t\tsetLocationProperty(\"origin\", location.origin);\n\t}\n\treturn infoTiddlerFields;\n};\n\n})();\n",
"title": "$:/core/modules/info/platform.js",
"type": "application/javascript",
"module-type": "info"
},
"$:/core/modules/keyboard.js": {
"text": "/*\\\ntitle: $:/core/modules/keyboard.js\ntype: application/javascript\nmodule-type: global\n\nKeyboard handling utilities\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar namedKeys = {\n\t\"cancel\": 3,\n\t\"help\": 6,\n\t\"backspace\": 8,\n\t\"tab\": 9,\n\t\"clear\": 12,\n\t\"return\": 13,\n\t\"enter\": 13,\n\t\"pause\": 19,\n\t\"escape\": 27,\n\t\"space\": 32,\n\t\"page_up\": 33,\n\t\"page_down\": 34,\n\t\"end\": 35,\n\t\"home\": 36,\n\t\"left\": 37,\n\t\"up\": 38,\n\t\"right\": 39,\n\t\"down\": 40,\n\t\"printscreen\": 44,\n\t\"insert\": 45,\n\t\"delete\": 46,\n\t\"0\": 48,\n\t\"1\": 49,\n\t\"2\": 50,\n\t\"3\": 51,\n\t\"4\": 52,\n\t\"5\": 53,\n\t\"6\": 54,\n\t\"7\": 55,\n\t\"8\": 56,\n\t\"9\": 57,\n\t\"firefoxsemicolon\": 59,\n\t\"firefoxequals\": 61,\n\t\"a\": 65,\n\t\"b\": 66,\n\t\"c\": 67,\n\t\"d\": 68,\n\t\"e\": 69,\n\t\"f\": 70,\n\t\"g\": 71,\n\t\"h\": 72,\n\t\"i\": 73,\n\t\"j\": 74,\n\t\"k\": 75,\n\t\"l\": 76,\n\t\"m\": 77,\n\t\"n\": 78,\n\t\"o\": 79,\n\t\"p\": 80,\n\t\"q\": 81,\n\t\"r\": 82,\n\t\"s\": 83,\n\t\"t\": 84,\n\t\"u\": 85,\n\t\"v\": 86,\n\t\"w\": 87,\n\t\"x\": 88,\n\t\"y\": 89,\n\t\"z\": 90,\n\t\"numpad0\": 96,\n\t\"numpad1\": 97,\n\t\"numpad2\": 98,\n\t\"numpad3\": 99,\n\t\"numpad4\": 100,\n\t\"numpad5\": 101,\n\t\"numpad6\": 102,\n\t\"numpad7\": 103,\n\t\"numpad8\": 104,\n\t\"numpad9\": 105,\n\t\"multiply\": 106,\n\t\"add\": 107,\n\t\"separator\": 108,\n\t\"subtract\": 109,\n\t\"decimal\": 110,\n\t\"divide\": 111,\n\t\"f1\": 112,\n\t\"f2\": 113,\n\t\"f3\": 114,\n\t\"f4\": 115,\n\t\"f5\": 116,\n\t\"f6\": 117,\n\t\"f7\": 118,\n\t\"f8\": 119,\n\t\"f9\": 120,\n\t\"f10\": 121,\n\t\"f11\": 122,\n\t\"f12\": 123,\n\t\"f13\": 124,\n\t\"f14\": 125,\n\t\"f15\": 126,\n\t\"f16\": 127,\n\t\"f17\": 128,\n\t\"f18\": 129,\n\t\"f19\": 130,\n\t\"f20\": 131,\n\t\"f21\": 132,\n\t\"f22\": 133,\n\t\"f23\": 134,\n\t\"f24\": 135,\n\t\"firefoxminus\": 173,\n\t\"semicolon\": 186,\n\t\"equals\": 187,\n\t\"comma\": 188,\n\t\"dash\": 189,\n\t\"period\": 190,\n\t\"slash\": 191,\n\t\"backquote\": 192,\n\t\"openbracket\": 219,\n\t\"backslash\": 220,\n\t\"closebracket\": 221,\n\t\"quote\": 222\n};\n\nfunction KeyboardManager(options) {\n\tvar self = this;\n\toptions = options || \"\";\n\t// Save the named key hashmap\n\tthis.namedKeys = namedKeys;\n\t// Create a reverse mapping of code to keyname\n\tthis.keyNames = [];\n\t$tw.utils.each(namedKeys,function(keyCode,name) {\n\t\tself.keyNames[keyCode] = name.substr(0,1).toUpperCase() + name.substr(1);\n\t});\n\t// Save the platform-specific name of the \"meta\" key\n\tthis.metaKeyName = $tw.platform.isMac ? \"cmd-\" : \"win-\";\n}\n\n/*\nReturn an array of keycodes for the modifier keys ctrl, shift, alt, meta\n*/\nKeyboardManager.prototype.getModifierKeys = function() {\n\treturn [\n\t\t16, // Shift\n\t\t17, // Ctrl\n\t\t18, // Alt\n\t\t20, // CAPS LOCK\n\t\t91, // Meta (left)\n\t\t93, // Meta (right)\n\t\t224 // Meta (Firefox)\n\t]\n};\n\n/*\nParses a key descriptor into the structure:\n{\n\tkeyCode: numeric keycode\n\tshiftKey: boolean\n\taltKey: boolean\n\tctrlKey: boolean\n\tmetaKey: boolean\n}\nKey descriptors have the following format:\n\tctrl+enter\n\tctrl+shift+alt+A\n*/\nKeyboardManager.prototype.parseKeyDescriptor = function(keyDescriptor) {\n\tvar components = keyDescriptor.split(/\\+|\\-/),\n\t\tinfo = {\n\t\t\tkeyCode: 0,\n\t\t\tshiftKey: false,\n\t\t\taltKey: false,\n\t\t\tctrlKey: false,\n\t\t\tmetaKey: false\n\t\t};\n\tfor(var t=0; t<components.length; t++) {\n\t\tvar s = components[t].toLowerCase(),\n\t\t\tc = s.charCodeAt(0);\n\t\t// Look for modifier keys\n\t\tif(s === \"ctrl\") {\n\t\t\tinfo.ctrlKey = true;\n\t\t} else if(s === \"shift\") {\n\t\t\tinfo.shiftKey = true;\n\t\t} else if(s === \"alt\") {\n\t\t\tinfo.altKey = true;\n\t\t} else if(s === \"meta\" || s === \"cmd\" || s === \"win\") {\n\t\t\tinfo.metaKey = true;\n\t\t}\n\t\t// Replace named keys with their code\n\t\tif(this.namedKeys[s]) {\n\t\t\tinfo.keyCode = this.namedKeys[s];\n\t\t}\n\t}\n\tif(info.keyCode) {\n\t\treturn info;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n/*\nParse a list of key descriptors into an array of keyInfo objects. The key descriptors can be passed as an array of strings or a space separated string\n*/\nKeyboardManager.prototype.parseKeyDescriptors = function(keyDescriptors,options) {\n\tvar self = this;\n\toptions = options || {};\n\toptions.stack = options.stack || [];\n\tvar wiki = options.wiki || $tw.wiki;\n\tif(typeof keyDescriptors === \"string\" && keyDescriptors === \"\") {\n\t\treturn [];\n\t}\n\tif(!$tw.utils.isArray(keyDescriptors)) {\n\t\tkeyDescriptors = keyDescriptors.split(\" \");\n\t}\n\tvar result = [];\n\t$tw.utils.each(keyDescriptors,function(keyDescriptor) {\n\t\t// Look for a named shortcut\n\t\tif(keyDescriptor.substr(0,2) === \"((\" && keyDescriptor.substr(-2,2) === \"))\") {\n\t\t\tif(options.stack.indexOf(keyDescriptor) === -1) {\n\t\t\t\toptions.stack.push(keyDescriptor);\n\t\t\t\tvar name = keyDescriptor.substring(2,keyDescriptor.length - 2),\n\t\t\t\t\tlookupName = function(configName) {\n\t\t\t\t\t\tvar keyDescriptors = wiki.getTiddlerText(\"$:/config/\" + configName + \"/\" + name);\n\t\t\t\t\t\tif(keyDescriptors) {\n\t\t\t\t\t\t\tresult.push.apply(result,self.parseKeyDescriptors(keyDescriptors,options));\n\t\t\t\t\t\t}\n\t\t\t\t\t};\n\t\t\t\tlookupName(\"shortcuts\");\n\t\t\t\tlookupName($tw.platform.isMac ? \"shortcuts-mac\" : \"shortcuts-not-mac\");\n\t\t\t\tlookupName($tw.platform.isWindows ? \"shortcuts-windows\" : \"shortcuts-not-windows\");\n\t\t\t\tlookupName($tw.platform.isLinux ? \"shortcuts-linux\" : \"shortcuts-not-linux\");\n\t\t\t}\n\t\t} else {\n\t\t\tresult.push(self.parseKeyDescriptor(keyDescriptor));\n\t\t}\n\t});\n\treturn result;\n};\n\nKeyboardManager.prototype.getPrintableShortcuts = function(keyInfoArray) {\n\tvar self = this,\n\t\tresult = [];\n\t$tw.utils.each(keyInfoArray,function(keyInfo) {\n\t\tif(keyInfo) {\n\t\t\tresult.push((keyInfo.ctrlKey ? \"ctrl-\" : \"\") + \n\t\t\t\t (keyInfo.shiftKey ? \"shift-\" : \"\") + \n\t\t\t\t (keyInfo.altKey ? \"alt-\" : \"\") + \n\t\t\t\t (keyInfo.metaKey ? self.metaKeyName : \"\") + \n\t\t\t\t (self.keyNames[keyInfo.keyCode]));\n\t\t}\n\t});\n\treturn result;\n}\n\nKeyboardManager.prototype.checkKeyDescriptor = function(event,keyInfo) {\n\treturn keyInfo &&\n\t\t\tevent.keyCode === keyInfo.keyCode && \n\t\t\tevent.shiftKey === keyInfo.shiftKey && \n\t\t\tevent.altKey === keyInfo.altKey && \n\t\t\tevent.ctrlKey === keyInfo.ctrlKey && \n\t\t\tevent.metaKey === keyInfo.metaKey;\n};\n\nKeyboardManager.prototype.checkKeyDescriptors = function(event,keyInfoArray) {\n\tfor(var t=0; t<keyInfoArray.length; t++) {\n\t\tif(this.checkKeyDescriptor(event,keyInfoArray[t])) {\n\t\t\treturn true;\n\t\t}\n\t}\n\treturn false;\n};\n\nexports.KeyboardManager = KeyboardManager;\n\n})();\n",
"title": "$:/core/modules/keyboard.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/language.js": {
"text": "/*\\\ntitle: $:/core/modules/language.js\ntype: application/javascript\nmodule-type: global\n\nThe $tw.Language() manages translateable strings\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nCreate an instance of the language manager. Options include:\nwiki: wiki from which to retrieve translation tiddlers\n*/\nfunction Language(options) {\n\toptions = options || \"\";\n\tthis.wiki = options.wiki || $tw.wiki;\n}\n\n/*\nReturn a wikified translateable string. The title is automatically prefixed with \"$:/language/\"\nOptions include:\nvariables: optional hashmap of variables to supply to the language wikification\n*/\nLanguage.prototype.getString = function(title,options) {\n\toptions = options || {};\n\ttitle = \"$:/language/\" + title;\n\treturn this.wiki.renderTiddler(\"text/plain\",title,{variables: options.variables});\n};\n\n/*\nReturn a raw, unwikified translateable string. The title is automatically prefixed with \"$:/language/\"\n*/\nLanguage.prototype.getRawString = function(title) {\n\ttitle = \"$:/language/\" + title;\n\treturn this.wiki.getTiddlerText(title);\n};\n\nexports.Language = Language;\n\n})();\n",
"title": "$:/core/modules/language.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/macros/changecount.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/changecount.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to return the changecount for the current tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"changecount\";\n\nexports.params = [];\n\n/*\nRun the macro\n*/\nexports.run = function() {\n\treturn this.wiki.getChangeCount(this.getVariable(\"currentTiddler\")) + \"\";\n};\n\n})();\n",
"title": "$:/core/modules/macros/changecount.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/contrastcolour.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/contrastcolour.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to choose which of two colours has the highest contrast with a base colour\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"contrastcolour\";\n\nexports.params = [\n\t{name: \"target\"},\n\t{name: \"fallbackTarget\"},\n\t{name: \"colourA\"},\n\t{name: \"colourB\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(target,fallbackTarget,colourA,colourB) {\n\tvar rgbTarget = $tw.utils.parseCSSColor(target) || $tw.utils.parseCSSColor(fallbackTarget);\n\tif(!rgbTarget) {\n\t\treturn colourA;\n\t}\n\tvar rgbColourA = $tw.utils.parseCSSColor(colourA),\n\t\trgbColourB = $tw.utils.parseCSSColor(colourB);\n\tif(rgbColourA && !rgbColourB) {\n\t\treturn rgbColourA;\n\t}\n\tif(rgbColourB && !rgbColourA) {\n\t\treturn rgbColourB;\n\t}\n\tif(!rgbColourA && !rgbColourB) {\n\t\t// If neither colour is readable, return a crude inverse of the target\n\t\treturn [255 - rgbTarget[0],255 - rgbTarget[1],255 - rgbTarget[2],rgbTarget[3]];\n\t}\n\t// Colour brightness formula derived from http://www.w3.org/WAI/ER/WD-AERT/#color-contrast\n\tvar brightnessTarget = rgbTarget[0] * 0.299 + rgbTarget[1] * 0.587 + rgbTarget[2] * 0.114,\n\t\tbrightnessA = rgbColourA[0] * 0.299 + rgbColourA[1] * 0.587 + rgbColourA[2] * 0.114,\n\t\tbrightnessB = rgbColourB[0] * 0.299 + rgbColourB[1] * 0.587 + rgbColourB[2] * 0.114;\n\treturn Math.abs(brightnessTarget - brightnessA) > Math.abs(brightnessTarget - brightnessB) ? colourA : colourB;\n};\n\n})();\n",
"title": "$:/core/modules/macros/contrastcolour.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/csvtiddlers.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/csvtiddlers.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to output tiddlers matching a filter to CSV\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"csvtiddlers\";\n\nexports.params = [\n\t{name: \"filter\"},\n\t{name: \"format\"},\n];\n\n/*\nRun the macro\n*/\nexports.run = function(filter,format) {\n\tvar self = this,\n\t\ttiddlers = this.wiki.filterTiddlers(filter),\n\t\ttiddler,\n\t\tfields = [],\n\t\tt,f;\n\t// Collect all the fields\n\tfor(t=0;t<tiddlers.length; t++) {\n\t\ttiddler = this.wiki.getTiddler(tiddlers[t]);\n\t\tfor(f in tiddler.fields) {\n\t\t\tif(fields.indexOf(f) === -1) {\n\t\t\t\tfields.push(f);\n\t\t\t}\n\t\t}\n\t}\n\t// Sort the fields and bring the standard ones to the front\n\tfields.sort();\n\t\"title text modified modifier created creator\".split(\" \").reverse().forEach(function(value,index) {\n\t\tvar p = fields.indexOf(value);\n\t\tif(p !== -1) {\n\t\t\tfields.splice(p,1);\n\t\t\tfields.unshift(value)\n\t\t}\n\t});\n\t// Output the column headings\n\tvar output = [], row = [];\n\tfields.forEach(function(value) {\n\t\trow.push(quoteAndEscape(value))\n\t});\n\toutput.push(row.join(\",\"));\n\t// Output each tiddler\n\tfor(var t=0;t<tiddlers.length; t++) {\n\t\trow = [];\n\t\ttiddler = this.wiki.getTiddler(tiddlers[t]);\n\t\t\tfor(f=0; f<fields.length; f++) {\n\t\t\t\trow.push(quoteAndEscape(tiddler ? tiddler.getFieldString(fields[f]) || \"\" : \"\"));\n\t\t\t}\n\t\toutput.push(row.join(\",\"));\n\t}\n\treturn output.join(\"\\n\");\n};\n\nfunction quoteAndEscape(value) {\n\treturn \"\\\"\" + value.replace(/\"/mg,\"\\\"\\\"\") + \"\\\"\";\n}\n\n})();\n",
"title": "$:/core/modules/macros/csvtiddlers.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/displayshortcuts.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/displayshortcuts.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to display a list of keyboard shortcuts in human readable form. Notably, it resolves named shortcuts like `((bold))` to the underlying keystrokes.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"displayshortcuts\";\n\nexports.params = [\n\t{name: \"shortcuts\"},\n\t{name: \"prefix\"},\n\t{name: \"separator\"},\n\t{name: \"suffix\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(shortcuts,prefix,separator,suffix) {\n\tvar shortcutArray = $tw.keyboardManager.getPrintableShortcuts($tw.keyboardManager.parseKeyDescriptors(shortcuts,{\n\t\twiki: this.wiki\n\t}));\n\tif(shortcutArray.length > 0) {\n\t\tshortcutArray.sort(function(a,b) {\n\t\t return a.toLowerCase().localeCompare(b.toLowerCase());\n\t\t})\n\t\treturn prefix + shortcutArray.join(separator) + suffix;\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/macros/displayshortcuts.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/dumpvariables.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/dumpvariables.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to dump all active variable values\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"dumpvariables\";\n\nexports.params = [\n];\n\n/*\nRun the macro\n*/\nexports.run = function() {\n\tvar output = [\"|!Variable |!Value |\"],\n\t\tvariables = [], variable;\n\tfor(variable in this.variables) {\n\t\tvariables.push(variable);\n\t}\n\tvariables.sort();\n\tfor(var index=0; index<variables.length; index++) {\n\t\tvar variable = variables[index];\n\t\toutput.push(\"|\" + variable + \" |<input size=50 value=<<\" + variable + \">>/> |\")\n\t}\n\treturn output.join(\"\\n\");\n};\n\n})();\n",
"title": "$:/core/modules/macros/dumpvariables.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/jsontiddler.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/jsontiddler.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to output a single tiddler to JSON\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"jsontiddler\";\n\nexports.params = [\n\t{name: \"title\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(title) {\n\ttitle = title || this.getVariable(\"currentTiddler\");\n\tvar tiddler = !!title && this.wiki.getTiddler(title),\n\t\tfields = new Object();\n\tif(tiddler) {\n\t\tfor(var field in tiddler.fields) {\n\t\t\tfields[field] = tiddler.getFieldString(field);\n\t\t}\n\t}\n\treturn JSON.stringify(fields,null,$tw.config.preferences.jsonSpaces);\n};\n\n})();\n",
"title": "$:/core/modules/macros/jsontiddler.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/jsontiddlers.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/jsontiddlers.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to output tiddlers matching a filter to JSON\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"jsontiddlers\";\n\nexports.params = [\n\t{name: \"filter\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(filter) {\n\tvar tiddlers = this.wiki.filterTiddlers(filter),\n\t\tdata = [];\n\tfor(var t=0;t<tiddlers.length; t++) {\n\t\tvar tiddler = this.wiki.getTiddler(tiddlers[t]);\n\t\tif(tiddler) {\n\t\t\tvar fields = new Object();\n\t\t\tfor(var field in tiddler.fields) {\n\t\t\t\tfields[field] = tiddler.getFieldString(field);\n\t\t\t}\n\t\t\tdata.push(fields);\n\t\t}\n\t}\n\treturn JSON.stringify(data,null,$tw.config.preferences.jsonSpaces);\n};\n\n})();\n",
"title": "$:/core/modules/macros/jsontiddlers.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/makedatauri.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/makedatauri.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to convert a string of text to a data URI\n\n<<makedatauri text:\"Text to be converted\" type:\"text/vnd.tiddlywiki\">>\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"makedatauri\";\n\nexports.params = [\n\t{name: \"text\"},\n\t{name: \"type\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(text,type) {\n\treturn $tw.utils.makeDataUri(text,type);\n};\n\n})();\n",
"title": "$:/core/modules/macros/makedatauri.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/now.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/now.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to return a formatted version of the current time\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"now\";\n\nexports.params = [\n\t{name: \"format\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(format) {\n\treturn $tw.utils.formatDateString(new Date(),format || \"0hh:0mm, DDth MMM YYYY\");\n};\n\n})();\n",
"title": "$:/core/modules/macros/now.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/qualify.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/qualify.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to qualify a state tiddler title according\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"qualify\";\n\nexports.params = [\n\t{name: \"title\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(title) {\n\treturn title + \"-\" + this.getStateQualifier();\n};\n\n})();\n",
"title": "$:/core/modules/macros/qualify.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/resolvepath.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/resolvepath.js\ntype: application/javascript\nmodule-type: macro\n\nResolves a relative path for an absolute rootpath.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"resolvepath\";\n\nexports.params = [\n\t{name: \"source\"},\n\t{name: \"root\"}\n];\n\n/*\nRun the macro\n*/\nexports.run = function(source, root) {\n\treturn $tw.utils.resolvePath(source, root);\n};\n\n})();\n",
"title": "$:/core/modules/macros/resolvepath.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/macros/version.js": {
"text": "/*\\\ntitle: $:/core/modules/macros/version.js\ntype: application/javascript\nmodule-type: macro\n\nMacro to return the TiddlyWiki core version number\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInformation about this macro\n*/\n\nexports.name = \"version\";\n\nexports.params = [];\n\n/*\nRun the macro\n*/\nexports.run = function() {\n\treturn $tw.version;\n};\n\n})();\n",
"title": "$:/core/modules/macros/version.js",
"type": "application/javascript",
"module-type": "macro"
},
"$:/core/modules/parsers/audioparser.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/audioparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe audio parser parses an audio tiddler into an embeddable HTML element\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar AudioParser = function(type,text,options) {\n\tvar element = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"audio\",\n\t\t\tattributes: {\n\t\t\t\tcontrols: {type: \"string\", value: \"controls\"}\n\t\t\t}\n\t\t},\n\t\tsrc;\n\tif(options._canonical_uri) {\n\t\telement.attributes.src = {type: \"string\", value: options._canonical_uri};\n\t} else if(text) {\n\t\telement.attributes.src = {type: \"string\", value: \"data:\" + type + \";base64,\" + text};\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"audio/ogg\"] = AudioParser;\nexports[\"audio/mpeg\"] = AudioParser;\nexports[\"audio/mp3\"] = AudioParser;\nexports[\"audio/mp4\"] = AudioParser;\n\n})();\n\n",
"title": "$:/core/modules/parsers/audioparser.js",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/csvparser.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/csvparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe CSV text parser processes CSV files into a table wrapped in a scrollable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar CsvParser = function(type,text,options) {\n\t// Table framework\n\tthis.tree = [{\n\t\t\"type\": \"scrollable\", \"children\": [{\n\t\t\t\"type\": \"element\", \"tag\": \"table\", \"children\": [{\n\t\t\t\t\"type\": \"element\", \"tag\": \"tbody\", \"children\": []\n\t\t\t}], \"attributes\": {\n\t\t\t\t\"class\": {\"type\": \"string\", \"value\": \"tc-csv-table\"}\n\t\t\t}\n\t\t}]\n\t}];\n\t// Split the text into lines\n\tvar lines = text.split(/\\r?\\n/mg),\n\t\ttag = \"th\";\n\tfor(var line=0; line<lines.length; line++) {\n\t\tvar lineText = lines[line];\n\t\tif(lineText) {\n\t\t\tvar row = {\n\t\t\t\t\t\"type\": \"element\", \"tag\": \"tr\", \"children\": []\n\t\t\t\t};\n\t\t\tvar columns = lineText.split(\",\");\n\t\t\tfor(var column=0; column<columns.length; column++) {\n\t\t\t\trow.children.push({\n\t\t\t\t\t\t\"type\": \"element\", \"tag\": tag, \"children\": [{\n\t\t\t\t\t\t\t\"type\": \"text\",\n\t\t\t\t\t\t\t\"text\": columns[column]\n\t\t\t\t\t\t}]\n\t\t\t\t\t});\n\t\t\t}\n\t\t\ttag = \"td\";\n\t\t\tthis.tree[0].children[0].children[0].children.push(row);\n\t\t}\n\t}\n};\n\nexports[\"text/csv\"] = CsvParser;\n\n})();\n\n",
"title": "$:/core/modules/parsers/csvparser.js",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/htmlparser.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/htmlparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe HTML parser displays text as raw HTML\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar HtmlParser = function(type,text,options) {\n\tvar src;\n\tif(options._canonical_uri) {\n\t\tsrc = options._canonical_uri;\n\t} else if(text) {\n\t\tsrc = \"data:text/html;charset=utf-8,\" + encodeURIComponent(text);\n\t}\n\tthis.tree = [{\n\t\ttype: \"element\",\n\t\ttag: \"iframe\",\n\t\tattributes: {\n\t\t\tsrc: {type: \"string\", value: src},\n\t\t\tsandbox: {type: \"string\", value: \"\"}\n\t\t}\n\t}];\n};\n\nexports[\"text/html\"] = HtmlParser;\n\n})();\n\n",
"title": "$:/core/modules/parsers/htmlparser.js",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/imageparser.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/imageparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe image parser parses an image into an embeddable HTML element\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar ImageParser = function(type,text,options) {\n\tvar element = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"img\",\n\t\t\tattributes: {}\n\t\t},\n\t\tsrc;\n\tif(options._canonical_uri) {\n\t\telement.attributes.src = {type: \"string\", value: options._canonical_uri};\n\t} else if(text) {\n\t\tif(type === \"image/svg+xml\" || type === \".svg\") {\n\t\t\telement.attributes.src = {type: \"string\", value: \"data:image/svg+xml,\" + encodeURIComponent(text)};\n\t\t} else {\n\t\t\telement.attributes.src = {type: \"string\", value: \"data:\" + type + \";base64,\" + text};\n\t\t}\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"image/svg+xml\"] = ImageParser;\nexports[\"image/jpg\"] = ImageParser;\nexports[\"image/jpeg\"] = ImageParser;\nexports[\"image/png\"] = ImageParser;\nexports[\"image/gif\"] = ImageParser;\nexports[\"image/x-icon\"] = ImageParser;\n\n})();\n\n",
"title": "$:/core/modules/parsers/imageparser.js",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/utils/parseutils.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/parseutils.js\ntype: application/javascript\nmodule-type: utils\n\nUtility functions concerned with parsing text into tokens.\n\nMost functions have the following pattern:\n\n* The parameters are:\n** `source`: the source string being parsed\n** `pos`: the current parse position within the string\n** Any further parameters are used to identify the token that is being parsed\n* The return value is:\n** null if the token was not found at the specified position\n** an object representing the token with the following standard fields:\n*** `type`: string indicating the type of the token\n*** `start`: start position of the token in the source string\n*** `end`: end position of the token in the source string\n*** Any further fields required to describe the token\n\nThe exception is `skipWhiteSpace`, which just returns the position after the whitespace.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nLook for a whitespace token. Returns null if not found, otherwise returns {type: \"whitespace\", start:, end:,}\n*/\nexports.parseWhiteSpace = function(source,pos) {\n\tvar p = pos,c;\n\twhile(true) {\n\t\tc = source.charAt(p);\n\t\tif((c === \" \") || (c === \"\\f\") || (c === \"\\n\") || (c === \"\\r\") || (c === \"\\t\") || (c === \"\\v\") || (c === \"\\u00a0\")) { // Ignores some obscure unicode spaces\n\t\t\tp++;\n\t\t} else {\n\t\t\tbreak;\n\t\t}\n\t}\n\tif(p === pos) {\n\t\treturn null;\n\t} else {\n\t\treturn {\n\t\t\ttype: \"whitespace\",\n\t\t\tstart: pos,\n\t\t\tend: p\n\t\t}\n\t}\n};\n\n/*\nConvenience wrapper for parseWhiteSpace. Returns the position after the whitespace\n*/\nexports.skipWhiteSpace = function(source,pos) {\n\tvar c;\n\twhile(true) {\n\t\tc = source.charAt(pos);\n\t\tif((c === \" \") || (c === \"\\f\") || (c === \"\\n\") || (c === \"\\r\") || (c === \"\\t\") || (c === \"\\v\") || (c === \"\\u00a0\")) { // Ignores some obscure unicode spaces\n\t\t\tpos++;\n\t\t} else {\n\t\t\treturn pos;\n\t\t}\n\t}\n};\n\n/*\nLook for a given string token. Returns null if not found, otherwise returns {type: \"token\", value:, start:, end:,}\n*/\nexports.parseTokenString = function(source,pos,token) {\n\tvar match = source.indexOf(token,pos) === pos;\n\tif(match) {\n\t\treturn {\n\t\t\ttype: \"token\",\n\t\t\tvalue: token,\n\t\t\tstart: pos,\n\t\t\tend: pos + token.length\n\t\t};\n\t}\n\treturn null;\n};\n\n/*\nLook for a token matching a regex. Returns null if not found, otherwise returns {type: \"regexp\", match:, start:, end:,}\n*/\nexports.parseTokenRegExp = function(source,pos,reToken) {\n\tvar node = {\n\t\ttype: \"regexp\",\n\t\tstart: pos\n\t};\n\treToken.lastIndex = pos;\n\tnode.match = reToken.exec(source);\n\tif(node.match && node.match.index === pos) {\n\t\tnode.end = pos + node.match[0].length;\n\t\treturn node;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n/*\nLook for a string literal. Returns null if not found, otherwise returns {type: \"string\", value:, start:, end:,}\n*/\nexports.parseStringLiteral = function(source,pos) {\n\tvar node = {\n\t\ttype: \"string\",\n\t\tstart: pos\n\t};\n\tvar reString = /(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\")|(?:'([^']*)')/g;\n\treString.lastIndex = pos;\n\tvar match = reString.exec(source);\n\tif(match && match.index === pos) {\n\t\tnode.value = match[1] !== undefined ? match[1] :(\n\t\t\tmatch[2] !== undefined ? match[2] : match[3] \n\t\t\t\t\t);\n\t\tnode.end = pos + match[0].length;\n\t\treturn node;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n/*\nLook for a macro invocation parameter. Returns null if not found, or {type: \"macro-parameter\", name:, value:, start:, end:}\n*/\nexports.parseMacroParameter = function(source,pos) {\n\tvar node = {\n\t\ttype: \"macro-parameter\",\n\t\tstart: pos\n\t};\n\t// Define our regexp\n\tvar reMacroParameter = /(?:([A-Za-z0-9\\-_]+)\\s*:)?(?:\\s*(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\"|'([^']*)'|\\[\\[([^\\]]*)\\]\\]|([^\\s>\"'=]+)))/g;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for the parameter\n\tvar token = $tw.utils.parseTokenRegExp(source,pos,reMacroParameter);\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Get the parameter details\n\tnode.value = token.match[2] !== undefined ? token.match[2] : (\n\t\t\t\t\ttoken.match[3] !== undefined ? token.match[3] : (\n\t\t\t\t\t\ttoken.match[4] !== undefined ? token.match[4] : (\n\t\t\t\t\t\t\ttoken.match[5] !== undefined ? token.match[5] : (\n\t\t\t\t\t\t\t\ttoken.match[6] !== undefined ? token.match[6] : (\n\t\t\t\t\t\t\t\t\t\"\"\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t)\n\t\t\t\t\t)\n\t\t\t\t);\n\tif(token.match[1]) {\n\t\tnode.name = token.match[1];\n\t}\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\n/*\nLook for a macro invocation. Returns null if not found, or {type: \"macrocall\", name:, parameters:, start:, end:}\n*/\nexports.parseMacroInvocation = function(source,pos) {\n\tvar node = {\n\t\ttype: \"macrocall\",\n\t\tstart: pos,\n\t\tparams: []\n\t};\n\t// Define our regexps\n\tvar reMacroName = /([^\\s>\"'=]+)/g;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for a double less than sign\n\tvar token = $tw.utils.parseTokenString(source,pos,\"<<\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Get the macro name\n\tvar name = $tw.utils.parseTokenRegExp(source,pos,reMacroName);\n\tif(!name) {\n\t\treturn null;\n\t}\n\tnode.name = name.match[1];\n\tpos = name.end;\n\t// Process parameters\n\tvar parameter = $tw.utils.parseMacroParameter(source,pos);\n\twhile(parameter) {\n\t\tnode.params.push(parameter);\n\t\tpos = parameter.end;\n\t\t// Get the next parameter\n\t\tparameter = $tw.utils.parseMacroParameter(source,pos);\n\t}\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for a double greater than sign\n\ttoken = $tw.utils.parseTokenString(source,pos,\">>\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\n/*\nLook for an HTML attribute definition. Returns null if not found, otherwise returns {type: \"attribute\", name:, valueType: \"string|indirect|macro\", value:, start:, end:,}\n*/\nexports.parseAttribute = function(source,pos) {\n\tvar node = {\n\t\tstart: pos\n\t};\n\t// Define our regexps\n\tvar reAttributeName = /([^\\/\\s>\"'=]+)/g,\n\t\treUnquotedAttribute = /([^\\/\\s<>\"'=]+)/g,\n\t\treFilteredValue = /\\{\\{\\{(.+?)\\}\\}\\}/g,\n\t\treIndirectValue = /\\{\\{([^\\}]+)\\}\\}/g;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Get the attribute name\n\tvar name = $tw.utils.parseTokenRegExp(source,pos,reAttributeName);\n\tif(!name) {\n\t\treturn null;\n\t}\n\tnode.name = name.match[1];\n\tpos = name.end;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for an equals sign\n\tvar token = $tw.utils.parseTokenString(source,pos,\"=\");\n\tif(token) {\n\t\tpos = token.end;\n\t\t// Skip whitespace\n\t\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t\t// Look for a string literal\n\t\tvar stringLiteral = $tw.utils.parseStringLiteral(source,pos);\n\t\tif(stringLiteral) {\n\t\t\tpos = stringLiteral.end;\n\t\t\tnode.type = \"string\";\n\t\t\tnode.value = stringLiteral.value;\n\t\t} else {\n\t\t\t// Look for a filtered value\n\t\t\tvar filteredValue = $tw.utils.parseTokenRegExp(source,pos,reFilteredValue);\n\t\t\tif(filteredValue) {\n\t\t\t\tpos = filteredValue.end;\n\t\t\t\tnode.type = \"filtered\";\n\t\t\t\tnode.filter = filteredValue.match[1];\n\t\t\t} else {\n\t\t\t\t// Look for an indirect value\n\t\t\t\tvar indirectValue = $tw.utils.parseTokenRegExp(source,pos,reIndirectValue);\n\t\t\t\tif(indirectValue) {\n\t\t\t\t\tpos = indirectValue.end;\n\t\t\t\t\tnode.type = \"indirect\";\n\t\t\t\t\tnode.textReference = indirectValue.match[1];\n\t\t\t\t} else {\n\t\t\t\t\t// Look for a unquoted value\n\t\t\t\t\tvar unquotedValue = $tw.utils.parseTokenRegExp(source,pos,reUnquotedAttribute);\n\t\t\t\t\tif(unquotedValue) {\n\t\t\t\t\t\tpos = unquotedValue.end;\n\t\t\t\t\t\tnode.type = \"string\";\n\t\t\t\t\t\tnode.value = unquotedValue.match[1];\n\t\t\t\t\t} else {\n\t\t\t\t\t\t// Look for a macro invocation value\n\t\t\t\t\t\tvar macroInvocation = $tw.utils.parseMacroInvocation(source,pos);\n\t\t\t\t\t\tif(macroInvocation) {\n\t\t\t\t\t\t\tpos = macroInvocation.end;\n\t\t\t\t\t\t\tnode.type = \"macro\";\n\t\t\t\t\t\t\tnode.value = macroInvocation;\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\tnode.type = \"string\";\n\t\t\t\t\t\t\tnode.value = \"true\";\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t} else {\n\t\tnode.type = \"string\";\n\t\tnode.value = \"true\";\n\t}\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\n})();\n",
"title": "$:/core/modules/utils/parseutils.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/parsers/pdfparser.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/pdfparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe PDF parser embeds a PDF viewer\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar ImageParser = function(type,text,options) {\n\tvar element = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"embed\",\n\t\t\tattributes: {}\n\t\t},\n\t\tsrc;\n\tif(options._canonical_uri) {\n\t\telement.attributes.src = {type: \"string\", value: options._canonical_uri};\n\t} else if(text) {\n\t\telement.attributes.src = {type: \"string\", value: \"data:application/pdf;base64,\" + text};\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"application/pdf\"] = ImageParser;\n\n})();\n\n",
"title": "$:/core/modules/parsers/pdfparser.js",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/textparser.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/textparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe plain text parser processes blocks of source text into a degenerate parse tree consisting of a single text node\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar TextParser = function(type,text,options) {\n\tthis.tree = [{\n\t\ttype: \"codeblock\",\n\t\tattributes: {\n\t\t\tcode: {type: \"string\", value: text},\n\t\t\tlanguage: {type: \"string\", value: type}\n\t\t}\n\t}];\n};\n\nexports[\"text/plain\"] = TextParser;\nexports[\"text/x-tiddlywiki\"] = TextParser;\nexports[\"application/javascript\"] = TextParser;\nexports[\"application/json\"] = TextParser;\nexports[\"text/css\"] = TextParser;\nexports[\"application/x-tiddler-dictionary\"] = TextParser;\n\n})();\n\n",
"title": "$:/core/modules/parsers/textparser.js",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/videoparser.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/videoparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe video parser parses a video tiddler into an embeddable HTML element\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar VideoParser = function(type,text,options) {\n\tvar element = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"video\",\n\t\t\tattributes: {\n\t\t\t\tcontrols: {type: \"string\", value: \"controls\"}\n\t\t\t}\n\t\t},\n\t\tsrc;\n\tif(options._canonical_uri) {\n\t\telement.attributes.src = {type: \"string\", value: options._canonical_uri};\n\t} else if(text) {\n\t\telement.attributes.src = {type: \"string\", value: \"data:\" + type + \";base64,\" + text};\n\t}\n\tthis.tree = [element];\n};\n\nexports[\"video/mp4\"] = VideoParser;\nexports[\"video/quicktime\"] = VideoParser;\n\n})();\n\n",
"title": "$:/core/modules/parsers/videoparser.js",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/wikiparser/rules/codeblock.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/codeblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for code blocks. For example:\n\n```\n\t```\n\tThis text will not be //wikified//\n\t```\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"codeblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match and get language if defined\n\tthis.matchRegExp = /```([\\w-]*)\\r?\\n/mg;\n};\n\nexports.parse = function() {\n\tvar reEnd = /(\\r?\\n```$)/mg;\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Look for the end of the block\n\treEnd.lastIndex = this.parser.pos;\n\tvar match = reEnd.exec(this.parser.source),\n\t\ttext;\n\t// Process the block\n\tif(match) {\n\t\ttext = this.parser.source.substring(this.parser.pos,match.index);\n\t\tthis.parser.pos = match.index + match[0].length;\n\t} else {\n\t\ttext = this.parser.source.substr(this.parser.pos);\n\t\tthis.parser.pos = this.parser.sourceLength;\n\t}\n\t// Return the $codeblock widget\n\treturn [{\n\t\t\ttype: \"codeblock\",\n\t\t\tattributes: {\n\t\t\t\t\tcode: {type: \"string\", value: text},\n\t\t\t\t\tlanguage: {type: \"string\", value: this.match[1]}\n\t\t\t}\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/codeblock.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/codeinline.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/codeinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for code runs. For example:\n\n```\n\tThis is a `code run`.\n\tThis is another ``code run``\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"codeinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /(``?)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar reEnd = new RegExp(this.match[1], \"mg\");\n\t// Look for the end marker\n\treEnd.lastIndex = this.parser.pos;\n\tvar match = reEnd.exec(this.parser.source),\n\t\ttext;\n\t// Process the text\n\tif(match) {\n\t\ttext = this.parser.source.substring(this.parser.pos,match.index);\n\t\tthis.parser.pos = match.index + match[0].length;\n\t} else {\n\t\ttext = this.parser.source.substr(this.parser.pos);\n\t\tthis.parser.pos = this.parser.sourceLength;\n\t}\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"code\",\n\t\tchildren: [{\n\t\t\ttype: \"text\",\n\t\t\ttext: text\n\t\t}]\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/codeinline.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/commentblock.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/commentblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for HTML comments. For example:\n\n```\n<!-- This is a comment -->\n```\n\nNote that the syntax for comments is simplified to an opening \"<!--\" sequence and a closing \"-->\" sequence -- HTML itself implements a more complex format (see http://ostermiller.org/findhtmlcomment.html)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"commentblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\tthis.matchRegExp = /<!--/mg;\n\tthis.endMatchRegExp = /-->/mg;\n};\n\nexports.findNextMatch = function(startPos) {\n\tthis.matchRegExp.lastIndex = startPos;\n\tthis.match = this.matchRegExp.exec(this.parser.source);\n\tif(this.match) {\n\t\tthis.endMatchRegExp.lastIndex = startPos + this.match[0].length;\n\t\tthis.endMatch = this.endMatchRegExp.exec(this.parser.source);\n\t\tif(this.endMatch) {\n\t\t\treturn this.match.index;\n\t\t}\n\t}\n\treturn undefined;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.endMatchRegExp.lastIndex;\n\t// Don't return any elements\n\treturn [];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/commentblock.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/commentinline.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/commentinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for HTML comments. For example:\n\n```\n<!-- This is a comment -->\n```\n\nNote that the syntax for comments is simplified to an opening \"<!--\" sequence and a closing \"-->\" sequence -- HTML itself implements a more complex format (see http://ostermiller.org/findhtmlcomment.html)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"commentinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\tthis.matchRegExp = /<!--/mg;\n\tthis.endMatchRegExp = /-->/mg;\n};\n\nexports.findNextMatch = function(startPos) {\n\tthis.matchRegExp.lastIndex = startPos;\n\tthis.match = this.matchRegExp.exec(this.parser.source);\n\tif(this.match) {\n\t\tthis.endMatchRegExp.lastIndex = startPos + this.match[0].length;\n\t\tthis.endMatch = this.endMatchRegExp.exec(this.parser.source);\n\t\tif(this.endMatch) {\n\t\t\treturn this.match.index;\n\t\t}\n\t}\n\treturn undefined;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.endMatchRegExp.lastIndex;\n\t// Don't return any elements\n\treturn [];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/commentinline.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/dash.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/dash.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for dashes. For example:\n\n```\nThis is an en-dash: --\n\nThis is an em-dash: ---\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"dash\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /-{2,3}(?!-)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar dash = this.match[0].length === 2 ? \"–\" : \"—\";\n\treturn [{\n\t\ttype: \"entity\",\n\t\tentity: dash\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/dash.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/bold.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/bold.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - bold. For example:\n\n```\n\tThis is ''bold'' text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except bold \n\\rules only bold \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"bold\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /''/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/''/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"strong\",\n\t\tchildren: tree\n\t}];\n};\n\n})();",
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/bold.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/italic.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/italic.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - italic. For example:\n\n```\n\tThis is //italic// text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except italic\n\\rules only italic\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"italic\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\/\\//mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/\\/\\//mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"em\",\n\t\tchildren: tree\n\t}];\n};\n\n})();",
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/italic.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/strikethrough.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/strikethrough.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - strikethrough. For example:\n\n```\n\tThis is ~~strikethrough~~ text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except strikethrough \n\\rules only strikethrough \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"strikethrough\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /~~/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/~~/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"strike\",\n\t\tchildren: tree\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/strikethrough.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/subscript.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/subscript.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - subscript. For example:\n\n```\n\tThis is ,,subscript,, text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except subscript \n\\rules only subscript \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"subscript\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /,,/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/,,/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"sub\",\n\t\tchildren: tree\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/subscript.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/superscript.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/superscript.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - superscript. For example:\n\n```\n\tThis is ^^superscript^^ text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except superscript \n\\rules only superscript \n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"superscript\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\^\\^/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/\\^\\^/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"sup\",\n\t\tchildren: tree\n\t}];\n};\n\n})();",
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/superscript.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/emphasis/underscore.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/emphasis/underscore.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for emphasis - underscore. For example:\n\n```\n\tThis is __underscore__ text\n```\n\nThis wikiparser can be modified using the rules eg:\n\n```\n\\rules except underscore \n\\rules only underscore\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"underscore\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /__/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\n\t// Parse the run including the terminator\n\tvar tree = this.parser.parseInlineRun(/__/mg,{eatTerminator: true});\n\n\t// Return the classed span\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"u\",\n\t\tchildren: tree\n\t}];\n};\n\n})();",
"title": "$:/core/modules/parsers/wikiparser/rules/emphasis/underscore.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/entity.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/entity.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for HTML entities. For example:\n\n```\n\tThis is a copyright symbol: ©\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"entity\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /(&#?[a-zA-Z0-9]{2,8};)/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get all the details of the match\n\tvar entityString = this.match[1];\n\t// Move past the macro call\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Return the entity\n\treturn [{type: \"entity\", entity: this.match[0]}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/entity.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/extlink.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/extlink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for external links. For example:\n\n```\nAn external link: http://www.tiddlywiki.com/\n\nA suppressed external link: ~http://www.tiddlyspace.com/\n```\n\nExternal links can be suppressed by preceding them with `~`.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"extlink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /~?(?:file|http|https|mailto|ftp|irc|news|data|skype):[^\\s<>{}\\[\\]`|\"\\\\^]+(?:\\/|\\b)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Create the link unless it is suppressed\n\tif(this.match[0].substr(0,1) === \"~\") {\n\t\treturn [{type: \"text\", text: this.match[0].substr(1)}];\n\t} else {\n\t\treturn [{\n\t\t\ttype: \"element\",\n\t\t\ttag: \"a\",\n\t\t\tattributes: {\n\t\t\t\thref: {type: \"string\", value: this.match[0]},\n\t\t\t\t\"class\": {type: \"string\", value: \"tc-tiddlylink-external\"},\n\t\t\t\ttarget: {type: \"string\", value: \"_blank\"},\n\t\t\t\trel: {type: \"string\", value: \"noopener noreferrer\"}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\", text: this.match[0]\n\t\t\t}]\n\t\t}];\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/extlink.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/filteredtranscludeblock.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/filteredtranscludeblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for block-level filtered transclusion. For example:\n\n```\n{{{ [tag[docs]] }}}\n{{{ [tag[docs]] |tooltip}}}\n{{{ [tag[docs]] ||TemplateTitle}}}\n{{{ [tag[docs]] |tooltip||TemplateTitle}}}\n{{{ [tag[docs]] }}width:40;height:50;}.class.class\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"filteredtranscludeblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\{\\{\\{([^\\|]+?)(?:\\|([^\\|\\{\\}]+))?(?:\\|\\|([^\\|\\{\\}]+))?\\}\\}([^\\}]*)\\}(?:\\.(\\S+))?(?:\\r?\\n|$)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Get the match details\n\tvar filter = this.match[1],\n\t\ttooltip = this.match[2],\n\t\ttemplate = $tw.utils.trim(this.match[3]),\n\t\tstyle = this.match[4],\n\t\tclasses = this.match[5];\n\t// Return the list widget\n\tvar node = {\n\t\ttype: \"list\",\n\t\tattributes: {\n\t\t\tfilter: {type: \"string\", value: filter}\n\t\t},\n\t\tisBlock: true\n\t};\n\tif(tooltip) {\n\t\tnode.attributes.tooltip = {type: \"string\", value: tooltip};\n\t}\n\tif(template) {\n\t\tnode.attributes.template = {type: \"string\", value: template};\n\t}\n\tif(style) {\n\t\tnode.attributes.style = {type: \"string\", value: style};\n\t}\n\tif(classes) {\n\t\tnode.attributes.itemClass = {type: \"string\", value: classes.split(\".\").join(\" \")};\n\t}\n\treturn [node];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/filteredtranscludeblock.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/filteredtranscludeinline.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/filteredtranscludeinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for inline filtered transclusion. For example:\n\n```\n{{{ [tag[docs]] }}}\n{{{ [tag[docs]] |tooltip}}}\n{{{ [tag[docs]] ||TemplateTitle}}}\n{{{ [tag[docs]] |tooltip||TemplateTitle}}}\n{{{ [tag[docs]] }}width:40;height:50;}.class.class\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"filteredtranscludeinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\{\\{\\{([^\\|]+?)(?:\\|([^\\|\\{\\}]+))?(?:\\|\\|([^\\|\\{\\}]+))?\\}\\}([^\\}]*)\\}(?:\\.(\\S+))?/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Get the match details\n\tvar filter = this.match[1],\n\t\ttooltip = this.match[2],\n\t\ttemplate = $tw.utils.trim(this.match[3]),\n\t\tstyle = this.match[4],\n\t\tclasses = this.match[5];\n\t// Return the list widget\n\tvar node = {\n\t\ttype: \"list\",\n\t\tattributes: {\n\t\t\tfilter: {type: \"string\", value: filter}\n\t\t}\n\t};\n\tif(tooltip) {\n\t\tnode.attributes.tooltip = {type: \"string\", value: tooltip};\n\t}\n\tif(template) {\n\t\tnode.attributes.template = {type: \"string\", value: template};\n\t}\n\tif(style) {\n\t\tnode.attributes.style = {type: \"string\", value: style};\n\t}\n\tif(classes) {\n\t\tnode.attributes.itemClass = {type: \"string\", value: classes.split(\".\").join(\" \")};\n\t}\n\treturn [node];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/filteredtranscludeinline.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/hardlinebreaks.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/hardlinebreaks.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for marking areas with hard line breaks. For example:\n\n```\n\"\"\"\nThis is some text\nThat is set like\nIt is a Poem\nWhen it is\nClearly\nNot\n\"\"\"\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"hardlinebreaks\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\"\"\"(?:\\r?\\n)?/mg;\n};\n\nexports.parse = function() {\n\tvar reEnd = /(\"\"\")|(\\r?\\n)/mg,\n\t\ttree = [],\n\t\tmatch;\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tdo {\n\t\t// Parse the run up to the terminator\n\t\ttree.push.apply(tree,this.parser.parseInlineRun(reEnd,{eatTerminator: false}));\n\t\t// Redo the terminator match\n\t\treEnd.lastIndex = this.parser.pos;\n\t\tmatch = reEnd.exec(this.parser.source);\n\t\tif(match) {\n\t\t\tthis.parser.pos = reEnd.lastIndex;\n\t\t\t// Add a line break if the terminator was a line break\n\t\t\tif(match[2]) {\n\t\t\t\ttree.push({type: \"element\", tag: \"br\"});\n\t\t\t}\n\t\t}\n\t} while(match && !match[1]);\n\t// Return the nodes\n\treturn tree;\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/hardlinebreaks.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/heading.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/heading.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for headings\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"heading\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /(!{1,6})/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get all the details of the match\n\tvar headingLevel = this.match[1].length;\n\t// Move past the !s\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse any classes, whitespace and then the heading itself\n\tvar classes = this.parser.parseClasses();\n\tthis.parser.skipWhitespace({treatNewlinesAsNonWhitespace: true});\n\tvar tree = this.parser.parseInlineRun(/(\\r?\\n)/mg);\n\t// Return the heading\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"h\" + headingLevel, \n\t\tattributes: {\n\t\t\t\"class\": {type: \"string\", value: classes.join(\" \")}\n\t\t},\n\t\tchildren: tree\n\t}];\n};\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/heading.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/horizrule.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/horizrule.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for rules. For example:\n\n```\n---\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"horizrule\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /-{3,}\\r?(?:\\n|$)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\treturn [{type: \"element\", tag: \"hr\"}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/horizrule.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/html.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/html.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki rule for HTML elements and widgets. For example:\n\n{{{\n<aside>\nThis is an HTML5 aside element\n</aside>\n\n<$slider target=\"MyTiddler\">\nThis is a widget invocation\n</$slider>\n\n}}}\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"html\";\nexports.types = {inline: true, block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n};\n\nexports.findNextMatch = function(startPos) {\n\t// Find the next tag\n\tthis.nextTag = this.findNextTag(this.parser.source,startPos,{\n\t\trequireLineBreak: this.is.block\n\t});\n\treturn this.nextTag ? this.nextTag.start : undefined;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Retrieve the most recent match so that recursive calls don't overwrite it\n\tvar tag = this.nextTag;\n\tthis.nextTag = null;\n\t// Advance the parser position to past the tag\n\tthis.parser.pos = tag.end;\n\t// Check for an immediately following double linebreak\n\tvar hasLineBreak = !tag.isSelfClosing && !!$tw.utils.parseTokenRegExp(this.parser.source,this.parser.pos,/([^\\S\\n\\r]*\\r?\\n(?:[^\\S\\n\\r]*\\r?\\n|$))/g);\n\t// Set whether we're in block mode\n\ttag.isBlock = this.is.block || hasLineBreak;\n\t// Parse the body if we need to\n\tif(!tag.isSelfClosing && $tw.config.htmlVoidElements.indexOf(tag.tag) === -1) {\n\t\t\tvar reEndString = \"</\" + $tw.utils.escapeRegExp(tag.tag) + \">\",\n\t\t\t\treEnd = new RegExp(\"(\" + reEndString + \")\",\"mg\");\n\t\tif(hasLineBreak) {\n\t\t\ttag.children = this.parser.parseBlocks(reEndString);\n\t\t} else {\n\t\t\ttag.children = this.parser.parseInlineRun(reEnd);\n\t\t}\n\t\treEnd.lastIndex = this.parser.pos;\n\t\tvar endMatch = reEnd.exec(this.parser.source);\n\t\tif(endMatch && endMatch.index === this.parser.pos) {\n\t\t\tthis.parser.pos = endMatch.index + endMatch[0].length;\n\t\t}\n\t}\n\t// Return the tag\n\treturn [tag];\n};\n\n/*\nLook for an HTML tag. Returns null if not found, otherwise returns {type: \"element\", name:, attributes: [], isSelfClosing:, start:, end:,}\n*/\nexports.parseTag = function(source,pos,options) {\n\toptions = options || {};\n\tvar token,\n\t\tnode = {\n\t\t\ttype: \"element\",\n\t\t\tstart: pos,\n\t\t\tattributes: {}\n\t\t};\n\t// Define our regexps\n\tvar reTagName = /([a-zA-Z0-9\\-\\$]+)/g;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for a less than sign\n\ttoken = $tw.utils.parseTokenString(source,pos,\"<\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Get the tag name\n\ttoken = $tw.utils.parseTokenRegExp(source,pos,reTagName);\n\tif(!token) {\n\t\treturn null;\n\t}\n\tnode.tag = token.match[1];\n\tif(node.tag.charAt(0) === \"$\") {\n\t\tnode.type = node.tag.substr(1);\n\t}\n\tpos = token.end;\n\t// Check that the tag is terminated by a space, / or >\n\tif(!$tw.utils.parseWhiteSpace(source,pos) && !(source.charAt(pos) === \"/\") && !(source.charAt(pos) === \">\") ) {\n\t\treturn null;\n\t}\n\t// Process attributes\n\tvar attribute = $tw.utils.parseAttribute(source,pos);\n\twhile(attribute) {\n\t\tnode.attributes[attribute.name] = attribute;\n\t\tpos = attribute.end;\n\t\t// Get the next attribute\n\t\tattribute = $tw.utils.parseAttribute(source,pos);\n\t}\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for a closing slash\n\ttoken = $tw.utils.parseTokenString(source,pos,\"/\");\n\tif(token) {\n\t\tpos = token.end;\n\t\tnode.isSelfClosing = true;\n\t}\n\t// Look for a greater than sign\n\ttoken = $tw.utils.parseTokenString(source,pos,\">\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Check for a required line break\n\tif(options.requireLineBreak) {\n\t\ttoken = $tw.utils.parseTokenRegExp(source,pos,/([^\\S\\n\\r]*\\r?\\n(?:[^\\S\\n\\r]*\\r?\\n|$))/g);\n\t\tif(!token) {\n\t\t\treturn null;\n\t\t}\n\t}\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\nexports.findNextTag = function(source,pos,options) {\n\t// A regexp for finding candidate HTML tags\n\tvar reLookahead = /<([a-zA-Z\\-\\$]+)/g;\n\t// Find the next candidate\n\treLookahead.lastIndex = pos;\n\tvar match = reLookahead.exec(source);\n\twhile(match) {\n\t\t// Try to parse the candidate as a tag\n\t\tvar tag = this.parseTag(source,match.index,options);\n\t\t// Return success\n\t\tif(tag && this.isLegalTag(tag)) {\n\t\t\treturn tag;\n\t\t}\n\t\t// Look for the next match\n\t\treLookahead.lastIndex = match.index + 1;\n\t\tmatch = reLookahead.exec(source);\n\t}\n\t// Failed\n\treturn null;\n};\n\nexports.isLegalTag = function(tag) {\n\t// Widgets are always OK\n\tif(tag.type !== \"element\") {\n\t\treturn true;\n\t// If it's an HTML tag that starts with a dash then it's not legal\n\t} else if(tag.tag.charAt(0) === \"-\") {\n\t\treturn false;\n\t} else {\n\t\t// Otherwise it's OK\n\t\treturn true;\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/html.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/image.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/image.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for embedding images. For example:\n\n```\n[img[http://tiddlywiki.com/fractalveg.jpg]]\n[img width=23 height=24 [http://tiddlywiki.com/fractalveg.jpg]]\n[img width={{!!width}} height={{!!height}} [http://tiddlywiki.com/fractalveg.jpg]]\n[img[Description of image|http://tiddlywiki.com/fractalveg.jpg]]\n[img[TiddlerTitle]]\n[img[Description of image|TiddlerTitle]]\n```\n\nGenerates the `<$image>` widget.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"image\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n};\n\nexports.findNextMatch = function(startPos) {\n\t// Find the next tag\n\tthis.nextImage = this.findNextImage(this.parser.source,startPos);\n\treturn this.nextImage ? this.nextImage.start : undefined;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.nextImage.end;\n\tvar node = {\n\t\ttype: \"image\",\n\t\tattributes: this.nextImage.attributes\n\t};\n\treturn [node];\n};\n\n/*\nFind the next image from the current position\n*/\nexports.findNextImage = function(source,pos) {\n\t// A regexp for finding candidate HTML tags\n\tvar reLookahead = /(\\[img)/g;\n\t// Find the next candidate\n\treLookahead.lastIndex = pos;\n\tvar match = reLookahead.exec(source);\n\twhile(match) {\n\t\t// Try to parse the candidate as a tag\n\t\tvar tag = this.parseImage(source,match.index);\n\t\t// Return success\n\t\tif(tag) {\n\t\t\treturn tag;\n\t\t}\n\t\t// Look for the next match\n\t\treLookahead.lastIndex = match.index + 1;\n\t\tmatch = reLookahead.exec(source);\n\t}\n\t// Failed\n\treturn null;\n};\n\n/*\nLook for an image at the specified position. Returns null if not found, otherwise returns {type: \"image\", attributes: [], isSelfClosing:, start:, end:,}\n*/\nexports.parseImage = function(source,pos) {\n\tvar token,\n\t\tnode = {\n\t\t\ttype: \"image\",\n\t\t\tstart: pos,\n\t\t\tattributes: {}\n\t\t};\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for the `[img`\n\ttoken = $tw.utils.parseTokenString(source,pos,\"[img\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Process attributes\n\tif(source.charAt(pos) !== \"[\") {\n\t\tvar attribute = $tw.utils.parseAttribute(source,pos);\n\t\twhile(attribute) {\n\t\t\tnode.attributes[attribute.name] = attribute;\n\t\t\tpos = attribute.end;\n\t\t\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t\t\tif(source.charAt(pos) !== \"[\") {\n\t\t\t\t// Get the next attribute\n\t\t\t\tattribute = $tw.utils.parseAttribute(source,pos);\n\t\t\t} else {\n\t\t\t\tattribute = null;\n\t\t\t}\n\t\t}\n\t}\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for the `[` after the attributes\n\ttoken = $tw.utils.parseTokenString(source,pos,\"[\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Get the source up to the terminating `]]`\n\ttoken = $tw.utils.parseTokenRegExp(source,pos,/(?:([^|\\]]*?)\\|)?([^\\]]+?)\\]\\]/g);\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\tif(token.match[1]) {\n\t\tnode.attributes.tooltip = {type: \"string\", value: token.match[1].trim()};\n\t}\n\tnode.attributes.source = {type: \"string\", value: (token.match[2] || \"\").trim()};\n\t// Update the end position\n\tnode.end = pos;\n\treturn node;\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/image.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/list.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/list.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for lists. For example:\n\n```\n* This is an unordered list\n* It has two items\n\n# This is a numbered list\n## With a subitem\n# And a third item\n\n; This is a term that is being defined\n: This is the definition of that term\n```\n\nNote that lists can be nested arbitrarily:\n\n```\n#** One\n#* Two\n#** Three\n#**** Four\n#**# Five\n#**## Six\n## Seven\n### Eight\n## Nine\n```\n\nA CSS class can be applied to a list item as follows:\n\n```\n* List item one\n*.active List item two has the class `active`\n* List item three\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"list\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /([\\*#;:>]+)/mg;\n};\n\nvar listTypes = {\n\t\"*\": {listTag: \"ul\", itemTag: \"li\"},\n\t\"#\": {listTag: \"ol\", itemTag: \"li\"},\n\t\";\": {listTag: \"dl\", itemTag: \"dt\"},\n\t\":\": {listTag: \"dl\", itemTag: \"dd\"},\n\t\">\": {listTag: \"blockquote\", itemTag: \"p\"}\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Array of parse tree nodes for the previous row of the list\n\tvar listStack = [];\n\t// Cycle through the items in the list\n\twhile(true) {\n\t\t// Match the list marker\n\t\tvar reMatch = /([\\*#;:>]+)/mg;\n\t\treMatch.lastIndex = this.parser.pos;\n\t\tvar match = reMatch.exec(this.parser.source);\n\t\tif(!match || match.index !== this.parser.pos) {\n\t\t\tbreak;\n\t\t}\n\t\t// Check whether the list type of the top level matches\n\t\tvar listInfo = listTypes[match[0].charAt(0)];\n\t\tif(listStack.length > 0 && listStack[0].tag !== listInfo.listTag) {\n\t\t\tbreak;\n\t\t}\n\t\t// Move past the list marker\n\t\tthis.parser.pos = match.index + match[0].length;\n\t\t// Walk through the list markers for the current row\n\t\tfor(var t=0; t<match[0].length; t++) {\n\t\t\tlistInfo = listTypes[match[0].charAt(t)];\n\t\t\t// Remove any stacked up element if we can't re-use it because the list type doesn't match\n\t\t\tif(listStack.length > t && listStack[t].tag !== listInfo.listTag) {\n\t\t\t\tlistStack.splice(t,listStack.length - t);\n\t\t\t}\n\t\t\t// Construct the list element or reuse the previous one at this level\n\t\t\tif(listStack.length <= t) {\n\t\t\t\tvar listElement = {type: \"element\", tag: listInfo.listTag, children: [\n\t\t\t\t\t{type: \"element\", tag: listInfo.itemTag, children: []}\n\t\t\t\t]};\n\t\t\t\t// Link this list element into the last child item of the parent list item\n\t\t\t\tif(t) {\n\t\t\t\t\tvar prevListItem = listStack[t-1].children[listStack[t-1].children.length-1];\n\t\t\t\t\tprevListItem.children.push(listElement);\n\t\t\t\t}\n\t\t\t\t// Save this element in the stack\n\t\t\t\tlistStack[t] = listElement;\n\t\t\t} else if(t === (match[0].length - 1)) {\n\t\t\t\tlistStack[t].children.push({type: \"element\", tag: listInfo.itemTag, children: []});\n\t\t\t}\n\t\t}\n\t\tif(listStack.length > match[0].length) {\n\t\t\tlistStack.splice(match[0].length,listStack.length - match[0].length);\n\t\t}\n\t\t// Process the body of the list item into the last list item\n\t\tvar lastListChildren = listStack[listStack.length-1].children,\n\t\t\tlastListItem = lastListChildren[lastListChildren.length-1],\n\t\t\tclasses = this.parser.parseClasses();\n\t\tthis.parser.skipWhitespace({treatNewlinesAsNonWhitespace: true});\n\t\tvar tree = this.parser.parseInlineRun(/(\\r?\\n)/mg);\n\t\tlastListItem.children.push.apply(lastListItem.children,tree);\n\t\tif(classes.length > 0) {\n\t\t\t$tw.utils.addClassToParseTreeNode(lastListItem,classes.join(\" \"));\n\t\t}\n\t\t// Consume any whitespace following the list item\n\t\tthis.parser.skipWhitespace();\n\t}\n\t// Return the root element of the list\n\treturn [listStack[0]];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/list.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/macrocallblock.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/macrocallblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki rule for block macro calls\n\n```\n<<name value value2>>\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"macrocallblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /<<([^>\\s]+)(?:\\s*)((?:[^>]|(?:>(?!>)))*?)>>(?:\\r?\\n|$)/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get all the details of the match\n\tvar macroName = this.match[1],\n\t\tparamString = this.match[2];\n\t// Move past the macro call\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar params = [],\n\t\treParam = /\\s*(?:([A-Za-z0-9\\-_]+)\\s*:)?(?:\\s*(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\"|'([^']*)'|\\[\\[([^\\]]*)\\]\\]|([^\"'\\s]+)))/mg,\n\t\tparamMatch = reParam.exec(paramString);\n\twhile(paramMatch) {\n\t\t// Process this parameter\n\t\tvar paramInfo = {\n\t\t\tvalue: paramMatch[2] || paramMatch[3] || paramMatch[4] || paramMatch[5] || paramMatch[6]\n\t\t};\n\t\tif(paramMatch[1]) {\n\t\t\tparamInfo.name = paramMatch[1];\n\t\t}\n\t\tparams.push(paramInfo);\n\t\t// Find the next match\n\t\tparamMatch = reParam.exec(paramString);\n\t}\n\treturn [{\n\t\ttype: \"macrocall\",\n\t\tname: macroName,\n\t\tparams: params,\n\t\tisBlock: true\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/macrocallblock.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/macrocallinline.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/macrocallinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki rule for macro calls\n\n```\n<<name value value2>>\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"macrocallinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /<<([^\\s>]+)\\s*([\\s\\S]*?)>>/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get all the details of the match\n\tvar macroName = this.match[1],\n\t\tparamString = this.match[2];\n\t// Move past the macro call\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\tvar params = [],\n\t\treParam = /\\s*(?:([A-Za-z0-9\\-_]+)\\s*:)?(?:\\s*(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\"|'([^']*)'|\\[\\[([^\\]]*)\\]\\]|([^\"'\\s]+)))/mg,\n\t\tparamMatch = reParam.exec(paramString);\n\twhile(paramMatch) {\n\t\t// Process this parameter\n\t\tvar paramInfo = {\n\t\t\tvalue: paramMatch[2] || paramMatch[3] || paramMatch[4] || paramMatch[5]|| paramMatch[6]\n\t\t};\n\t\tif(paramMatch[1]) {\n\t\t\tparamInfo.name = paramMatch[1];\n\t\t}\n\t\tparams.push(paramInfo);\n\t\t// Find the next match\n\t\tparamMatch = reParam.exec(paramString);\n\t}\n\treturn [{\n\t\ttype: \"macrocall\",\n\t\tname: macroName,\n\t\tparams: params\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/macrocallinline.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/macrodef.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/macrodef.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki pragma rule for macro definitions\n\n```\n\\define name(param:defaultvalue,param2:defaultvalue)\ndefinition text, including $param$ markers\n\\end\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"macrodef\";\nexports.types = {pragma: true};\n\n/*\nInstantiate parse rule\n*/\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /^\\\\define\\s+([^(\\s]+)\\(\\s*([^)]*)\\)(\\s*\\r?\\n)?/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Move past the macro name and parameters\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse the parameters\n\tvar paramString = this.match[2],\n\t\tparams = [];\n\tif(paramString !== \"\") {\n\t\tvar reParam = /\\s*([A-Za-z0-9\\-_]+)(?:\\s*:\\s*(?:\"\"\"([\\s\\S]*?)\"\"\"|\"([^\"]*)\"|'([^']*)'|\\[\\[([^\\]]*)\\]\\]|([^\"'\\s]+)))?/mg,\n\t\t\tparamMatch = reParam.exec(paramString);\n\t\twhile(paramMatch) {\n\t\t\t// Save the parameter details\n\t\t\tvar paramInfo = {name: paramMatch[1]},\n\t\t\t\tdefaultValue = paramMatch[2] || paramMatch[3] || paramMatch[4] || paramMatch[5] || paramMatch[6];\n\t\t\tif(defaultValue) {\n\t\t\t\tparamInfo[\"default\"] = defaultValue;\n\t\t\t}\n\t\t\tparams.push(paramInfo);\n\t\t\t// Look for the next parameter\n\t\t\tparamMatch = reParam.exec(paramString);\n\t\t}\n\t}\n\t// Is this a multiline definition?\n\tvar reEnd;\n\tif(this.match[3]) {\n\t\t// If so, the end of the body is marked with \\end\n\t\treEnd = /(\\r?\\n\\\\end[^\\S\\n\\r]*(?:$|\\r?\\n))/mg;\n\t} else {\n\t\t// Otherwise, the end of the definition is marked by the end of the line\n\t\treEnd = /($|\\r?\\n)/mg;\n\t\t// Move past any whitespace\n\t\tthis.parser.pos = $tw.utils.skipWhiteSpace(this.parser.source,this.parser.pos);\n\t}\n\t// Find the end of the definition\n\treEnd.lastIndex = this.parser.pos;\n\tvar text,\n\t\tendMatch = reEnd.exec(this.parser.source);\n\tif(endMatch) {\n\t\ttext = this.parser.source.substring(this.parser.pos,endMatch.index);\n\t\tthis.parser.pos = endMatch.index + endMatch[0].length;\n\t} else {\n\t\t// We didn't find the end of the definition, so we'll make it blank\n\t\ttext = \"\";\n\t}\n\t// Save the macro definition\n\treturn [{\n\t\ttype: \"set\",\n\t\tattributes: {\n\t\t\tname: {type: \"string\", value: this.match[1]},\n\t\t\tvalue: {type: \"string\", value: text}\n\t\t},\n\t\tchildren: [],\n\t\tparams: params\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/macrodef.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/prettyextlink.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/prettyextlink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for external links. For example:\n\n```\n[ext[http://tiddlywiki.com/fractalveg.jpg]]\n[ext[Tooltip|http://tiddlywiki.com/fractalveg.jpg]]\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"prettyextlink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n};\n\nexports.findNextMatch = function(startPos) {\n\t// Find the next tag\n\tthis.nextLink = this.findNextLink(this.parser.source,startPos);\n\treturn this.nextLink ? this.nextLink.start : undefined;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.nextLink.end;\n\treturn [this.nextLink];\n};\n\n/*\nFind the next link from the current position\n*/\nexports.findNextLink = function(source,pos) {\n\t// A regexp for finding candidate links\n\tvar reLookahead = /(\\[ext\\[)/g;\n\t// Find the next candidate\n\treLookahead.lastIndex = pos;\n\tvar match = reLookahead.exec(source);\n\twhile(match) {\n\t\t// Try to parse the candidate as a link\n\t\tvar link = this.parseLink(source,match.index);\n\t\t// Return success\n\t\tif(link) {\n\t\t\treturn link;\n\t\t}\n\t\t// Look for the next match\n\t\treLookahead.lastIndex = match.index + 1;\n\t\tmatch = reLookahead.exec(source);\n\t}\n\t// Failed\n\treturn null;\n};\n\n/*\nLook for an link at the specified position. Returns null if not found, otherwise returns {type: \"element\", tag: \"a\", attributes: [], isSelfClosing:, start:, end:,}\n*/\nexports.parseLink = function(source,pos) {\n\tvar token,\n\t\ttextNode = {\n\t\t\ttype: \"text\"\n\t\t},\n\t\tnode = {\n\t\t\ttype: \"element\",\n\t\t\ttag: \"a\",\n\t\t\tstart: pos,\n\t\t\tattributes: {\n\t\t\t\t\"class\": {type: \"string\", value: \"tc-tiddlylink-external\"},\n\t\t\t},\n\t\t\tchildren: [textNode]\n\t\t};\n\t// Skip whitespace\n\tpos = $tw.utils.skipWhiteSpace(source,pos);\n\t// Look for the `[ext[`\n\ttoken = $tw.utils.parseTokenString(source,pos,\"[ext[\");\n\tif(!token) {\n\t\treturn null;\n\t}\n\tpos = token.end;\n\t// Look ahead for the terminating `]]`\n\tvar closePos = source.indexOf(\"]]\",pos);\n\tif(closePos === -1) {\n\t\treturn null;\n\t}\n\t// Look for a `|` separating the tooltip\n\tvar splitPos = source.indexOf(\"|\",pos);\n\tif(splitPos === -1 || splitPos > closePos) {\n\t\tsplitPos = null;\n\t}\n\t// Pull out the tooltip and URL\n\tvar tooltip, URL;\n\tif(splitPos) {\n\t\tURL = source.substring(splitPos + 1,closePos).trim();\n\t\ttextNode.text = source.substring(pos,splitPos).trim();\n\t} else {\n\t\tURL = source.substring(pos,closePos).trim();\n\t\ttextNode.text = URL;\n\t}\n\tnode.attributes.href = {type: \"string\", value: URL};\n\tnode.attributes.target = {type: \"string\", value: \"_blank\"};\n\tnode.attributes.rel = {type: \"string\", value: \"noopener noreferrer\"};\n\t// Update the end position\n\tnode.end = closePos + 2;\n\treturn node;\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/prettyextlink.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/prettylink.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/prettylink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for pretty links. For example:\n\n```\n[[Introduction]]\n\n[[Link description|TiddlerTitle]]\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"prettylink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\[\\[(.*?)(?:\\|(.*?))?\\]\\]/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Process the link\n\tvar text = this.match[1],\n\t\tlink = this.match[2] || text;\n\tif($tw.utils.isLinkExternal(link)) {\n\t\treturn [{\n\t\t\ttype: \"element\",\n\t\t\ttag: \"a\",\n\t\t\tattributes: {\n\t\t\t\thref: {type: \"string\", value: link},\n\t\t\t\t\"class\": {type: \"string\", value: \"tc-tiddlylink-external\"},\n\t\t\t\ttarget: {type: \"string\", value: \"_blank\"},\n\t\t\t\trel: {type: \"string\", value: \"noopener noreferrer\"}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\", text: text\n\t\t\t}]\n\t\t}];\n\t} else {\n\t\treturn [{\n\t\t\ttype: \"link\",\n\t\t\tattributes: {\n\t\t\t\tto: {type: \"string\", value: link}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\", text: text\n\t\t\t}]\n\t\t}];\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/prettylink.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/quoteblock.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/quoteblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for quote blocks. For example:\n\n```\n\t<<<.optionalClass(es) optional cited from\n\ta quote\n\t<<<\n\t\n\t<<<.optionalClass(es)\n\ta quote\n\t<<< optional cited from\n```\n\nQuotes can be quoted by putting more <s\n\n```\n\t<<<\n\tQuote Level 1\n\t\n\t<<<<\n\tQuoteLevel 2\n\t<<<<\n\t\n\t<<<\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"quoteblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /(<<<+)/mg;\n};\n\nexports.parse = function() {\n\tvar classes = [\"tc-quote\"];\n\t// Get all the details of the match\n\tvar reEndString = \"^\" + this.match[1] + \"(?!<)\";\n\t// Move past the <s\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t\n\t// Parse any classes, whitespace and then the optional cite itself\n\tclasses.push.apply(classes, this.parser.parseClasses());\n\tthis.parser.skipWhitespace({treatNewlinesAsNonWhitespace: true});\n\tvar cite = this.parser.parseInlineRun(/(\\r?\\n)/mg);\n\t// before handling the cite, parse the body of the quote\n\tvar tree= this.parser.parseBlocks(reEndString);\n\t// If we got a cite, put it before the text\n\tif(cite.length > 0) {\n\t\ttree.unshift({\n\t\t\ttype: \"element\",\n\t\t\ttag: \"cite\",\n\t\t\tchildren: cite\n\t\t});\n\t}\n\t// Parse any optional cite\n\tthis.parser.skipWhitespace({treatNewlinesAsNonWhitespace: true});\n\tcite = this.parser.parseInlineRun(/(\\r?\\n)/mg);\n\t// If we got a cite, push it\n\tif(cite.length > 0) {\n\t\ttree.push({\n\t\t\ttype: \"element\",\n\t\t\ttag: \"cite\",\n\t\t\tchildren: cite\n\t\t});\n\t}\n\t// Return the blockquote element\n\treturn [{\n\t\ttype: \"element\",\n\t\ttag: \"blockquote\",\n\t\tattributes: {\n\t\t\tclass: { type: \"string\", value: classes.join(\" \") },\n\t\t},\n\t\tchildren: tree\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/quoteblock.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/rules.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/rules.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki pragma rule for rules specifications\n\n```\n\\rules except ruleone ruletwo rulethree\n\\rules only ruleone ruletwo rulethree\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"rules\";\nexports.types = {pragma: true};\n\n/*\nInstantiate parse rule\n*/\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /^\\\\rules[^\\S\\n]/mg;\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Move past the pragma invocation\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse whitespace delimited tokens terminated by a line break\n\tvar reMatch = /[^\\S\\n]*(\\S+)|(\\r?\\n)/mg,\n\t\ttokens = [];\n\treMatch.lastIndex = this.parser.pos;\n\tvar match = reMatch.exec(this.parser.source);\n\twhile(match && match.index === this.parser.pos) {\n\t\tthis.parser.pos = reMatch.lastIndex;\n\t\t// Exit if we've got the line break\n\t\tif(match[2]) {\n\t\t\tbreak;\n\t\t}\n\t\t// Process the token\n\t\tif(match[1]) {\n\t\t\ttokens.push(match[1]);\n\t\t}\n\t\t// Match the next token\n\t\tmatch = reMatch.exec(this.parser.source);\n\t}\n\t// Process the tokens\n\tif(tokens.length > 0) {\n\t\tthis.parser.amendRules(tokens[0],tokens.slice(1));\n\t}\n\t// No parse tree nodes to return\n\treturn [];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/rules.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/styleblock.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/styleblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for assigning styles and classes to paragraphs and other blocks. For example:\n\n```\n@@.myClass\n@@background-color:red;\nThis paragraph will have the CSS class `myClass`.\n\n* The `<ul>` around this list will also have the class `myClass`\n* List item 2\n\n@@\n```\n\nNote that classes and styles can be mixed subject to the rule that styles must precede classes. For example\n\n```\n@@.myFirstClass.mySecondClass\n@@width:100px;.myThirdClass\nThis is a paragraph\n@@\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"styleblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /@@((?:[^\\.\\r\\n\\s:]+:[^\\r\\n;]+;)+)?(?:\\.([^\\r\\n\\s]+))?\\r?\\n/mg;\n};\n\nexports.parse = function() {\n\tvar reEndString = \"^@@(?:\\\\r?\\\\n)?\";\n\tvar classes = [], styles = [];\n\tdo {\n\t\t// Get the class and style\n\t\tif(this.match[1]) {\n\t\t\tstyles.push(this.match[1]);\n\t\t}\n\t\tif(this.match[2]) {\n\t\t\tclasses.push(this.match[2].split(\".\").join(\" \"));\n\t\t}\n\t\t// Move past the match\n\t\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t\t// Look for another line of classes and styles\n\t\tthis.match = this.matchRegExp.exec(this.parser.source);\n\t} while(this.match && this.match.index === this.parser.pos);\n\t// Parse the body\n\tvar tree = this.parser.parseBlocks(reEndString);\n\tfor(var t=0; t<tree.length; t++) {\n\t\tif(classes.length > 0) {\n\t\t\t$tw.utils.addClassToParseTreeNode(tree[t],classes.join(\" \"));\n\t\t}\n\t\tif(styles.length > 0) {\n\t\t\t$tw.utils.addAttributeToParseTreeNode(tree[t],\"style\",styles.join(\"\"));\n\t\t}\n\t}\n\treturn tree;\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/styleblock.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/styleinline.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/styleinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for assigning styles and classes to inline runs. For example:\n\n```\n@@.myClass This is some text with a class@@\n@@background-color:red;This is some text with a background colour@@\n@@width:100px;.myClass This is some text with a class and a width@@\n```\n\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"styleinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /@@((?:[^\\.\\r\\n\\s:]+:[^\\r\\n;]+;)+)?(\\.(?:[^\\r\\n\\s]+)\\s+)?/mg;\n};\n\nexports.parse = function() {\n\tvar reEnd = /@@/g;\n\t// Get the styles and class\n\tvar stylesString = this.match[1],\n\t\tclassString = this.match[2] ? this.match[2].split(\".\").join(\" \") : undefined;\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Parse the run up to the terminator\n\tvar tree = this.parser.parseInlineRun(reEnd,{eatTerminator: true});\n\t// Return the classed span\n\tvar node = {\n\t\ttype: \"element\",\n\t\ttag: \"span\",\n\t\tattributes: {\n\t\t\t\"class\": {type: \"string\", value: \"tc-inline-style\"}\n\t\t},\n\t\tchildren: tree\n\t};\n\tif(classString) {\n\t\t$tw.utils.addClassToParseTreeNode(node,classString);\n\t}\n\tif(stylesString) {\n\t\t$tw.utils.addAttributeToParseTreeNode(node,\"style\",stylesString);\n\t}\n\treturn [node];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/styleinline.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/syslink.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/syslink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for system tiddler links.\nCan be suppressed preceding them with `~`.\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"syslink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = new RegExp(\n\t\t\"~?\\\\$:\\\\/[\" +\n\t\t$tw.config.textPrimitives.anyLetter.substr(1,$tw.config.textPrimitives.anyLetter.length - 2) +\n\t\t\"\\/._-]+\",\n\t\t\"mg\"\n\t);\n};\n\nexports.parse = function() {\n\tvar match = this.match[0];\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Create the link unless it is suppressed\n\tif(match.substr(0,1) === \"~\") {\n\t\treturn [{type: \"text\", text: match.substr(1)}];\n\t} else {\n\t\treturn [{\n\t\t\ttype: \"link\",\n\t\t\tattributes: {\n\t\t\t\tto: {type: \"string\", value: match}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\",\n\t\t\t\ttext: match\n\t\t\t}]\n\t\t}];\n\t}\n};\n\n})();",
"title": "$:/core/modules/parsers/wikiparser/rules/syslink.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/table.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/table.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text block rule for tables.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"table\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /^\\|(?:[^\\n]*)\\|(?:[fhck]?)\\r?(?:\\n|$)/mg;\n};\n\nvar processRow = function(prevColumns) {\n\tvar cellRegExp = /(?:\\|([^\\n\\|]*)\\|)|(\\|[fhck]?\\r?(?:\\n|$))/mg,\n\t\tcellTermRegExp = /((?:\\x20*)\\|)/mg,\n\t\ttree = [],\n\t\tcol = 0,\n\t\tcolSpanCount = 1,\n\t\tprevCell,\n\t\tvAlign;\n\t// Match a single cell\n\tcellRegExp.lastIndex = this.parser.pos;\n\tvar cellMatch = cellRegExp.exec(this.parser.source);\n\twhile(cellMatch && cellMatch.index === this.parser.pos) {\n\t\tif(cellMatch[1] === \"~\") {\n\t\t\t// Rowspan\n\t\t\tvar last = prevColumns[col];\n\t\t\tif(last) {\n\t\t\t\tlast.rowSpanCount++;\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(last.element,\"rowspan\",last.rowSpanCount);\n\t\t\t\tvAlign = $tw.utils.getAttributeValueFromParseTreeNode(last.element,\"valign\",\"center\");\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(last.element,\"valign\",vAlign);\n\t\t\t\tif(colSpanCount > 1) {\n\t\t\t\t\t$tw.utils.addAttributeToParseTreeNode(last.element,\"colspan\",colSpanCount);\n\t\t\t\t\tcolSpanCount = 1;\n\t\t\t\t}\n\t\t\t}\n\t\t\t// Move to just before the `|` terminating the cell\n\t\t\tthis.parser.pos = cellRegExp.lastIndex - 1;\n\t\t} else if(cellMatch[1] === \">\") {\n\t\t\t// Colspan\n\t\t\tcolSpanCount++;\n\t\t\t// Move to just before the `|` terminating the cell\n\t\t\tthis.parser.pos = cellRegExp.lastIndex - 1;\n\t\t} else if(cellMatch[1] === \"<\" && prevCell) {\n\t\t\tcolSpanCount = 1 + $tw.utils.getAttributeValueFromParseTreeNode(prevCell,\"colspan\",1);\n\t\t\t$tw.utils.addAttributeToParseTreeNode(prevCell,\"colspan\",colSpanCount);\n\t\t\tcolSpanCount = 1;\n\t\t\t// Move to just before the `|` terminating the cell\n\t\t\tthis.parser.pos = cellRegExp.lastIndex - 1;\n\t\t} else if(cellMatch[2]) {\n\t\t\t// End of row\n\t\t\tif(prevCell && colSpanCount > 1) {\n\t\t\t\tif(prevCell.attributes && prevCell.attributes && prevCell.attributes.colspan) {\n\t\t\t\t\t\tcolSpanCount += prevCell.attributes.colspan.value;\n\t\t\t\t} else {\n\t\t\t\t\tcolSpanCount -= 1;\n\t\t\t\t}\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(prevCell,\"colspan\",colSpanCount);\n\t\t\t}\n\t\t\tthis.parser.pos = cellRegExp.lastIndex - 1;\n\t\t\tbreak;\n\t\t} else {\n\t\t\t// For ordinary cells, step beyond the opening `|`\n\t\t\tthis.parser.pos++;\n\t\t\t// Look for a space at the start of the cell\n\t\t\tvar spaceLeft = false;\n\t\t\tvAlign = null;\n\t\t\tif(this.parser.source.substr(this.parser.pos).search(/^\\^([^\\^]|\\^\\^)/) === 0) {\n\t\t\t\tvAlign = \"top\";\n\t\t\t} else if(this.parser.source.substr(this.parser.pos).search(/^,([^,]|,,)/) === 0) {\n\t\t\t\tvAlign = \"bottom\";\n\t\t\t}\n\t\t\tif(vAlign) {\n\t\t\t\tthis.parser.pos++;\n\t\t\t}\n\t\t\tvar chr = this.parser.source.substr(this.parser.pos,1);\n\t\t\twhile(chr === \" \") {\n\t\t\t\tspaceLeft = true;\n\t\t\t\tthis.parser.pos++;\n\t\t\t\tchr = this.parser.source.substr(this.parser.pos,1);\n\t\t\t}\n\t\t\t// Check whether this is a heading cell\n\t\t\tvar cell;\n\t\t\tif(chr === \"!\") {\n\t\t\t\tthis.parser.pos++;\n\t\t\t\tcell = {type: \"element\", tag: \"th\", children: []};\n\t\t\t} else {\n\t\t\t\tcell = {type: \"element\", tag: \"td\", children: []};\n\t\t\t}\n\t\t\ttree.push(cell);\n\t\t\t// Record information about this cell\n\t\t\tprevCell = cell;\n\t\t\tprevColumns[col] = {rowSpanCount:1,element:cell};\n\t\t\t// Check for a colspan\n\t\t\tif(colSpanCount > 1) {\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(cell,\"colspan\",colSpanCount);\n\t\t\t\tcolSpanCount = 1;\n\t\t\t}\n\t\t\t// Parse the cell\n\t\t\tcell.children = this.parser.parseInlineRun(cellTermRegExp,{eatTerminator: true});\n\t\t\t// Set the alignment for the cell\n\t\t\tif(vAlign) {\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(cell,\"valign\",vAlign);\n\t\t\t}\n\t\t\tif(this.parser.source.substr(this.parser.pos - 2,1) === \" \") { // spaceRight\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(cell,\"align\",spaceLeft ? \"center\" : \"left\");\n\t\t\t} else if(spaceLeft) {\n\t\t\t\t$tw.utils.addAttributeToParseTreeNode(cell,\"align\",\"right\");\n\t\t\t}\n\t\t\t// Move back to the closing `|`\n\t\t\tthis.parser.pos--;\n\t\t}\n\t\tcol++;\n\t\tcellRegExp.lastIndex = this.parser.pos;\n\t\tcellMatch = cellRegExp.exec(this.parser.source);\n\t}\n\treturn tree;\n};\n\nexports.parse = function() {\n\tvar rowContainerTypes = {\"c\":\"caption\", \"h\":\"thead\", \"\":\"tbody\", \"f\":\"tfoot\"},\n\t\ttable = {type: \"element\", tag: \"table\", children: []},\n\t\trowRegExp = /^\\|([^\\n]*)\\|([fhck]?)\\r?(?:\\n|$)/mg,\n\t\trowTermRegExp = /(\\|(?:[fhck]?)\\r?(?:\\n|$))/mg,\n\t\tprevColumns = [],\n\t\tcurrRowType,\n\t\trowContainer,\n\t\trowCount = 0;\n\t// Match the row\n\trowRegExp.lastIndex = this.parser.pos;\n\tvar rowMatch = rowRegExp.exec(this.parser.source);\n\twhile(rowMatch && rowMatch.index === this.parser.pos) {\n\t\tvar rowType = rowMatch[2];\n\t\t// Check if it is a class assignment\n\t\tif(rowType === \"k\") {\n\t\t\t$tw.utils.addClassToParseTreeNode(table,rowMatch[1]);\n\t\t\tthis.parser.pos = rowMatch.index + rowMatch[0].length;\n\t\t} else {\n\t\t\t// Otherwise, create a new row if this one is of a different type\n\t\t\tif(rowType !== currRowType) {\n\t\t\t\trowContainer = {type: \"element\", tag: rowContainerTypes[rowType], children: []};\n\t\t\t\ttable.children.push(rowContainer);\n\t\t\t\tcurrRowType = rowType;\n\t\t\t}\n\t\t\t// Is this a caption row?\n\t\t\tif(currRowType === \"c\") {\n\t\t\t\t// If so, move past the opening `|` of the row\n\t\t\t\tthis.parser.pos++;\n\t\t\t\t// Move the caption to the first row if it isn't already\n\t\t\t\tif(table.children.length !== 1) {\n\t\t\t\t\ttable.children.pop(); // Take rowContainer out of the children array\n\t\t\t\t\ttable.children.splice(0,0,rowContainer); // Insert it at the bottom\t\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\t// Set the alignment - TODO: figure out why TW did this\n//\t\t\t\trowContainer.attributes.align = rowCount === 0 ? \"top\" : \"bottom\";\n\t\t\t\t// Parse the caption\n\t\t\t\trowContainer.children = this.parser.parseInlineRun(rowTermRegExp,{eatTerminator: true});\n\t\t\t} else {\n\t\t\t\t// Create the row\n\t\t\t\tvar theRow = {type: \"element\", tag: \"tr\", children: []};\n\t\t\t\t$tw.utils.addClassToParseTreeNode(theRow,rowCount%2 ? \"oddRow\" : \"evenRow\");\n\t\t\t\trowContainer.children.push(theRow);\n\t\t\t\t// Process the row\n\t\t\t\ttheRow.children = processRow.call(this,prevColumns);\n\t\t\t\tthis.parser.pos = rowMatch.index + rowMatch[0].length;\n\t\t\t\t// Increment the row count\n\t\t\t\trowCount++;\n\t\t\t}\n\t\t}\n\t\trowMatch = rowRegExp.exec(this.parser.source);\n\t}\n\treturn [table];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/table.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/transcludeblock.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/transcludeblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for block-level transclusion. For example:\n\n```\n{{MyTiddler}}\n{{MyTiddler||TemplateTitle}}\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"transcludeblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\{\\{([^\\{\\}\\|]*)(?:\\|\\|([^\\|\\{\\}]+))?\\}\\}(?:\\r?\\n|$)/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Get the match details\n\tvar template = $tw.utils.trim(this.match[2]),\n\t\ttextRef = $tw.utils.trim(this.match[1]);\n\t// Prepare the transclude widget\n\tvar transcludeNode = {\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {},\n\t\t\tisBlock: true\n\t\t};\n\t// Prepare the tiddler widget\n\tvar tr, targetTitle, targetField, targetIndex, tiddlerNode;\n\tif(textRef) {\n\t\ttr = $tw.utils.parseTextReference(textRef);\n\t\ttargetTitle = tr.title;\n\t\ttargetField = tr.field;\n\t\ttargetIndex = tr.index;\n\t\ttiddlerNode = {\n\t\t\ttype: \"tiddler\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: targetTitle}\n\t\t\t},\n\t\t\tisBlock: true,\n\t\t\tchildren: [transcludeNode]\n\t\t};\n\t}\n\tif(template) {\n\t\ttranscludeNode.attributes.tiddler = {type: \"string\", value: template};\n\t\tif(textRef) {\n\t\t\treturn [tiddlerNode];\n\t\t} else {\n\t\t\treturn [transcludeNode];\n\t\t}\n\t} else {\n\t\tif(textRef) {\n\t\t\ttranscludeNode.attributes.tiddler = {type: \"string\", value: targetTitle};\n\t\t\tif(targetField) {\n\t\t\t\ttranscludeNode.attributes.field = {type: \"string\", value: targetField};\n\t\t\t}\n\t\t\tif(targetIndex) {\n\t\t\t\ttranscludeNode.attributes.index = {type: \"string\", value: targetIndex};\n\t\t\t}\n\t\t\treturn [tiddlerNode];\n\t\t} else {\n\t\t\treturn [transcludeNode];\n\t\t}\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/transcludeblock.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/transcludeinline.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/transcludeinline.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for inline-level transclusion. For example:\n\n```\n{{MyTiddler}}\n{{MyTiddler||TemplateTitle}}\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"transcludeinline\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\{\\{([^\\{\\}\\|]*)(?:\\|\\|([^\\|\\{\\}]+))?\\}\\}/mg;\n};\n\nexports.parse = function() {\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Get the match details\n\tvar template = $tw.utils.trim(this.match[2]),\n\t\ttextRef = $tw.utils.trim(this.match[1]);\n\t// Prepare the transclude widget\n\tvar transcludeNode = {\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {}\n\t\t};\n\t// Prepare the tiddler widget\n\tvar tr, targetTitle, targetField, targetIndex, tiddlerNode;\n\tif(textRef) {\n\t\ttr = $tw.utils.parseTextReference(textRef);\n\t\ttargetTitle = tr.title;\n\t\ttargetField = tr.field;\n\t\ttargetIndex = tr.index;\n\t\ttiddlerNode = {\n\t\t\ttype: \"tiddler\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {type: \"string\", value: targetTitle}\n\t\t\t},\n\t\t\tchildren: [transcludeNode]\n\t\t};\n\t}\n\tif(template) {\n\t\ttranscludeNode.attributes.tiddler = {type: \"string\", value: template};\n\t\tif(textRef) {\n\t\t\treturn [tiddlerNode];\n\t\t} else {\n\t\t\treturn [transcludeNode];\n\t\t}\n\t} else {\n\t\tif(textRef) {\n\t\t\ttranscludeNode.attributes.tiddler = {type: \"string\", value: targetTitle};\n\t\t\tif(targetField) {\n\t\t\t\ttranscludeNode.attributes.field = {type: \"string\", value: targetField};\n\t\t\t}\n\t\t\tif(targetIndex) {\n\t\t\t\ttranscludeNode.attributes.index = {type: \"string\", value: targetIndex};\n\t\t\t}\n\t\t\treturn [tiddlerNode];\n\t\t} else {\n\t\t\treturn [transcludeNode];\n\t\t}\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/transcludeinline.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/typedblock.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/typedblock.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text rule for typed blocks. For example:\n\n```\n$$$.js\nThis will be rendered as JavaScript\n$$$\n\n$$$.svg\n<svg xmlns=\"http://www.w3.org/2000/svg\" width=\"150\" height=\"100\">\n <circle cx=\"100\" cy=\"50\" r=\"40\" stroke=\"black\" stroke-width=\"2\" fill=\"red\" />\n</svg>\n$$$\n\n$$$text/vnd.tiddlywiki>text/html\nThis will be rendered as an //HTML representation// of WikiText\n$$$\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.name = \"typedblock\";\nexports.types = {block: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = /\\$\\$\\$([^ >\\r\\n]*)(?: *> *([^ \\r\\n]+))?\\r?\\n/mg;\n};\n\nexports.parse = function() {\n\tvar reEnd = /\\r?\\n\\$\\$\\$\\r?(?:\\n|$)/mg;\n\t// Save the type\n\tvar parseType = this.match[1],\n\t\trenderType = this.match[2];\n\t// Move past the match\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// Look for the end of the block\n\treEnd.lastIndex = this.parser.pos;\n\tvar match = reEnd.exec(this.parser.source),\n\t\ttext;\n\t// Process the block\n\tif(match) {\n\t\ttext = this.parser.source.substring(this.parser.pos,match.index);\n\t\tthis.parser.pos = match.index + match[0].length;\n\t} else {\n\t\ttext = this.parser.source.substr(this.parser.pos);\n\t\tthis.parser.pos = this.parser.sourceLength;\n\t}\n\t// Parse the block according to the specified type\n\tvar parser = this.parser.wiki.parseText(parseType,text,{defaultType: \"text/plain\"});\n\t// If there's no render type, just return the parse tree\n\tif(!renderType) {\n\t\treturn parser.tree;\n\t} else {\n\t\t// Otherwise, render to the rendertype and return in a <PRE> tag\n\t\tvar widgetNode = this.parser.wiki.makeWidget(parser),\n\t\t\tcontainer = $tw.fakeDocument.createElement(\"div\");\n\t\twidgetNode.render(container,null);\n\t\ttext = renderType === \"text/html\" ? container.innerHTML : container.textContent;\n\t\treturn [{\n\t\t\ttype: \"element\",\n\t\t\ttag: \"pre\",\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\",\n\t\t\t\ttext: text\n\t\t\t}]\n\t\t}];\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/typedblock.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/rules/wikilink.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/wikilink.js\ntype: application/javascript\nmodule-type: wikirule\n\nWiki text inline rule for wiki links. For example:\n\n```\nAWikiLink\nAnotherLink\n~SuppressedLink\n```\n\nPrecede a camel case word with `~` to prevent it from being recognised as a link.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.name = \"wikilink\";\nexports.types = {inline: true};\n\nexports.init = function(parser) {\n\tthis.parser = parser;\n\t// Regexp to match\n\tthis.matchRegExp = new RegExp($tw.config.textPrimitives.unWikiLink + \"?\" + $tw.config.textPrimitives.wikiLink,\"mg\");\n};\n\n/*\nParse the most recent match\n*/\nexports.parse = function() {\n\t// Get the details of the match\n\tvar linkText = this.match[0];\n\t// Move past the macro call\n\tthis.parser.pos = this.matchRegExp.lastIndex;\n\t// If the link starts with the unwikilink character then just output it as plain text\n\tif(linkText.substr(0,1) === $tw.config.textPrimitives.unWikiLink) {\n\t\treturn [{type: \"text\", text: linkText.substr(1)}];\n\t}\n\t// If the link has been preceded with a blocked letter then don't treat it as a link\n\tif(this.match.index > 0) {\n\t\tvar preRegExp = new RegExp($tw.config.textPrimitives.blockPrefixLetters,\"mg\");\n\t\tpreRegExp.lastIndex = this.match.index-1;\n\t\tvar preMatch = preRegExp.exec(this.parser.source);\n\t\tif(preMatch && preMatch.index === this.match.index-1) {\n\t\t\treturn [{type: \"text\", text: linkText}];\n\t\t}\n\t}\n\treturn [{\n\t\ttype: \"link\",\n\t\tattributes: {\n\t\t\tto: {type: \"string\", value: linkText}\n\t\t},\n\t\tchildren: [{\n\t\t\ttype: \"text\",\n\t\t\ttext: linkText\n\t\t}]\n\t}];\n};\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/wikilink.js",
"type": "application/javascript",
"module-type": "wikirule"
},
"$:/core/modules/parsers/wikiparser/wikiparser.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/wikiparser.js\ntype: application/javascript\nmodule-type: parser\n\nThe wiki text parser processes blocks of source text into a parse tree.\n\nThe parse tree is made up of nested arrays of these JavaScript objects:\n\n\t{type: \"element\", tag: <string>, attributes: {}, children: []} - an HTML element\n\t{type: \"text\", text: <string>} - a text node\n\t{type: \"entity\", value: <string>} - an entity\n\t{type: \"raw\", html: <string>} - raw HTML\n\nAttributes are stored as hashmaps of the following objects:\n\n\t{type: \"string\", value: <string>} - literal string\n\t{type: \"indirect\", textReference: <textReference>} - indirect through a text reference\n\t{type: \"macro\", macro: <TBD>} - indirect through a macro invocation\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar WikiParser = function(type,text,options) {\n\tthis.wiki = options.wiki;\n\tvar self = this;\n\t// Check for an externally linked tiddler\n\tif($tw.browser && (text || \"\") === \"\" && options._canonical_uri) {\n\t\tthis.loadRemoteTiddler(options._canonical_uri);\n\t\ttext = $tw.language.getRawString(\"LazyLoadingWarning\");\n\t}\n\t// Initialise the classes if we don't have them already\n\tif(!this.pragmaRuleClasses) {\n\t\tWikiParser.prototype.pragmaRuleClasses = $tw.modules.createClassesFromModules(\"wikirule\",\"pragma\",$tw.WikiRuleBase);\n\t\tthis.setupRules(WikiParser.prototype.pragmaRuleClasses,\"$:/config/WikiParserRules/Pragmas/\");\n\t}\n\tif(!this.blockRuleClasses) {\n\t\tWikiParser.prototype.blockRuleClasses = $tw.modules.createClassesFromModules(\"wikirule\",\"block\",$tw.WikiRuleBase);\n\t\tthis.setupRules(WikiParser.prototype.blockRuleClasses,\"$:/config/WikiParserRules/Block/\");\n\t}\n\tif(!this.inlineRuleClasses) {\n\t\tWikiParser.prototype.inlineRuleClasses = $tw.modules.createClassesFromModules(\"wikirule\",\"inline\",$tw.WikiRuleBase);\n\t\tthis.setupRules(WikiParser.prototype.inlineRuleClasses,\"$:/config/WikiParserRules/Inline/\");\n\t}\n\t// Save the parse text\n\tthis.type = type || \"text/vnd.tiddlywiki\";\n\tthis.source = text || \"\";\n\tthis.sourceLength = this.source.length;\n\t// Set current parse position\n\tthis.pos = 0;\n\t// Instantiate the pragma parse rules\n\tthis.pragmaRules = this.instantiateRules(this.pragmaRuleClasses,\"pragma\",0);\n\t// Instantiate the parser block and inline rules\n\tthis.blockRules = this.instantiateRules(this.blockRuleClasses,\"block\",0);\n\tthis.inlineRules = this.instantiateRules(this.inlineRuleClasses,\"inline\",0);\n\t// Parse any pragmas\n\tthis.tree = [];\n\tvar topBranch = this.parsePragmas();\n\t// Parse the text into inline runs or blocks\n\tif(options.parseAsInline) {\n\t\ttopBranch.push.apply(topBranch,this.parseInlineRun());\n\t} else {\n\t\ttopBranch.push.apply(topBranch,this.parseBlocks());\n\t}\n\t// Return the parse tree\n};\n\n/*\n*/\nWikiParser.prototype.loadRemoteTiddler = function(url) {\n\tvar self = this;\n\t$tw.utils.httpRequest({\n\t\turl: url,\n\t\ttype: \"GET\",\n\t\tcallback: function(err,data) {\n\t\t\tif(!err) {\n\t\t\t\tvar tiddlers = self.wiki.deserializeTiddlers(\".tid\",data,self.wiki.getCreationFields());\n\t\t\t\t$tw.utils.each(tiddlers,function(tiddler) {\n\t\t\t\t\ttiddler[\"_canonical_uri\"] = url;\n\t\t\t\t});\n\t\t\t\tif(tiddlers) {\n\t\t\t\t\tself.wiki.addTiddlers(tiddlers);\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n};\n\n/*\n*/\nWikiParser.prototype.setupRules = function(proto,configPrefix) {\n\tvar self = this;\n\tif(!$tw.safemode) {\n\t\t$tw.utils.each(proto,function(object,name) {\n\t\t\tif(self.wiki.getTiddlerText(configPrefix + name,\"enable\") !== \"enable\") {\n\t\t\t\tdelete proto[name];\n\t\t\t}\n\t\t});\n\t}\n};\n\n/*\nInstantiate an array of parse rules\n*/\nWikiParser.prototype.instantiateRules = function(classes,type,startPos) {\n\tvar rulesInfo = [],\n\t\tself = this;\n\t$tw.utils.each(classes,function(RuleClass) {\n\t\t// Instantiate the rule\n\t\tvar rule = new RuleClass(self);\n\t\trule.is = {};\n\t\trule.is[type] = true;\n\t\trule.init(self);\n\t\tvar matchIndex = rule.findNextMatch(startPos);\n\t\tif(matchIndex !== undefined) {\n\t\t\trulesInfo.push({\n\t\t\t\trule: rule,\n\t\t\t\tmatchIndex: matchIndex\n\t\t\t});\n\t\t}\n\t});\n\treturn rulesInfo;\n};\n\n/*\nSkip any whitespace at the current position. Options are:\n\ttreatNewlinesAsNonWhitespace: true if newlines are NOT to be treated as whitespace\n*/\nWikiParser.prototype.skipWhitespace = function(options) {\n\toptions = options || {};\n\tvar whitespaceRegExp = options.treatNewlinesAsNonWhitespace ? /([^\\S\\n]+)/mg : /(\\s+)/mg;\n\twhitespaceRegExp.lastIndex = this.pos;\n\tvar whitespaceMatch = whitespaceRegExp.exec(this.source);\n\tif(whitespaceMatch && whitespaceMatch.index === this.pos) {\n\t\tthis.pos = whitespaceRegExp.lastIndex;\n\t}\n};\n\n/*\nGet the next match out of an array of parse rule instances\n*/\nWikiParser.prototype.findNextMatch = function(rules,startPos) {\n\t// Find the best matching rule by finding the closest match position\n\tvar matchingRule,\n\t\tmatchingRulePos = this.sourceLength;\n\t// Step through each rule\n\tfor(var t=0; t<rules.length; t++) {\n\t\tvar ruleInfo = rules[t];\n\t\t// Ask the rule to get the next match if we've moved past the current one\n\t\tif(ruleInfo.matchIndex !== undefined && ruleInfo.matchIndex < startPos) {\n\t\t\truleInfo.matchIndex = ruleInfo.rule.findNextMatch(startPos);\n\t\t}\n\t\t// Adopt this match if it's closer than the current best match\n\t\tif(ruleInfo.matchIndex !== undefined && ruleInfo.matchIndex <= matchingRulePos) {\n\t\t\tmatchingRule = ruleInfo;\n\t\t\tmatchingRulePos = ruleInfo.matchIndex;\n\t\t}\n\t}\n\treturn matchingRule;\n};\n\n/*\nParse any pragmas at the beginning of a block of parse text\n*/\nWikiParser.prototype.parsePragmas = function() {\n\tvar currentTreeBranch = this.tree;\n\twhile(true) {\n\t\t// Skip whitespace\n\t\tthis.skipWhitespace();\n\t\t// Check for the end of the text\n\t\tif(this.pos >= this.sourceLength) {\n\t\t\tbreak;\n\t\t}\n\t\t// Check if we've arrived at a pragma rule match\n\t\tvar nextMatch = this.findNextMatch(this.pragmaRules,this.pos);\n\t\t// If not, just exit\n\t\tif(!nextMatch || nextMatch.matchIndex !== this.pos) {\n\t\t\tbreak;\n\t\t}\n\t\t// Process the pragma rule\n\t\tvar subTree = nextMatch.rule.parse();\n\t\tif(subTree.length > 0) {\n\t\t\t// Quick hack; we only cope with a single parse tree node being returned, which is true at the moment\n\t\t\tcurrentTreeBranch.push.apply(currentTreeBranch,subTree);\n\t\t\tsubTree[0].children = [];\n\t\t\tcurrentTreeBranch = subTree[0].children;\n\t\t}\n\t}\n\treturn currentTreeBranch;\n};\n\n/*\nParse a block from the current position\n\tterminatorRegExpString: optional regular expression string that identifies the end of plain paragraphs. Must not include capturing parenthesis\n*/\nWikiParser.prototype.parseBlock = function(terminatorRegExpString) {\n\tvar terminatorRegExp = terminatorRegExpString ? new RegExp(\"(\" + terminatorRegExpString + \"|\\\\r?\\\\n\\\\r?\\\\n)\",\"mg\") : /(\\r?\\n\\r?\\n)/mg;\n\tthis.skipWhitespace();\n\tif(this.pos >= this.sourceLength) {\n\t\treturn [];\n\t}\n\t// Look for a block rule that applies at the current position\n\tvar nextMatch = this.findNextMatch(this.blockRules,this.pos);\n\tif(nextMatch && nextMatch.matchIndex === this.pos) {\n\t\treturn nextMatch.rule.parse();\n\t}\n\t// Treat it as a paragraph if we didn't find a block rule\n\treturn [{type: \"element\", tag: \"p\", children: this.parseInlineRun(terminatorRegExp)}];\n};\n\n/*\nParse a series of blocks of text until a terminating regexp is encountered or the end of the text\n\tterminatorRegExpString: terminating regular expression\n*/\nWikiParser.prototype.parseBlocks = function(terminatorRegExpString) {\n\tif(terminatorRegExpString) {\n\t\treturn this.parseBlocksTerminated(terminatorRegExpString);\n\t} else {\n\t\treturn this.parseBlocksUnterminated();\n\t}\n};\n\n/*\nParse a block from the current position to the end of the text\n*/\nWikiParser.prototype.parseBlocksUnterminated = function() {\n\tvar tree = [];\n\twhile(this.pos < this.sourceLength) {\n\t\ttree.push.apply(tree,this.parseBlock());\n\t}\n\treturn tree;\n};\n\n/*\nParse blocks of text until a terminating regexp is encountered\n*/\nWikiParser.prototype.parseBlocksTerminated = function(terminatorRegExpString) {\n\tvar terminatorRegExp = new RegExp(\"(\" + terminatorRegExpString + \")\",\"mg\"),\n\t\ttree = [];\n\t// Skip any whitespace\n\tthis.skipWhitespace();\n\t// Check if we've got the end marker\n\tterminatorRegExp.lastIndex = this.pos;\n\tvar match = terminatorRegExp.exec(this.source);\n\t// Parse the text into blocks\n\twhile(this.pos < this.sourceLength && !(match && match.index === this.pos)) {\n\t\tvar blocks = this.parseBlock(terminatorRegExpString);\n\t\ttree.push.apply(tree,blocks);\n\t\t// Skip any whitespace\n\t\tthis.skipWhitespace();\n\t\t// Check if we've got the end marker\n\t\tterminatorRegExp.lastIndex = this.pos;\n\t\tmatch = terminatorRegExp.exec(this.source);\n\t}\n\tif(match && match.index === this.pos) {\n\t\tthis.pos = match.index + match[0].length;\n\t}\n\treturn tree;\n};\n\n/*\nParse a run of text at the current position\n\tterminatorRegExp: a regexp at which to stop the run\n\toptions: see below\nOptions available:\n\teatTerminator: move the parse position past any encountered terminator (default false)\n*/\nWikiParser.prototype.parseInlineRun = function(terminatorRegExp,options) {\n\tif(terminatorRegExp) {\n\t\treturn this.parseInlineRunTerminated(terminatorRegExp,options);\n\t} else {\n\t\treturn this.parseInlineRunUnterminated(options);\n\t}\n};\n\nWikiParser.prototype.parseInlineRunUnterminated = function(options) {\n\tvar tree = [];\n\t// Find the next occurrence of an inline rule\n\tvar nextMatch = this.findNextMatch(this.inlineRules,this.pos);\n\t// Loop around the matches until we've reached the end of the text\n\twhile(this.pos < this.sourceLength && nextMatch) {\n\t\t// Process the text preceding the run rule\n\t\tif(nextMatch.matchIndex > this.pos) {\n\t\t\ttree.push({type: \"text\", text: this.source.substring(this.pos,nextMatch.matchIndex)});\n\t\t\tthis.pos = nextMatch.matchIndex;\n\t\t}\n\t\t// Process the run rule\n\t\ttree.push.apply(tree,nextMatch.rule.parse());\n\t\t// Look for the next run rule\n\t\tnextMatch = this.findNextMatch(this.inlineRules,this.pos);\n\t}\n\t// Process the remaining text\n\tif(this.pos < this.sourceLength) {\n\t\ttree.push({type: \"text\", text: this.source.substr(this.pos)});\n\t}\n\tthis.pos = this.sourceLength;\n\treturn tree;\n};\n\nWikiParser.prototype.parseInlineRunTerminated = function(terminatorRegExp,options) {\n\toptions = options || {};\n\tvar tree = [];\n\t// Find the next occurrence of the terminator\n\tterminatorRegExp.lastIndex = this.pos;\n\tvar terminatorMatch = terminatorRegExp.exec(this.source);\n\t// Find the next occurrence of a inlinerule\n\tvar inlineRuleMatch = this.findNextMatch(this.inlineRules,this.pos);\n\t// Loop around until we've reached the end of the text\n\twhile(this.pos < this.sourceLength && (terminatorMatch || inlineRuleMatch)) {\n\t\t// Return if we've found the terminator, and it precedes any inline rule match\n\t\tif(terminatorMatch) {\n\t\t\tif(!inlineRuleMatch || inlineRuleMatch.matchIndex >= terminatorMatch.index) {\n\t\t\t\tif(terminatorMatch.index > this.pos) {\n\t\t\t\t\ttree.push({type: \"text\", text: this.source.substring(this.pos,terminatorMatch.index)});\n\t\t\t\t}\n\t\t\t\tthis.pos = terminatorMatch.index;\n\t\t\t\tif(options.eatTerminator) {\n\t\t\t\t\tthis.pos += terminatorMatch[0].length;\n\t\t\t\t}\n\t\t\t\treturn tree;\n\t\t\t}\n\t\t}\n\t\t// Process any inline rule, along with the text preceding it\n\t\tif(inlineRuleMatch) {\n\t\t\t// Preceding text\n\t\t\tif(inlineRuleMatch.matchIndex > this.pos) {\n\t\t\t\ttree.push({type: \"text\", text: this.source.substring(this.pos,inlineRuleMatch.matchIndex)});\n\t\t\t\tthis.pos = inlineRuleMatch.matchIndex;\n\t\t\t}\n\t\t\t// Process the inline rule\n\t\t\ttree.push.apply(tree,inlineRuleMatch.rule.parse());\n\t\t\t// Look for the next inline rule\n\t\t\tinlineRuleMatch = this.findNextMatch(this.inlineRules,this.pos);\n\t\t\t// Look for the next terminator match\n\t\t\tterminatorRegExp.lastIndex = this.pos;\n\t\t\tterminatorMatch = terminatorRegExp.exec(this.source);\n\t\t}\n\t}\n\t// Process the remaining text\n\tif(this.pos < this.sourceLength) {\n\t\ttree.push({type: \"text\", text: this.source.substr(this.pos)});\n\t}\n\tthis.pos = this.sourceLength;\n\treturn tree;\n};\n\n/*\nParse zero or more class specifiers `.classname`\n*/\nWikiParser.prototype.parseClasses = function() {\n\tvar classRegExp = /\\.([^\\s\\.]+)/mg,\n\t\tclassNames = [];\n\tclassRegExp.lastIndex = this.pos;\n\tvar match = classRegExp.exec(this.source);\n\twhile(match && match.index === this.pos) {\n\t\tthis.pos = match.index + match[0].length;\n\t\tclassNames.push(match[1]);\n\t\tmatch = classRegExp.exec(this.source);\n\t}\n\treturn classNames;\n};\n\n/*\nAmend the rules used by this instance of the parser\n\ttype: `only` keeps just the named rules, `except` keeps all but the named rules\n\tnames: array of rule names\n*/\nWikiParser.prototype.amendRules = function(type,names) {\n\tnames = names || [];\n\t// Define the filter function\n\tvar keepFilter;\n\tif(type === \"only\") {\n\t\tkeepFilter = function(name) {\n\t\t\treturn names.indexOf(name) !== -1;\n\t\t};\n\t} else if(type === \"except\") {\n\t\tkeepFilter = function(name) {\n\t\t\treturn names.indexOf(name) === -1;\n\t\t};\n\t} else {\n\t\treturn;\n\t}\n\t// Define a function to process each of our rule arrays\n\tvar processRuleArray = function(ruleArray) {\n\t\tfor(var t=ruleArray.length-1; t>=0; t--) {\n\t\t\tif(!keepFilter(ruleArray[t].rule.name)) {\n\t\t\t\truleArray.splice(t,1);\n\t\t\t}\n\t\t}\n\t};\n\t// Process each rule array\n\tprocessRuleArray(this.pragmaRules);\n\tprocessRuleArray(this.blockRules);\n\tprocessRuleArray(this.inlineRules);\n};\n\nexports[\"text/vnd.tiddlywiki\"] = WikiParser;\n\n})();\n\n",
"title": "$:/core/modules/parsers/wikiparser/wikiparser.js",
"type": "application/javascript",
"module-type": "parser"
},
"$:/core/modules/parsers/wikiparser/rules/wikirulebase.js": {
"text": "/*\\\ntitle: $:/core/modules/parsers/wikiparser/rules/wikirulebase.js\ntype: application/javascript\nmodule-type: global\n\nBase class for wiki parser rules\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nThis constructor is always overridden with a blank constructor, and so shouldn't be used\n*/\nvar WikiRuleBase = function() {\n};\n\n/*\nTo be overridden by individual rules\n*/\nWikiRuleBase.prototype.init = function(parser) {\n\tthis.parser = parser;\n};\n\n/*\nDefault implementation of findNextMatch uses RegExp matching\n*/\nWikiRuleBase.prototype.findNextMatch = function(startPos) {\n\tthis.matchRegExp.lastIndex = startPos;\n\tthis.match = this.matchRegExp.exec(this.parser.source);\n\treturn this.match ? this.match.index : undefined;\n};\n\nexports.WikiRuleBase = WikiRuleBase;\n\n})();\n",
"title": "$:/core/modules/parsers/wikiparser/rules/wikirulebase.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/pluginswitcher.js": {
"text": "/*\\\ntitle: $:/core/modules/pluginswitcher.js\ntype: application/javascript\nmodule-type: global\n\nManages switching plugins for themes and languages.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\noptions:\nwiki: wiki store to be used\npluginType: type of plugin to be switched\ncontrollerTitle: title of tiddler used to control switching of this resource\ndefaultPlugins: array of default plugins to be used if nominated plugin isn't found\nonSwitch: callback when plugin is switched (single parameter is array of plugin titles)\n*/\nfunction PluginSwitcher(options) {\n\tthis.wiki = options.wiki;\n\tthis.pluginType = options.pluginType;\n\tthis.controllerTitle = options.controllerTitle;\n\tthis.defaultPlugins = options.defaultPlugins || [];\n\tthis.onSwitch = options.onSwitch;\n\t// Switch to the current plugin\n\tthis.switchPlugins();\n\t// Listen for changes to the selected plugin\n\tvar self = this;\n\tthis.wiki.addEventListener(\"change\",function(changes) {\n\t\tif($tw.utils.hop(changes,self.controllerTitle)) {\n\t\t\tself.switchPlugins();\n\t\t}\n\t});\n}\n\nPluginSwitcher.prototype.switchPlugins = function() {\n\t// Get the name of the current theme\n\tvar selectedPluginTitle = this.wiki.getTiddlerText(this.controllerTitle);\n\t// If it doesn't exist, then fallback to one of the default themes\n\tvar index = 0;\n\twhile(!this.wiki.getTiddler(selectedPluginTitle) && index < this.defaultPlugins.length) {\n\t\tselectedPluginTitle = this.defaultPlugins[index++];\n\t}\n\t// Accumulate the titles of the plugins that we need to load\n\tvar plugins = [],\n\t\tself = this,\n\t\taccumulatePlugin = function(title) {\n\t\t\tvar tiddler = self.wiki.getTiddler(title);\n\t\t\tif(tiddler && tiddler.isPlugin() && plugins.indexOf(title) === -1) {\n\t\t\t\tplugins.push(title);\n\t\t\t\tvar pluginInfo = JSON.parse(self.wiki.getTiddlerText(title)),\n\t\t\t\t\tdependents = $tw.utils.parseStringArray(tiddler.fields.dependents || \"\");\n\t\t\t\t$tw.utils.each(dependents,function(title) {\n\t\t\t\t\taccumulatePlugin(title);\n\t\t\t\t});\n\t\t\t}\n\t\t};\n\taccumulatePlugin(selectedPluginTitle);\n\t// Unregister any existing theme tiddlers\n\tvar unregisteredTiddlers = $tw.wiki.unregisterPluginTiddlers(this.pluginType);\n\t// Register any new theme tiddlers\n\tvar registeredTiddlers = $tw.wiki.registerPluginTiddlers(this.pluginType,plugins);\n\t// Unpack the current theme tiddlers\n\t$tw.wiki.unpackPluginTiddlers();\n\t// Call the switch handler\n\tif(this.onSwitch) {\n\t\tthis.onSwitch(plugins);\n\t}\n};\n\nexports.PluginSwitcher = PluginSwitcher;\n\n})();\n",
"title": "$:/core/modules/pluginswitcher.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/saver-handler.js": {
"text": "/*\\\ntitle: $:/core/modules/saver-handler.js\ntype: application/javascript\nmodule-type: global\n\nThe saver handler tracks changes to the store and handles saving the entire wiki via saver modules.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nInstantiate the saver handler with the following options:\nwiki: wiki to be synced\ndirtyTracking: true if dirty tracking should be performed\n*/\nfunction SaverHandler(options) {\n\tvar self = this;\n\tthis.wiki = options.wiki;\n\tthis.dirtyTracking = options.dirtyTracking;\n\tthis.pendingAutoSave = false;\n\t// Make a logger\n\tthis.logger = new $tw.utils.Logger(\"saver-handler\");\n\t// Initialise our savers\n\tif($tw.browser) {\n\t\tthis.initSavers();\n\t}\n\t// Only do dirty tracking if required\n\tif($tw.browser && this.dirtyTracking) {\n\t\t// Compile the dirty tiddler filter\n\t\tthis.filterFn = this.wiki.compileFilter(this.wiki.getTiddlerText(this.titleSyncFilter));\n\t\t// Count of changes that have not yet been saved\n\t\tthis.numChanges = 0;\n\t\t// Listen out for changes to tiddlers\n\t\tthis.wiki.addEventListener(\"change\",function(changes) {\n\t\t\t// Filter the changes so that we only count changes to tiddlers that we care about\n\t\t\tvar filteredChanges = self.filterFn.call(self.wiki,function(iterator) {\n\t\t\t\t$tw.utils.each(changes,function(change,title) {\n\t\t\t\t\tvar tiddler = self.wiki.getTiddler(title);\n\t\t\t\t\titerator(tiddler,title);\n\t\t\t\t});\n\t\t\t});\n\t\t\t// Adjust the number of changes\n\t\t\tself.numChanges += filteredChanges.length;\n\t\t\tself.updateDirtyStatus();\n\t\t\t// Do any autosave if one is pending and there's no more change events\n\t\t\tif(self.pendingAutoSave && self.wiki.getSizeOfTiddlerEventQueue() === 0) {\n\t\t\t\t// Check if we're dirty\n\t\t\t\tif(self.numChanges > 0) {\n\t\t\t\t\tself.saveWiki({\n\t\t\t\t\t\tmethod: \"autosave\",\n\t\t\t\t\t\tdownloadType: \"text/plain\"\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t\tself.pendingAutoSave = false;\n\t\t\t}\n\t\t});\n\t\t// Listen for the autosave event\n\t\t$tw.rootWidget.addEventListener(\"tm-auto-save-wiki\",function(event) {\n\t\t\t// Do the autosave unless there are outstanding tiddler change events\n\t\t\tif(self.wiki.getSizeOfTiddlerEventQueue() === 0) {\n\t\t\t\t// Check if we're dirty\n\t\t\t\tif(self.numChanges > 0) {\n\t\t\t\t\tself.saveWiki({\n\t\t\t\t\t\tmethod: \"autosave\",\n\t\t\t\t\t\tdownloadType: \"text/plain\"\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t// Otherwise put ourselves in the \"pending autosave\" state and wait for the change event before we do the autosave\n\t\t\t\tself.pendingAutoSave = true;\n\t\t\t}\n\t\t});\n\t\t// Set up our beforeunload handler\n\t\t$tw.addUnloadTask(function(event) {\n\t\t\tvar confirmationMessage;\n\t\t\tif(self.isDirty()) {\n\t\t\t\tconfirmationMessage = $tw.language.getString(\"UnsavedChangesWarning\");\n\t\t\t\tevent.returnValue = confirmationMessage; // Gecko\n\t\t\t}\n\t\t\treturn confirmationMessage;\n\t\t});\n\t}\n\t// Install the save action handlers\n\tif($tw.browser) {\n\t\t$tw.rootWidget.addEventListener(\"tm-save-wiki\",function(event) {\n\t\t\tself.saveWiki({\n\t\t\t\ttemplate: event.param,\n\t\t\t\tdownloadType: \"text/plain\",\n\t\t\t\tvariables: event.paramObject\n\t\t\t});\n\t\t});\n\t\t$tw.rootWidget.addEventListener(\"tm-download-file\",function(event) {\n\t\t\tself.saveWiki({\n\t\t\t\tmethod: \"download\",\n\t\t\t\ttemplate: event.param,\n\t\t\t\tdownloadType: \"text/plain\",\n\t\t\t\tvariables: event.paramObject\n\t\t\t});\n\t\t});\n\t}\n}\n\nSaverHandler.prototype.titleSyncFilter = \"$:/config/SaverFilter\";\nSaverHandler.prototype.titleAutoSave = \"$:/config/AutoSave\";\nSaverHandler.prototype.titleSavedNotification = \"$:/language/Notifications/Save/Done\";\n\n/*\nSelect the appropriate saver modules and set them up\n*/\nSaverHandler.prototype.initSavers = function(moduleType) {\n\tmoduleType = moduleType || \"saver\";\n\t// Instantiate the available savers\n\tthis.savers = [];\n\tvar self = this;\n\t$tw.modules.forEachModuleOfType(moduleType,function(title,module) {\n\t\tif(module.canSave(self)) {\n\t\t\tself.savers.push(module.create(self.wiki));\n\t\t}\n\t});\n\t// Sort the savers into priority order\n\tthis.savers.sort(function(a,b) {\n\t\tif(a.info.priority < b.info.priority) {\n\t\t\treturn -1;\n\t\t} else {\n\t\t\tif(a.info.priority > b.info.priority) {\n\t\t\t\treturn +1;\n\t\t\t} else {\n\t\t\t\treturn 0;\n\t\t\t}\n\t\t}\n\t});\n};\n\n/*\nSave the wiki contents. Options are:\n\tmethod: \"save\", \"autosave\" or \"download\"\n\ttemplate: the tiddler containing the template to save\n\tdownloadType: the content type for the saved file\n*/\nSaverHandler.prototype.saveWiki = function(options) {\n\toptions = options || {};\n\tvar self = this,\n\t\tmethod = options.method || \"save\",\n\t\tvariables = options.variables || {},\n\t\ttemplate = options.template || \"$:/core/save/all\",\n\t\tdownloadType = options.downloadType || \"text/plain\",\n\t\ttext = this.wiki.renderTiddler(downloadType,template,options),\n\t\tcallback = function(err) {\n\t\t\tif(err) {\n\t\t\t\talert($tw.language.getString(\"Error/WhileSaving\") + \":\\n\\n\" + err);\n\t\t\t} else {\n\t\t\t\t// Clear the task queue if we're saving (rather than downloading)\n\t\t\t\tif(method !== \"download\") {\n\t\t\t\t\tself.numChanges = 0;\n\t\t\t\t\tself.updateDirtyStatus();\n\t\t\t\t}\n\t\t\t\t$tw.notifier.display(self.titleSavedNotification);\n\t\t\t\tif(options.callback) {\n\t\t\t\t\toptions.callback();\n\t\t\t\t}\n\t\t\t}\n\t\t};\n\t// Ignore autosave if disabled\n\tif(method === \"autosave\" && this.wiki.getTiddlerText(this.titleAutoSave,\"yes\") !== \"yes\") {\n\t\treturn false;\n\t}\n\t// Call the highest priority saver that supports this method\n\tfor(var t=this.savers.length-1; t>=0; t--) {\n\t\tvar saver = this.savers[t];\n\t\tif(saver.info.capabilities.indexOf(method) !== -1 && saver.save(text,method,callback,{variables: {filename: variables.filename}})) {\n\t\t\tthis.logger.log(\"Saving wiki with method\",method,\"through saver\",saver.info.name);\n\t\t\treturn true;\n\t\t}\n\t}\n\treturn false;\n};\n\n/*\nChecks whether the wiki is dirty (ie the window shouldn't be closed)\n*/\nSaverHandler.prototype.isDirty = function() {\n\treturn this.numChanges > 0;\n};\n\n/*\nUpdate the document body with the class \"tc-dirty\" if the wiki has unsaved/unsynced changes\n*/\nSaverHandler.prototype.updateDirtyStatus = function() {\n\tif($tw.browser) {\n\t\t$tw.utils.toggleClass(document.body,\"tc-dirty\",this.isDirty());\n\t}\n};\n\nexports.SaverHandler = SaverHandler;\n\n})();\n",
"title": "$:/core/modules/saver-handler.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/savers/andtidwiki.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/andtidwiki.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via the AndTidWiki Android app\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false, netscape: false, Components: false */\n\"use strict\";\n\nvar AndTidWiki = function(wiki) {\n};\n\nAndTidWiki.prototype.save = function(text,method,callback) {\n\t// Get the pathname of this document\n\tvar pathname = decodeURIComponent(document.location.toString().split(\"#\")[0]);\n\t// Strip the file://\n\tif(pathname.indexOf(\"file://\") === 0) {\n\t\tpathname = pathname.substr(7);\n\t}\n\t// Strip any query or location part\n\tvar p = pathname.indexOf(\"?\");\n\tif(p !== -1) {\n\t\tpathname = pathname.substr(0,p);\n\t}\n\tp = pathname.indexOf(\"#\");\n\tif(p !== -1) {\n\t\tpathname = pathname.substr(0,p);\n\t}\n\t// Save the file\n\twindow.twi.saveFile(pathname,text);\n\t// Call the callback\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nAndTidWiki.prototype.info = {\n\tname: \"andtidwiki\",\n\tpriority: 1600,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn !!window.twi && !!window.twi.saveFile;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new AndTidWiki(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/andtidwiki.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/beaker.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/beaker.js\ntype: application/javascript\nmodule-type: saver\n\nSaves files using the Beaker browser's (https://beakerbrowser.com) Dat protocol (https://datproject.org/)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSet up the saver\n*/\nvar BeakerSaver = function(wiki) {\n\tthis.wiki = wiki;\n};\n\nBeakerSaver.prototype.save = function(text,method,callback) {\n\tvar url = (location.toString()).split(\"#\")[0];\n\tdat.stat(url).then(function(value) {\n\t\tif(value.type === \"directory\") {\n\t\t\turl = url + \"/index.html\";\n\t\t}\n\t\tdat.writeFile(url,text,\"utf8\").then(function(value) {\n\t\t\tcallback(null);\n\t\t},function(reason) {\n\t\t\tcallback(\"Beaker Saver Write Error: \" + reason);\n\t\t});\t\t\n\t},function(reason) {\n\t\tcallback(\"Beaker Saver Stat Error: \" + reason);\n\t});\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nBeakerSaver.prototype.info = {\n\tname: \"beaker\",\n\tpriority: 3000,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn !!window.dat;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new BeakerSaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/beaker.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/download.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/download.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via HTML5's download APIs\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar DownloadSaver = function(wiki) {\n};\n\nDownloadSaver.prototype.save = function(text,method,callback,options) {\n\toptions = options || {};\n\t// Get the current filename\n\tvar filename = options.variables.filename;\n\tif(!filename) {\n\t\tvar p = document.location.pathname.lastIndexOf(\"/\");\n\t\tif(p !== -1) {\n\t\t\tfilename = document.location.pathname.substr(p+1);\n\t\t}\n\t}\n\tif(!filename) {\n\t\tfilename = \"tiddlywiki.html\";\n\t}\n\t// Set up the link\n\tvar link = document.createElement(\"a\");\n\tif(Blob !== undefined) {\n\t\tvar blob = new Blob([text], {type: \"text/html\"});\n\t\tlink.setAttribute(\"href\", URL.createObjectURL(blob));\n\t} else {\n\t\tlink.setAttribute(\"href\",\"data:text/html,\" + encodeURIComponent(text));\n\t}\n\tlink.setAttribute(\"download\",filename);\n\tdocument.body.appendChild(link);\n\tlink.click();\n\tdocument.body.removeChild(link);\n\t// Callback that we succeeded\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nDownloadSaver.prototype.info = {\n\tname: \"download\",\n\tpriority: 100\n};\n\nObject.defineProperty(DownloadSaver.prototype.info, \"capabilities\", {\n\tget: function() {\n\t\tvar capabilities = [\"save\", \"download\"];\n\t\tif(($tw.wiki.getTextReference(\"$:/config/DownloadSaver/AutoSave\") || \"\").toLowerCase() === \"yes\") {\n\t\t\tcapabilities.push(\"autosave\");\n\t\t}\n\t\treturn capabilities;\n\t}\n});\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn document.createElement(\"a\").download !== undefined;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new DownloadSaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/download.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/fsosaver.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/fsosaver.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via MS FileSystemObject ActiveXObject\n\nNote: Since TiddlyWiki's markup contains the MOTW, the FileSystemObject normally won't be available. \nHowever, if the wiki is loaded as an .HTA file (Windows HTML Applications) then the FSO can be used.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar FSOSaver = function(wiki) {\n};\n\nFSOSaver.prototype.save = function(text,method,callback) {\n\t// Get the pathname of this document\n\tvar pathname = unescape(document.location.pathname);\n\t// Test for a Windows path of the form /x:\\blah...\n\tif(/^\\/[A-Z]\\:\\\\[^\\\\]+/i.test(pathname)) {\t// ie: ^/[a-z]:/[^/]+\n\t\t// Remove the leading slash\n\t\tpathname = pathname.substr(1);\n\t} else if(document.location.hostname !== \"\" && /^\\/\\\\[^\\\\]+\\\\[^\\\\]+/i.test(pathname)) {\t// test for \\\\server\\share\\blah... - ^/[^/]+/[^/]+\n\t\t// Remove the leading slash\n\t\tpathname = pathname.substr(1);\n\t\t// reconstruct UNC path\n\t\tpathname = \"\\\\\\\\\" + document.location.hostname + pathname;\n\t} else {\n\t\treturn false;\n\t}\n\t// Save the file (as UTF-16)\n\tvar fso = new ActiveXObject(\"Scripting.FileSystemObject\");\n\tvar file = fso.OpenTextFile(pathname,2,-1,-1);\n\tfile.Write(text);\n\tfile.Close();\n\t// Callback that we succeeded\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nFSOSaver.prototype.info = {\n\tname: \"FSOSaver\",\n\tpriority: 120,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\ttry {\n\t\treturn (window.location.protocol === \"file:\") && !!(new ActiveXObject(\"Scripting.FileSystemObject\"));\n\t} catch(e) { return false; }\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new FSOSaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/fsosaver.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/manualdownload.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/manualdownload.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via HTML5's download APIs\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Title of the tiddler containing the download message\nvar downloadInstructionsTitle = \"$:/language/Modals/Download\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar ManualDownloadSaver = function(wiki) {\n};\n\nManualDownloadSaver.prototype.save = function(text,method,callback) {\n\t$tw.modal.display(downloadInstructionsTitle,{\n\t\tdownloadLink: \"data:text/html,\" + encodeURIComponent(text)\n\t});\n\t// Callback that we succeeded\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nManualDownloadSaver.prototype.info = {\n\tname: \"manualdownload\",\n\tpriority: 0,\n\tcapabilities: [\"save\", \"download\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new ManualDownloadSaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/manualdownload.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/msdownload.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/msdownload.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via window.navigator.msSaveBlob()\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar MsDownloadSaver = function(wiki) {\n};\n\nMsDownloadSaver.prototype.save = function(text,method,callback) {\n\t// Get the current filename\n\tvar filename = \"tiddlywiki.html\",\n\t\tp = document.location.pathname.lastIndexOf(\"/\");\n\tif(p !== -1) {\n\t\tfilename = document.location.pathname.substr(p+1);\n\t}\n\t// Set up the link\n\tvar blob = new Blob([text], {type: \"text/html\"});\n\twindow.navigator.msSaveBlob(blob,filename);\n\t// Callback that we succeeded\n\tcallback(null);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nMsDownloadSaver.prototype.info = {\n\tname: \"msdownload\",\n\tpriority: 110,\n\tcapabilities: [\"save\", \"download\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn !!window.navigator.msSaveBlob;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new MsDownloadSaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/msdownload.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/put.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/put.js\ntype: application/javascript\nmodule-type: saver\n\nSaves wiki by performing a PUT request to the server\n\nWorks with any server which accepts a PUT request\nto the current URL, such as a WebDAV server.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar PutSaver = function(wiki) {\n\tthis.wiki = wiki;\n\tvar self = this;\n\tvar uri = this.uri();\n\t// Async server probe. Until probe finishes, save will fail fast\n\t// See also https://github.com/Jermolene/TiddlyWiki5/issues/2276\n\t$tw.utils.httpRequest({\n\t\turl: uri,\n\t\ttype: \"OPTIONS\",\n\t\tcallback: function(err, data, xhr) {\n\t\t\t// Check DAV header http://www.webdav.org/specs/rfc2518.html#rfc.section.9.1\n\t\t\tif(!err) {\n\t\t\t\tself.serverAcceptsPuts = xhr.status === 200 && !!xhr.getResponseHeader(\"dav\");\n\t\t\t}\n\t\t}\n\t});\n\t// Retrieve ETag if available\n\t$tw.utils.httpRequest({\n\t\turl: uri,\n\t\ttype: \"HEAD\",\n\t\tcallback: function(err, data, xhr) {\n\t\t\tif(!err) {\n\t\t\t\tself.etag = xhr.getResponseHeader(\"ETag\");\n\t\t\t}\n\t\t}\n\t});\n};\n\nPutSaver.prototype.uri = function() {\n\treturn encodeURI(document.location.toString().split(\"#\")[0]);\n};\n\n// TODO: in case of edit conflict\n// Prompt: Do you want to save over this? Y/N\n// Merging would be ideal, and may be possible using future generic merge flow\nPutSaver.prototype.save = function(text, method, callback) {\n\tif(!this.serverAcceptsPuts) {\n\t\treturn false;\n\t}\n\tvar self = this;\n\tvar headers = { \"Content-Type\": \"text/html;charset=UTF-8\" };\n\tif(this.etag) {\n\t\theaders[\"If-Match\"] = this.etag;\n\t}\n\t$tw.utils.httpRequest({\n\t\turl: this.uri(),\n\t\ttype: \"PUT\",\n\t\theaders: headers,\n\t\tdata: text,\n\t\tcallback: function(err, data, xhr) {\n\t\t\tif(err) {\n\t\t\t\tcallback(err);\n\t\t\t} if(xhr.status === 200 || xhr.status === 201) {\n\t\t\t\tself.etag = xhr.getResponseHeader(\"ETag\");\n\t\t\t\tcallback(null); // success\n\t\t\t} else if(xhr.status === 412) { // edit conflict\n\t\t\t\tvar message = $tw.language.getString(\"Error/EditConflict\");\n\t\t\t\tcallback(message);\n\t\t\t} else {\n\t\t\t\tcallback(xhr.responseText); // fail\n\t\t\t}\n\t\t}\n\t});\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nPutSaver.prototype.info = {\n\tname: \"put\",\n\tpriority: 2000,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn /^https?:/.test(location.protocol);\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new PutSaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/put.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/tiddlyfox.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/tiddlyfox.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via the TiddlyFox file extension\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false, netscape: false, Components: false */\n\"use strict\";\n\nvar TiddlyFoxSaver = function(wiki) {\n};\n\nTiddlyFoxSaver.prototype.save = function(text,method,callback) {\n\tvar messageBox = document.getElementById(\"tiddlyfox-message-box\");\n\tif(messageBox) {\n\t\t// Get the pathname of this document\n\t\tvar pathname = document.location.toString().split(\"#\")[0];\n\t\t// Replace file://localhost/ with file:///\n\t\tif(pathname.indexOf(\"file://localhost/\") === 0) {\n\t\t\tpathname = \"file://\" + pathname.substr(16);\n\t\t}\n\t\t// Windows path file:///x:/blah/blah --> x:\\blah\\blah\n\t\tif(/^file\\:\\/\\/\\/[A-Z]\\:\\//i.test(pathname)) {\n\t\t\t// Remove the leading slash and convert slashes to backslashes\n\t\t\tpathname = pathname.substr(8).replace(/\\//g,\"\\\\\");\n\t\t// Firefox Windows network path file://///server/share/blah/blah --> //server/share/blah/blah\n\t\t} else if(pathname.indexOf(\"file://///\") === 0) {\n\t\t\tpathname = \"\\\\\\\\\" + unescape(pathname.substr(10)).replace(/\\//g,\"\\\\\");\n\t\t// Mac/Unix local path file:///path/path --> /path/path\n\t\t} else if(pathname.indexOf(\"file:///\") === 0) {\n\t\t\tpathname = unescape(pathname.substr(7));\n\t\t// Mac/Unix local path file:/path/path --> /path/path\n\t\t} else if(pathname.indexOf(\"file:/\") === 0) {\n\t\t\tpathname = unescape(pathname.substr(5));\n\t\t// Otherwise Windows networth path file://server/share/path/path --> \\\\server\\share\\path\\path\n\t\t} else {\n\t\t\tpathname = \"\\\\\\\\\" + unescape(pathname.substr(7)).replace(new RegExp(\"/\",\"g\"),\"\\\\\");\n\t\t}\n\t\t// Create the message element and put it in the message box\n\t\tvar message = document.createElement(\"div\");\n\t\tmessage.setAttribute(\"data-tiddlyfox-path\",decodeURIComponent(pathname));\n\t\tmessage.setAttribute(\"data-tiddlyfox-content\",text);\n\t\tmessageBox.appendChild(message);\n\t\t// Add an event handler for when the file has been saved\n\t\tmessage.addEventListener(\"tiddlyfox-have-saved-file\",function(event) {\n\t\t\tcallback(null);\n\t\t}, false);\n\t\t// Create and dispatch the custom event to the extension\n\t\tvar event = document.createEvent(\"Events\");\n\t\tevent.initEvent(\"tiddlyfox-save-file\",true,false);\n\t\tmessage.dispatchEvent(event);\n\t\treturn true;\n\t} else {\n\t\treturn false;\n\t}\n};\n\n/*\nInformation about this saver\n*/\nTiddlyFoxSaver.prototype.info = {\n\tname: \"tiddlyfox\",\n\tpriority: 1500,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new TiddlyFoxSaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/tiddlyfox.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/tiddlyie.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/tiddlyie.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via Internet Explorer BHO extenion (TiddlyIE)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar TiddlyIESaver = function(wiki) {\n};\n\nTiddlyIESaver.prototype.save = function(text,method,callback) {\n\t// Check existence of TiddlyIE BHO extension (note: only works after document is complete)\n\tif(typeof(window.TiddlyIE) != \"undefined\") {\n\t\t// Get the pathname of this document\n\t\tvar pathname = unescape(document.location.pathname);\n\t\t// Test for a Windows path of the form /x:/blah...\n\t\tif(/^\\/[A-Z]\\:\\/[^\\/]+/i.test(pathname)) {\t// ie: ^/[a-z]:/[^/]+ (is this better?: ^/[a-z]:/[^/]+(/[^/]+)*\\.[^/]+ )\n\t\t\t// Remove the leading slash\n\t\t\tpathname = pathname.substr(1);\n\t\t\t// Convert slashes to backslashes\n\t\t\tpathname = pathname.replace(/\\//g,\"\\\\\");\n\t\t} else if(document.hostname !== \"\" && /^\\/[^\\/]+\\/[^\\/]+/i.test(pathname)) {\t// test for \\\\server\\share\\blah... - ^/[^/]+/[^/]+\n\t\t\t// Convert slashes to backslashes\n\t\t\tpathname = pathname.replace(/\\//g,\"\\\\\");\n\t\t\t// reconstruct UNC path\n\t\t\tpathname = \"\\\\\\\\\" + document.location.hostname + pathname;\n\t\t} else return false;\n\t\t// Prompt the user to save the file\n\t\twindow.TiddlyIE.save(pathname, text);\n\t\t// Callback that we succeeded\n\t\tcallback(null);\n\t\treturn true;\n\t} else {\n\t\treturn false;\n\t}\n};\n\n/*\nInformation about this saver\n*/\nTiddlyIESaver.prototype.info = {\n\tname: \"tiddlyiesaver\",\n\tpriority: 1500,\n\tcapabilities: [\"save\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn (window.location.protocol === \"file:\");\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new TiddlyIESaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/tiddlyie.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/twedit.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/twedit.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via the TWEdit iOS app\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false, netscape: false, Components: false */\n\"use strict\";\n\nvar TWEditSaver = function(wiki) {\n};\n\nTWEditSaver.prototype.save = function(text,method,callback) {\n\t// Bail if we're not running under TWEdit\n\tif(typeof DeviceInfo !== \"object\") {\n\t\treturn false;\n\t}\n\t// Get the pathname of this document\n\tvar pathname = decodeURIComponent(document.location.pathname);\n\t// Strip any query or location part\n\tvar p = pathname.indexOf(\"?\");\n\tif(p !== -1) {\n\t\tpathname = pathname.substr(0,p);\n\t}\n\tp = pathname.indexOf(\"#\");\n\tif(p !== -1) {\n\t\tpathname = pathname.substr(0,p);\n\t}\n\t// Remove the leading \"/Documents\" from path\n\tvar prefix = \"/Documents\";\n\tif(pathname.indexOf(prefix) === 0) {\n\t\tpathname = pathname.substr(prefix.length);\n\t}\n\t// Error handler\n\tvar errorHandler = function(event) {\n\t\t// Error\n\t\tcallback($tw.language.getString(\"Error/SavingToTWEdit\") + \": \" + event.target.error.code);\n\t};\n\t// Get the file system\n\twindow.requestFileSystem(LocalFileSystem.PERSISTENT,0,function(fileSystem) {\n\t\t// Now we've got the filesystem, get the fileEntry\n\t\tfileSystem.root.getFile(pathname, {create: true}, function(fileEntry) {\n\t\t\t// Now we've got the fileEntry, create the writer\n\t\t\tfileEntry.createWriter(function(writer) {\n\t\t\t\twriter.onerror = errorHandler;\n\t\t\t\twriter.onwrite = function() {\n\t\t\t\t\tcallback(null);\n\t\t\t\t};\n\t\t\t\twriter.position = 0;\n\t\t\t\twriter.write(text);\n\t\t\t},errorHandler);\n\t\t}, errorHandler);\n\t}, errorHandler);\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nTWEditSaver.prototype.info = {\n\tname: \"twedit\",\n\tpriority: 1600,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new TWEditSaver(wiki);\n};\n\n/////////////////////////// Hack\n// HACK: This ensures that TWEdit recognises us as a TiddlyWiki document\nif($tw.browser) {\n\twindow.version = {title: \"TiddlyWiki\"};\n}\n\n})();\n",
"title": "$:/core/modules/savers/twedit.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/savers/upload.js": {
"text": "/*\\\ntitle: $:/core/modules/savers/upload.js\ntype: application/javascript\nmodule-type: saver\n\nHandles saving changes via upload to a server.\n\nDesigned to be compatible with BidiX's UploadPlugin at http://tiddlywiki.bidix.info/#UploadPlugin\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSelect the appropriate saver module and set it up\n*/\nvar UploadSaver = function(wiki) {\n\tthis.wiki = wiki;\n};\n\nUploadSaver.prototype.save = function(text,method,callback) {\n\t// Get the various parameters we need\n\tvar backupDir = this.wiki.getTextReference(\"$:/UploadBackupDir\") || \".\",\n\t\tusername = this.wiki.getTextReference(\"$:/UploadName\"),\n\t\tpassword = $tw.utils.getPassword(\"upload\"),\n\t\tuploadDir = this.wiki.getTextReference(\"$:/UploadDir\") || \".\",\n\t\tuploadFilename = this.wiki.getTextReference(\"$:/UploadFilename\") || \"index.html\",\n\t\turl = this.wiki.getTextReference(\"$:/UploadURL\");\n\t// Bail out if we don't have the bits we need\n\tif(!username || username.toString().trim() === \"\" || !password || password.toString().trim() === \"\") {\n\t\treturn false;\n\t}\n\t// Construct the url if not provided\n\tif(!url) {\n\t\turl = \"http://\" + username + \".tiddlyspot.com/store.cgi\";\n\t}\n\t// Assemble the header\n\tvar boundary = \"---------------------------\" + \"AaB03x\";\t\n\tvar uploadFormName = \"UploadPlugin\";\n\tvar head = [];\n\thead.push(\"--\" + boundary + \"\\r\\nContent-disposition: form-data; name=\\\"UploadPlugin\\\"\\r\\n\");\n\thead.push(\"backupDir=\" + backupDir + \";user=\" + username + \";password=\" + password + \";uploaddir=\" + uploadDir + \";;\"); \n\thead.push(\"\\r\\n\" + \"--\" + boundary);\n\thead.push(\"Content-disposition: form-data; name=\\\"userfile\\\"; filename=\\\"\" + uploadFilename + \"\\\"\");\n\thead.push(\"Content-Type: text/html;charset=UTF-8\");\n\thead.push(\"Content-Length: \" + text.length + \"\\r\\n\");\n\thead.push(\"\");\n\t// Assemble the tail and the data itself\n\tvar tail = \"\\r\\n--\" + boundary + \"--\\r\\n\",\n\t\tdata = head.join(\"\\r\\n\") + text + tail;\n\t// Do the HTTP post\n\tvar http = new XMLHttpRequest();\n\thttp.open(\"POST\",url,true,username,password);\n\thttp.setRequestHeader(\"Content-Type\",\"multipart/form-data; charset=UTF-8; boundary=\" + boundary);\n\thttp.onreadystatechange = function() {\n\t\tif(http.readyState == 4 && http.status == 200) {\n\t\t\tif(http.responseText.substr(0,4) === \"0 - \") {\n\t\t\t\tcallback(null);\n\t\t\t} else {\n\t\t\t\tcallback(http.responseText);\n\t\t\t}\n\t\t}\n\t};\n\ttry {\n\t\thttp.send(data);\n\t} catch(ex) {\n\t\treturn callback($tw.language.getString(\"Error/Caption\") + \":\" + ex);\n\t}\n\t$tw.notifier.display(\"$:/language/Notifications/Save/Starting\");\n\treturn true;\n};\n\n/*\nInformation about this saver\n*/\nUploadSaver.prototype.info = {\n\tname: \"upload\",\n\tpriority: 2000,\n\tcapabilities: [\"save\", \"autosave\"]\n};\n\n/*\nStatic method that returns true if this saver is capable of working\n*/\nexports.canSave = function(wiki) {\n\treturn true;\n};\n\n/*\nCreate an instance of this saver\n*/\nexports.create = function(wiki) {\n\treturn new UploadSaver(wiki);\n};\n\n})();\n",
"title": "$:/core/modules/savers/upload.js",
"type": "application/javascript",
"module-type": "saver"
},
"$:/core/modules/browser-messaging.js": {
"text": "/*\\\ntitle: $:/core/modules/browser-messaging.js\ntype: application/javascript\nmodule-type: startup\n\nBrowser message handling\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"browser-messaging\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\n/*\nLoad a specified url as an iframe and call the callback when it is loaded. If the url is already loaded then the existing iframe instance is used\n*/\nfunction loadIFrame(url,callback) {\n\t// Check if iframe already exists\n\tvar iframeInfo = $tw.browserMessaging.iframeInfoMap[url];\n\tif(iframeInfo) {\n\t\t// We've already got the iframe\n\t\tcallback(null,iframeInfo);\n\t} else {\n\t\t// Create the iframe and save it in the list\n\t\tvar iframe = document.createElement(\"iframe\");\n\t\tiframeInfo = {\n\t\t\turl: url,\n\t\t\tstatus: \"loading\",\n\t\t\tdomNode: iframe\n\t\t};\n\t\t$tw.browserMessaging.iframeInfoMap[url] = iframeInfo;\n\t\tsaveIFrameInfoTiddler(iframeInfo);\n\t\t// Add the iframe to the DOM and hide it\n\t\tiframe.style.display = \"none\";\n\t\tiframe.setAttribute(\"library\",\"true\");\n\t\tdocument.body.appendChild(iframe);\n\t\t// Set up onload\n\t\tiframe.onload = function() {\n\t\t\tiframeInfo.status = \"loaded\";\n\t\t\tsaveIFrameInfoTiddler(iframeInfo);\n\t\t\tcallback(null,iframeInfo);\n\t\t};\n\t\tiframe.onerror = function() {\n\t\t\tcallback(\"Cannot load iframe\");\n\t\t};\n\t\ttry {\n\t\t\tiframe.src = url;\n\t\t} catch(ex) {\n\t\t\tcallback(ex);\n\t\t}\n\t}\n}\n\n/*\nUnload library iframe for given url\n*/\nfunction unloadIFrame(url){\n\t$tw.utils.each(document.getElementsByTagName('iframe'), function(iframe) {\n\t\tif(iframe.getAttribute(\"library\") === \"true\" &&\n\t\t iframe.getAttribute(\"src\") === url) {\n\t\t\tiframe.parentNode.removeChild(iframe);\n\t\t}\n\t});\n}\n\nfunction saveIFrameInfoTiddler(iframeInfo) {\n\t$tw.wiki.addTiddler(new $tw.Tiddler($tw.wiki.getCreationFields(),{\n\t\ttitle: \"$:/temp/ServerConnection/\" + iframeInfo.url,\n\t\ttext: iframeInfo.status,\n\t\ttags: [\"$:/tags/ServerConnection\"],\n\t\turl: iframeInfo.url\n\t},$tw.wiki.getModificationFields()));\n}\n\nexports.startup = function() {\n\t// Initialise the store of iframes we've created\n\t$tw.browserMessaging = {\n\t\tiframeInfoMap: {} // Hashmap by URL of {url:,status:\"loading/loaded\",domNode:}\n\t};\n\t// Listen for widget messages to control loading the plugin library\n\t$tw.rootWidget.addEventListener(\"tm-load-plugin-library\",function(event) {\n\t\tvar paramObject = event.paramObject || {},\n\t\t\turl = paramObject.url;\n\t\tif(url) {\n\t\t\tloadIFrame(url,function(err,iframeInfo) {\n\t\t\t\tif(err) {\n\t\t\t\t\talert($tw.language.getString(\"Error/LoadingPluginLibrary\") + \": \" + url);\n\t\t\t\t} else {\n\t\t\t\t\tiframeInfo.domNode.contentWindow.postMessage({\n\t\t\t\t\t\tverb: \"GET\",\n\t\t\t\t\t\turl: \"recipes/library/tiddlers.json\",\n\t\t\t\t\t\tcookies: {\n\t\t\t\t\t\t\ttype: \"save-info\",\n\t\t\t\t\t\t\tinfoTitlePrefix: paramObject.infoTitlePrefix || \"$:/temp/RemoteAssetInfo/\",\n\t\t\t\t\t\t\turl: url\n\t\t\t\t\t\t}\n\t\t\t\t\t},\"*\");\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\t// Listen for widget messages to control unloading the plugin library\n\t$tw.rootWidget.addEventListener(\"tm-unload-plugin-library\",function(event) {\n\t\tvar paramObject = event.paramObject || {},\n\t\t\turl = paramObject.url;\n\t\t$tw.browserMessaging.iframeInfoMap[url] = undefined;\n\t\tif(url) {\n\t\t\tunloadIFrame(url);\n\t\t\t$tw.utils.each(\n\t\t\t\t$tw.wiki.filterTiddlers(\"[[$:/temp/ServerConnection/\" + url + \"]] [prefix[$:/temp/RemoteAssetInfo/\" + url + \"/]]\"),\n\t\t\t\tfunction(title) {\n\t\t\t\t\t$tw.wiki.deleteTiddler(title);\n\t\t\t\t}\n\t\t\t);\n\t\t}\n\t});\n\t$tw.rootWidget.addEventListener(\"tm-load-plugin-from-library\",function(event) {\n\t\tvar paramObject = event.paramObject || {},\n\t\t\turl = paramObject.url,\n\t\t\ttitle = paramObject.title;\n\t\tif(url && title) {\n\t\t\tloadIFrame(url,function(err,iframeInfo) {\n\t\t\t\tif(err) {\n\t\t\t\t\talert($tw.language.getString(\"Error/LoadingPluginLibrary\") + \": \" + url);\n\t\t\t\t} else {\n\t\t\t\t\tiframeInfo.domNode.contentWindow.postMessage({\n\t\t\t\t\t\tverb: \"GET\",\n\t\t\t\t\t\turl: \"recipes/library/tiddlers/\" + encodeURIComponent(title) + \".json\",\n\t\t\t\t\t\tcookies: {\n\t\t\t\t\t\t\ttype: \"save-tiddler\",\n\t\t\t\t\t\t\turl: url\n\t\t\t\t\t\t}\n\t\t\t\t\t},\"*\");\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\t// Listen for window messages from other windows\n\twindow.addEventListener(\"message\",function listener(event){\n\t\tconsole.log(\"browser-messaging: \",document.location.toString())\n\t\tconsole.log(\"browser-messaging: Received message from\",event.origin);\n\t\tconsole.log(\"browser-messaging: Message content\",event.data);\n\t\tswitch(event.data.verb) {\n\t\t\tcase \"GET-RESPONSE\":\n\t\t\t\tif(event.data.status.charAt(0) === \"2\") {\n\t\t\t\t\tif(event.data.cookies) {\n\t\t\t\t\t\tif(event.data.cookies.type === \"save-info\") {\n\t\t\t\t\t\t\tvar tiddlers = JSON.parse(event.data.body);\n\t\t\t\t\t\t\t$tw.utils.each(tiddlers,function(tiddler) {\n\t\t\t\t\t\t\t\t$tw.wiki.addTiddler(new $tw.Tiddler($tw.wiki.getCreationFields(),tiddler,{\n\t\t\t\t\t\t\t\t\ttitle: event.data.cookies.infoTitlePrefix + event.data.cookies.url + \"/\" + tiddler.title,\n\t\t\t\t\t\t\t\t\t\"original-title\": tiddler.title,\n\t\t\t\t\t\t\t\t\ttext: \"\",\n\t\t\t\t\t\t\t\t\ttype: \"text/vnd.tiddlywiki\",\n\t\t\t\t\t\t\t\t\t\"original-type\": tiddler.type,\n\t\t\t\t\t\t\t\t\t\"plugin-type\": undefined,\n\t\t\t\t\t\t\t\t\t\"original-plugin-type\": tiddler[\"plugin-type\"],\n\t\t\t\t\t\t\t\t\t\"module-type\": undefined,\n\t\t\t\t\t\t\t\t\t\"original-module-type\": tiddler[\"module-type\"],\n\t\t\t\t\t\t\t\t\ttags: [\"$:/tags/RemoteAssetInfo\"],\n\t\t\t\t\t\t\t\t\t\"original-tags\": $tw.utils.stringifyList(tiddler.tags || []),\n\t\t\t\t\t\t\t\t\t\"server-url\": event.data.cookies.url\n\t\t\t\t\t\t\t\t},$tw.wiki.getModificationFields()));\n\t\t\t\t\t\t\t});\n\t\t\t\t\t\t} else if(event.data.cookies.type === \"save-tiddler\") {\n\t\t\t\t\t\t\tvar tiddler = JSON.parse(event.data.body);\n\t\t\t\t\t\t\t$tw.wiki.addTiddler(new $tw.Tiddler(tiddler));\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t}\n\t},false);\n};\n\n})();\n",
"title": "$:/core/modules/browser-messaging.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/commands.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/commands.js\ntype: application/javascript\nmodule-type: startup\n\nCommand processing\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"commands\";\nexports.platforms = [\"node\"];\nexports.after = [\"story\"];\nexports.synchronous = false;\n\nexports.startup = function(callback) {\n\t// On the server, start a commander with the command line arguments\n\tvar commander = new $tw.Commander(\n\t\t$tw.boot.argv,\n\t\tfunction(err) {\n\t\t\tif(err) {\n\t\t\t\treturn $tw.utils.error(\"Error: \" + err);\n\t\t\t}\n\t\t\tcallback();\n\t\t},\n\t\t$tw.wiki,\n\t\t{output: process.stdout, error: process.stderr}\n\t);\n\tcommander.execute();\n};\n\n})();\n",
"title": "$:/core/modules/startup/commands.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/favicon.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/favicon.js\ntype: application/javascript\nmodule-type: startup\n\nFavicon handling\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"favicon\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\t\t\n// Favicon tiddler\nvar FAVICON_TITLE = \"$:/favicon.ico\";\n\nexports.startup = function() {\n\t// Set up the favicon\n\tsetFavicon();\n\t// Reset the favicon when the tiddler changes\n\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\tif($tw.utils.hop(changes,FAVICON_TITLE)) {\n\t\t\tsetFavicon();\n\t\t}\n\t});\n};\n\nfunction setFavicon() {\n\tvar tiddler = $tw.wiki.getTiddler(FAVICON_TITLE);\n\tif(tiddler) {\n\t\tvar faviconLink = document.getElementById(\"faviconLink\");\n\t\tfaviconLink.setAttribute(\"href\",\"data:\" + tiddler.fields.type + \";base64,\" + tiddler.fields.text);\n\t}\n}\n\n})();\n",
"title": "$:/core/modules/startup/favicon.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/info.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/info.js\ntype: application/javascript\nmodule-type: startup\n\nInitialise $:/info tiddlers via $:/temp/info-plugin pseudo-plugin\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"info\";\nexports.before = [\"startup\"];\nexports.after = [\"load-modules\"];\nexports.synchronous = true;\n\nexports.startup = function() {\n\t// Collect up the info tiddlers\n\tvar infoTiddlerFields = {};\n\t// Give each info module a chance to fill in as many info tiddlers as they want\n\t$tw.modules.forEachModuleOfType(\"info\",function(title,moduleExports) {\n\t\tif(moduleExports && moduleExports.getInfoTiddlerFields) {\n\t\t\tvar tiddlerFieldsArray = moduleExports.getInfoTiddlerFields(infoTiddlerFields);\n\t\t\t$tw.utils.each(tiddlerFieldsArray,function(fields) {\n\t\t\t\tif(fields) {\n\t\t\t\t\tinfoTiddlerFields[fields.title] = fields;\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n\t// Bake the info tiddlers into a plugin\n\tvar fields = {\n\t\ttitle: \"$:/temp/info-plugin\",\n\t\ttype: \"application/json\",\n\t\t\"plugin-type\": \"info\",\n\t\ttext: JSON.stringify({tiddlers: infoTiddlerFields},null,$tw.config.preferences.jsonSpaces)\n\t};\n\t$tw.wiki.addTiddler(new $tw.Tiddler(fields));\n\t$tw.wiki.readPluginInfo();\n\t$tw.wiki.registerPluginTiddlers(\"info\");\n\t$tw.wiki.unpackPluginTiddlers();\n};\n\n})();\n",
"title": "$:/core/modules/startup/info.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/load-modules.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/load-modules.js\ntype: application/javascript\nmodule-type: startup\n\nLoad core modules\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"load-modules\";\nexports.synchronous = true;\n\nexports.startup = function() {\n\t// Load modules\n\t$tw.modules.applyMethods(\"utils\",$tw.utils);\n\tif($tw.node) {\n\t\t$tw.modules.applyMethods(\"utils-node\",$tw.utils);\n\t}\n\t$tw.modules.applyMethods(\"global\",$tw);\n\t$tw.modules.applyMethods(\"config\",$tw.config);\n\t$tw.Tiddler.fieldModules = $tw.modules.getModulesByTypeAsHashmap(\"tiddlerfield\");\n\t$tw.modules.applyMethods(\"tiddlermethod\",$tw.Tiddler.prototype);\n\t$tw.modules.applyMethods(\"wikimethod\",$tw.Wiki.prototype);\n\t$tw.modules.applyMethods(\"tiddlerdeserializer\",$tw.Wiki.tiddlerDeserializerModules);\n\t$tw.macros = $tw.modules.getModulesByTypeAsHashmap(\"macro\");\n\t$tw.wiki.initParsers();\n\t$tw.Commander.initCommands();\n};\n\n})();\n",
"title": "$:/core/modules/startup/load-modules.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/password.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/password.js\ntype: application/javascript\nmodule-type: startup\n\nPassword handling\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"password\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\nexports.startup = function() {\n\t$tw.rootWidget.addEventListener(\"tm-set-password\",function(event) {\n\t\t$tw.passwordPrompt.createPrompt({\n\t\t\tserviceName: $tw.language.getString(\"Encryption/PromptSetPassword\"),\n\t\t\tnoUserName: true,\n\t\t\tsubmitText: $tw.language.getString(\"Encryption/SetPassword\"),\n\t\t\tcanCancel: true,\n\t\t\trepeatPassword: true,\n\t\t\tcallback: function(data) {\n\t\t\t\tif(data) {\n\t\t\t\t\t$tw.crypto.setPassword(data.password);\n\t\t\t\t}\n\t\t\t\treturn true; // Get rid of the password prompt\n\t\t\t}\n\t\t});\n\t});\n\t$tw.rootWidget.addEventListener(\"tm-clear-password\",function(event) {\n\t\tif($tw.browser) {\n\t\t\tif(!confirm($tw.language.getString(\"Encryption/ConfirmClearPassword\"))) {\n\t\t\t\treturn;\n\t\t\t}\n\t\t}\n\t\t$tw.crypto.setPassword(null);\n\t});\n\t// Ensure that $:/isEncrypted is maintained properly\n\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\tif($tw.utils.hop(changes,\"$:/isEncrypted\")) {\n\t\t\t$tw.crypto.updateCryptoStateTiddler();\n\t\t}\n\t});\n};\n\n})();\n",
"title": "$:/core/modules/startup/password.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/render.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/render.js\ntype: application/javascript\nmodule-type: startup\n\nTitle, stylesheet and page rendering\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"render\";\nexports.platforms = [\"browser\"];\nexports.after = [\"story\"];\nexports.synchronous = true;\n\n// Default story and history lists\nvar PAGE_TITLE_TITLE = \"$:/core/wiki/title\";\nvar PAGE_STYLESHEET_TITLE = \"$:/core/ui/PageStylesheet\";\nvar PAGE_TEMPLATE_TITLE = \"$:/core/ui/PageTemplate\";\n\n// Time (in ms) that we defer refreshing changes to draft tiddlers\nvar DRAFT_TIDDLER_TIMEOUT_TITLE = \"$:/config/Drafts/TypingTimeout\";\nvar DRAFT_TIDDLER_TIMEOUT = 400;\n\nexports.startup = function() {\n\t// Set up the title\n\t$tw.titleWidgetNode = $tw.wiki.makeTranscludeWidget(PAGE_TITLE_TITLE,{document: $tw.fakeDocument, parseAsInline: true});\n\t$tw.titleContainer = $tw.fakeDocument.createElement(\"div\");\n\t$tw.titleWidgetNode.render($tw.titleContainer,null);\n\tdocument.title = $tw.titleContainer.textContent;\n\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\tif($tw.titleWidgetNode.refresh(changes,$tw.titleContainer,null)) {\n\t\t\tdocument.title = $tw.titleContainer.textContent;\n\t\t}\n\t});\n\t// Set up the styles\n\t$tw.styleWidgetNode = $tw.wiki.makeTranscludeWidget(PAGE_STYLESHEET_TITLE,{document: $tw.fakeDocument});\n\t$tw.styleContainer = $tw.fakeDocument.createElement(\"style\");\n\t$tw.styleWidgetNode.render($tw.styleContainer,null);\n\t$tw.styleElement = document.createElement(\"style\");\n\t$tw.styleElement.innerHTML = $tw.styleContainer.textContent;\n\tdocument.head.insertBefore($tw.styleElement,document.head.firstChild);\n\t$tw.wiki.addEventListener(\"change\",$tw.perf.report(\"styleRefresh\",function(changes) {\n\t\tif($tw.styleWidgetNode.refresh(changes,$tw.styleContainer,null)) {\n\t\t\t$tw.styleElement.innerHTML = $tw.styleContainer.textContent;\n\t\t}\n\t}));\n\t// Display the $:/core/ui/PageTemplate tiddler to kick off the display\n\t$tw.perf.report(\"mainRender\",function() {\n\t\t$tw.pageWidgetNode = $tw.wiki.makeTranscludeWidget(PAGE_TEMPLATE_TITLE,{document: document, parentWidget: $tw.rootWidget});\n\t\t$tw.pageContainer = document.createElement(\"div\");\n\t\t$tw.utils.addClass($tw.pageContainer,\"tc-page-container-wrapper\");\n\t\tdocument.body.insertBefore($tw.pageContainer,document.body.firstChild);\n\t\t$tw.pageWidgetNode.render($tw.pageContainer,null);\n\t})();\n\t// Prepare refresh mechanism\n\tvar deferredChanges = Object.create(null),\n\t\ttimerId;\n\tfunction refresh() {\n\t\t// Process the refresh\n\t\t$tw.pageWidgetNode.refresh(deferredChanges);\n\t\tdeferredChanges = Object.create(null);\n\t}\n\t// Add the change event handler\n\t$tw.wiki.addEventListener(\"change\",$tw.perf.report(\"mainRefresh\",function(changes) {\n\t\t// Check if only drafts have changed\n\t\tvar onlyDraftsHaveChanged = true;\n\t\tfor(var title in changes) {\n\t\t\tvar tiddler = $tw.wiki.getTiddler(title);\n\t\t\tif(!tiddler || !tiddler.hasField(\"draft.of\")) {\n\t\t\t\tonlyDraftsHaveChanged = false;\n\t\t\t}\n\t\t}\n\t\t// Defer the change if only drafts have changed\n\t\tif(timerId) {\n\t\t\tclearTimeout(timerId);\n\t\t}\n\t\ttimerId = null;\n\t\tif(onlyDraftsHaveChanged) {\n\t\t\tvar timeout = parseInt($tw.wiki.getTiddlerText(DRAFT_TIDDLER_TIMEOUT_TITLE,\"\"),10);\n\t\t\tif(isNaN(timeout)) {\n\t\t\t\ttimeout = DRAFT_TIDDLER_TIMEOUT;\n\t\t\t}\n\t\t\ttimerId = setTimeout(refresh,timeout);\n\t\t\t$tw.utils.extend(deferredChanges,changes);\n\t\t} else {\n\t\t\t$tw.utils.extend(deferredChanges,changes);\n\t\t\trefresh();\n\t\t}\n\t}));\n\t// Fix up the link between the root widget and the page container\n\t$tw.rootWidget.domNodes = [$tw.pageContainer];\n\t$tw.rootWidget.children = [$tw.pageWidgetNode];\n};\n\n})();\n",
"title": "$:/core/modules/startup/render.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/rootwidget.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/rootwidget.js\ntype: application/javascript\nmodule-type: startup\n\nSetup the root widget and the core root widget handlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"rootwidget\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.before = [\"story\"];\nexports.synchronous = true;\n\nexports.startup = function() {\n\t// Install the modal message mechanism\n\t$tw.modal = new $tw.utils.Modal($tw.wiki);\n\t$tw.rootWidget.addEventListener(\"tm-modal\",function(event) {\n\t\t$tw.modal.display(event.param,{variables: event.paramObject});\n\t});\n\t// Install the notification mechanism\n\t$tw.notifier = new $tw.utils.Notifier($tw.wiki);\n\t$tw.rootWidget.addEventListener(\"tm-notify\",function(event) {\n\t\t$tw.notifier.display(event.param,{variables: event.paramObject});\n\t});\n\t// Install the scroller\n\t$tw.pageScroller = new $tw.utils.PageScroller();\n\t$tw.rootWidget.addEventListener(\"tm-scroll\",function(event) {\n\t\t$tw.pageScroller.handleEvent(event);\n\t});\n\tvar fullscreen = $tw.utils.getFullScreenApis();\n\tif(fullscreen) {\n\t\t$tw.rootWidget.addEventListener(\"tm-full-screen\",function(event) {\n\t\t\tif(document[fullscreen._fullscreenElement]) {\n\t\t\t\tdocument[fullscreen._exitFullscreen]();\n\t\t\t} else {\n\t\t\t\tdocument.documentElement[fullscreen._requestFullscreen](Element.ALLOW_KEYBOARD_INPUT);\n\t\t\t}\n\t\t});\n\t}\n\t// If we're being viewed on a data: URI then give instructions for how to save\n\tif(document.location.protocol === \"data:\") {\n\t\t$tw.rootWidget.dispatchEvent({\n\t\t\ttype: \"tm-modal\",\n\t\t\tparam: \"$:/language/Modals/SaveInstructions\"\n\t\t});\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/startup/rootwidget.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup.js": {
"text": "/*\\\ntitle: $:/core/modules/startup.js\ntype: application/javascript\nmodule-type: startup\n\nMiscellaneous startup logic for both the client and server.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"startup\";\nexports.after = [\"load-modules\"];\nexports.synchronous = true;\n\n// Set to `true` to enable performance instrumentation\nvar PERFORMANCE_INSTRUMENTATION_CONFIG_TITLE = \"$:/config/Performance/Instrumentation\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nexports.startup = function() {\n\tvar modules,n,m,f;\n\t// Minimal browser detection\n\tif($tw.browser) {\n\t\t$tw.browser.isIE = (/msie|trident/i.test(navigator.userAgent));\n\t\t$tw.browser.isFirefox = !!document.mozFullScreenEnabled;\n\t}\n\t// Platform detection\n\t$tw.platform = {};\n\tif($tw.browser) {\n\t\t$tw.platform.isMac = /Mac/.test(navigator.platform);\n\t\t$tw.platform.isWindows = /win/i.test(navigator.platform);\n\t\t$tw.platform.isLinux = /Linux/i.test(navigator.appVersion);\n\t} else {\n\t\tswitch(require(\"os\").platform()) {\n\t\t\tcase \"darwin\":\n\t\t\t\t$tw.platform.isMac = true;\n\t\t\t\tbreak;\n\t\t\tcase \"win32\":\n\t\t\t\t$tw.platform.isWindows = true;\n\t\t\t\tbreak;\n\t\t\tcase \"freebsd\":\n\t\t\t\t$tw.platform.isLinux = true;\n\t\t\t\tbreak;\n\t\t\tcase \"linux\":\n\t\t\t\t$tw.platform.isLinux = true;\n\t\t\t\tbreak;\n\t\t}\n\t}\n\t// Initialise version\n\t$tw.version = $tw.utils.extractVersionInfo();\n\t// Set up the performance framework\n\t$tw.perf = new $tw.Performance($tw.wiki.getTiddlerText(PERFORMANCE_INSTRUMENTATION_CONFIG_TITLE,\"no\") === \"yes\");\n\t// Kick off the language manager and switcher\n\t$tw.language = new $tw.Language();\n\t$tw.languageSwitcher = new $tw.PluginSwitcher({\n\t\twiki: $tw.wiki,\n\t\tpluginType: \"language\",\n\t\tcontrollerTitle: \"$:/language\",\n\t\tdefaultPlugins: [\n\t\t\t\"$:/languages/en-US\"\n\t\t],\n\t\tonSwitch: function(plugins) {\n\t\t\tif($tw.browser) {\n\t\t\t\tvar pluginTiddler = $tw.wiki.getTiddler(plugins[0]);\n\t\t\t\tif(pluginTiddler) {\n\t\t\t\t\tdocument.documentElement.setAttribute(\"dir\",pluginTiddler.getFieldString(\"text-direction\") || \"auto\");\n\t\t\t\t} else {\n\t\t\t\t\tdocument.documentElement.removeAttribute(\"dir\");\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\t// Kick off the theme manager\n\t$tw.themeManager = new $tw.PluginSwitcher({\n\t\twiki: $tw.wiki,\n\t\tpluginType: \"theme\",\n\t\tcontrollerTitle: \"$:/theme\",\n\t\tdefaultPlugins: [\n\t\t\t\"$:/themes/tiddlywiki/snowwhite\",\n\t\t\t\"$:/themes/tiddlywiki/vanilla\"\n\t\t]\n\t});\n\t// Kick off the keyboard manager\n\t$tw.keyboardManager = new $tw.KeyboardManager();\n\t// Clear outstanding tiddler store change events to avoid an unnecessary refresh cycle at startup\n\t$tw.wiki.clearTiddlerEventQueue();\n\t// Create a root widget for attaching event handlers. By using it as the parentWidget for another widget tree, one can reuse the event handlers\n\tif($tw.browser) {\n\t\t$tw.rootWidget = new widget.widget({\n\t\t\ttype: \"widget\",\n\t\t\tchildren: []\n\t\t},{\n\t\t\twiki: $tw.wiki,\n\t\t\tdocument: document\n\t\t});\n\t}\n\t// Find a working syncadaptor\n\t$tw.syncadaptor = undefined;\n\t$tw.modules.forEachModuleOfType(\"syncadaptor\",function(title,module) {\n\t\tif(!$tw.syncadaptor && module.adaptorClass) {\n\t\t\t$tw.syncadaptor = new module.adaptorClass({wiki: $tw.wiki});\n\t\t}\n\t});\n\t// Set up the syncer object if we've got a syncadaptor\n\tif($tw.syncadaptor) {\n\t\t$tw.syncer = new $tw.Syncer({wiki: $tw.wiki, syncadaptor: $tw.syncadaptor});\n\t} \n\t// Setup the saver handler\n\t$tw.saverHandler = new $tw.SaverHandler({wiki: $tw.wiki, dirtyTracking: !$tw.syncadaptor});\n\t// Host-specific startup\n\tif($tw.browser) {\n\t\t// Install the popup manager\n\t\t$tw.popup = new $tw.utils.Popup();\n\t\t// Install the animator\n\t\t$tw.anim = new $tw.utils.Animator();\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/startup.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/story.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/story.js\ntype: application/javascript\nmodule-type: startup\n\nLoad core modules\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"story\";\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\n// Default story and history lists\nvar DEFAULT_STORY_TITLE = \"$:/StoryList\";\nvar DEFAULT_HISTORY_TITLE = \"$:/HistoryList\";\n\n// Default tiddlers\nvar DEFAULT_TIDDLERS_TITLE = \"$:/DefaultTiddlers\";\n\n// Config\nvar CONFIG_UPDATE_ADDRESS_BAR = \"$:/config/Navigation/UpdateAddressBar\"; // Can be \"no\", \"permalink\", \"permaview\"\nvar CONFIG_UPDATE_HISTORY = \"$:/config/Navigation/UpdateHistory\"; // Can be \"yes\" or \"no\"\n\nexports.startup = function() {\n\t// Open startup tiddlers\n\topenStartupTiddlers();\n\tif($tw.browser) {\n\t\t// Set up location hash update\n\t\t$tw.wiki.addEventListener(\"change\",function(changes) {\n\t\t\tif($tw.utils.hop(changes,DEFAULT_STORY_TITLE) || $tw.utils.hop(changes,DEFAULT_HISTORY_TITLE)) {\n\t\t\t\tupdateLocationHash({\n\t\t\t\t\tupdateAddressBar: $tw.wiki.getTiddlerText(CONFIG_UPDATE_ADDRESS_BAR,\"permaview\").trim(),\n\t\t\t\t\tupdateHistory: $tw.wiki.getTiddlerText(CONFIG_UPDATE_HISTORY,\"no\").trim()\n\t\t\t\t});\n\t\t\t}\n\t\t});\n\t\t// Listen for changes to the browser location hash\n\t\twindow.addEventListener(\"hashchange\",function() {\n\t\t\tvar hash = $tw.utils.getLocationHash();\n\t\t\tif(hash !== $tw.locationHash) {\n\t\t\t\t$tw.locationHash = hash;\n\t\t\t\topenStartupTiddlers({defaultToCurrentStory: true});\n\t\t\t}\n\t\t},false);\n\t\t// Listen for the tm-browser-refresh message\n\t\t$tw.rootWidget.addEventListener(\"tm-browser-refresh\",function(event) {\n\t\t\twindow.location.reload(true);\n\t\t});\n\t\t// Listen for the tm-print message\n\t\t$tw.rootWidget.addEventListener(\"tm-print\",function(event) {\n\t\t\t(event.event.view || window).print();\n\t\t});\n\t\t// Listen for the tm-home message\n\t\t$tw.rootWidget.addEventListener(\"tm-home\",function(event) {\n\t\t\twindow.location.hash = \"\";\n\t\t\tvar storyFilter = $tw.wiki.getTiddlerText(DEFAULT_TIDDLERS_TITLE),\n\t\t\t\tstoryList = $tw.wiki.filterTiddlers(storyFilter);\n\t\t\t//invoke any hooks that might change the default story list\n\t\t\tstoryList = $tw.hooks.invokeHook(\"th-opening-default-tiddlers-list\",storyList);\n\t\t\t$tw.wiki.addTiddler({title: DEFAULT_STORY_TITLE, text: \"\", list: storyList},$tw.wiki.getModificationFields());\n\t\t\tif(storyList[0]) {\n\t\t\t\t$tw.wiki.addToHistory(storyList[0]);\t\t\t\t\n\t\t\t}\n\t\t});\n\t\t// Listen for the tm-permalink message\n\t\t$tw.rootWidget.addEventListener(\"tm-permalink\",function(event) {\n\t\t\tupdateLocationHash({\n\t\t\t\tupdateAddressBar: \"permalink\",\n\t\t\t\tupdateHistory: $tw.wiki.getTiddlerText(CONFIG_UPDATE_HISTORY,\"no\").trim(),\n\t\t\t\ttargetTiddler: event.param || event.tiddlerTitle\n\t\t\t});\n\t\t});\n\t\t// Listen for the tm-permaview message\n\t\t$tw.rootWidget.addEventListener(\"tm-permaview\",function(event) {\n\t\t\tupdateLocationHash({\n\t\t\t\tupdateAddressBar: \"permaview\",\n\t\t\t\tupdateHistory: $tw.wiki.getTiddlerText(CONFIG_UPDATE_HISTORY,\"no\").trim(),\n\t\t\t\ttargetTiddler: event.param || event.tiddlerTitle\n\t\t\t});\n\t\t});\n\t}\n};\n\n/*\nProcess the location hash to open the specified tiddlers. Options:\ndefaultToCurrentStory: If true, the current story is retained as the default, instead of opening the default tiddlers\n*/\nfunction openStartupTiddlers(options) {\n\toptions = options || {};\n\t// Work out the target tiddler and the story filter. \"null\" means \"unspecified\"\n\tvar target = null,\n\t\tstoryFilter = null;\n\tif($tw.locationHash.length > 1) {\n\t\tvar hash = $tw.locationHash.substr(1),\n\t\t\tsplit = hash.indexOf(\":\");\n\t\tif(split === -1) {\n\t\t\ttarget = decodeURIComponent(hash.trim());\n\t\t} else {\n\t\t\ttarget = decodeURIComponent(hash.substr(0,split).trim());\n\t\t\tstoryFilter = decodeURIComponent(hash.substr(split + 1).trim());\n\t\t}\n\t}\n\t// If the story wasn't specified use the current tiddlers or a blank story\n\tif(storyFilter === null) {\n\t\tif(options.defaultToCurrentStory) {\n\t\t\tvar currStoryList = $tw.wiki.getTiddlerList(DEFAULT_STORY_TITLE);\n\t\t\tstoryFilter = $tw.utils.stringifyList(currStoryList);\n\t\t} else {\n\t\t\tif(target && target !== \"\") {\n\t\t\t\tstoryFilter = \"\";\n\t\t\t} else {\n\t\t\t\tstoryFilter = $tw.wiki.getTiddlerText(DEFAULT_TIDDLERS_TITLE);\n\t\t\t}\n\t\t}\n\t}\n\t// Process the story filter to get the story list\n\tvar storyList = $tw.wiki.filterTiddlers(storyFilter);\n\t// Invoke any hooks that want to change the default story list\n\tstoryList = $tw.hooks.invokeHook(\"th-opening-default-tiddlers-list\",storyList);\n\t// If the target tiddler isn't included then splice it in at the top\n\tif(target && storyList.indexOf(target) === -1) {\n\t\tstoryList.unshift(target);\n\t}\n\t// Save the story list\n\t$tw.wiki.addTiddler({title: DEFAULT_STORY_TITLE, text: \"\", list: storyList},$tw.wiki.getModificationFields());\n\t// If a target tiddler was specified add it to the history stack\n\tif(target && target !== \"\") {\n\t\t// The target tiddler doesn't need double square brackets, but we'll silently remove them if they're present\n\t\tif(target.indexOf(\"[[\") === 0 && target.substr(-2) === \"]]\") {\n\t\t\ttarget = target.substr(2,target.length - 4);\n\t\t}\n\t\t$tw.wiki.addToHistory(target);\n\t} else if(storyList.length > 0) {\n\t\t$tw.wiki.addToHistory(storyList[0]);\n\t}\n}\n\n/*\noptions: See below\noptions.updateAddressBar: \"permalink\", \"permaview\" or \"no\" (defaults to \"permaview\")\noptions.updateHistory: \"yes\" or \"no\" (defaults to \"no\")\noptions.targetTiddler: optional title of target tiddler for permalink\n*/\nfunction updateLocationHash(options) {\n\tif(options.updateAddressBar !== \"no\") {\n\t\t// Get the story and the history stack\n\t\tvar storyList = $tw.wiki.getTiddlerList(DEFAULT_STORY_TITLE),\n\t\t\thistoryList = $tw.wiki.getTiddlerData(DEFAULT_HISTORY_TITLE,[]),\n\t\t\ttargetTiddler = \"\";\n\t\tif(options.targetTiddler) {\n\t\t\ttargetTiddler = options.targetTiddler;\n\t\t} else {\n\t\t\t// The target tiddler is the one at the top of the stack\n\t\t\tif(historyList.length > 0) {\n\t\t\t\ttargetTiddler = historyList[historyList.length-1].title;\n\t\t\t}\n\t\t\t// Blank the target tiddler if it isn't present in the story\n\t\t\tif(storyList.indexOf(targetTiddler) === -1) {\n\t\t\t\ttargetTiddler = \"\";\n\t\t\t}\n\t\t}\n\t\t// Assemble the location hash\n\t\tif(options.updateAddressBar === \"permalink\") {\n\t\t\t$tw.locationHash = \"#\" + encodeURIComponent(targetTiddler);\n\t\t} else {\n\t\t\t$tw.locationHash = \"#\" + encodeURIComponent(targetTiddler) + \":\" + encodeURIComponent($tw.utils.stringifyList(storyList));\n\t\t}\n\t\t// Only change the location hash if we must, thus avoiding unnecessary onhashchange events\n\t\tif($tw.utils.getLocationHash() !== $tw.locationHash) {\n\t\t\tif(options.updateHistory === \"yes\") {\n\t\t\t\t// Assign the location hash so that history is updated\n\t\t\t\twindow.location.hash = $tw.locationHash;\n\t\t\t} else {\n\t\t\t\t// We use replace so that browser history isn't affected\n\t\t\t\twindow.location.replace(window.location.toString().split(\"#\")[0] + $tw.locationHash);\n\t\t\t}\n\t\t}\n\t}\n}\n\n})();\n",
"title": "$:/core/modules/startup/story.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/startup/windows.js": {
"text": "/*\\\ntitle: $:/core/modules/startup/windows.js\ntype: application/javascript\nmodule-type: startup\n\nSetup root widget handlers for the messages concerned with opening external browser windows\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Export name and synchronous status\nexports.name = \"windows\";\nexports.platforms = [\"browser\"];\nexports.after = [\"startup\"];\nexports.synchronous = true;\n\n// Global to keep track of open windows (hashmap by title)\nvar windows = {};\n\nexports.startup = function() {\n\t// Handle open window message\n\t$tw.rootWidget.addEventListener(\"tm-open-window\",function(event) {\n\t\t// Get the parameters\n\t\tvar refreshHandler,\n\t\t\ttitle = event.param || event.tiddlerTitle,\n\t\t\tparamObject = event.paramObject || {},\n\t\t\ttemplate = paramObject.template || \"$:/core/templates/single.tiddler.window\",\n\t\t\twidth = paramObject.width || \"700\",\n\t\t\theight = paramObject.height || \"600\",\n\t\t\tvariables = $tw.utils.extend({},paramObject,{currentTiddler: title});\n\t\t// Open the window\n\t\tvar srcWindow = window.open(\"\",\"external-\" + title,\"scrollbars,width=\" + width + \",height=\" + height),\n\t\t\tsrcDocument = srcWindow.document;\n\t\twindows[title] = srcWindow;\n\t\t// Check for reopening the same window\n\t\tif(srcWindow.haveInitialisedWindow) {\n\t\t\treturn;\n\t\t}\n\t\t// Initialise the document\n\t\tsrcDocument.write(\"<html><head></head><body class='tc-body tc-single-tiddler-window'></body></html>\");\n\t\tsrcDocument.close();\n\t\tsrcDocument.title = title;\n\t\tsrcWindow.addEventListener(\"beforeunload\",function(event) {\n\t\t\tdelete windows[title];\n\t\t\t$tw.wiki.removeEventListener(\"change\",refreshHandler);\n\t\t},false);\n\t\t// Set up the styles\n\t\tvar styleWidgetNode = $tw.wiki.makeTranscludeWidget(\"$:/core/ui/PageStylesheet\",{\n\t\t\t\tdocument: $tw.fakeDocument,\n\t\t\t\tvariables: variables,\n\t\t\t\timportPageMacros: true}),\n\t\t\tstyleContainer = $tw.fakeDocument.createElement(\"style\");\n\t\tstyleWidgetNode.render(styleContainer,null);\n\t\tvar styleElement = srcDocument.createElement(\"style\");\n\t\tstyleElement.innerHTML = styleContainer.textContent;\n\t\tsrcDocument.head.insertBefore(styleElement,srcDocument.head.firstChild);\n\t\t// Render the text of the tiddler\n\t\tvar parser = $tw.wiki.parseTiddler(template),\n\t\t\twidgetNode = $tw.wiki.makeWidget(parser,{document: srcDocument, parentWidget: $tw.rootWidget, variables: variables});\n\t\twidgetNode.render(srcDocument.body,srcDocument.body.firstChild);\n\t\t// Function to handle refreshes\n\t\trefreshHandler = function(changes) {\n\t\t\tif(styleWidgetNode.refresh(changes,styleContainer,null)) {\n\t\t\t\tstyleElement.innerHTML = styleContainer.textContent;\n\t\t\t}\n\t\t\twidgetNode.refresh(changes);\n\t\t};\n\t\t$tw.wiki.addEventListener(\"change\",refreshHandler);\n\t\tsrcWindow.haveInitialisedWindow = true;\n\t});\n\t// Close open windows when unloading main window\n\t$tw.addUnloadTask(function() {\n\t\t$tw.utils.each(windows,function(win) {\n\t\t\twin.close();\n\t\t});\n\t});\n\n};\n\n})();\n",
"title": "$:/core/modules/startup/windows.js",
"type": "application/javascript",
"module-type": "startup"
},
"$:/core/modules/story.js": {
"text": "/*\\\ntitle: $:/core/modules/story.js\ntype: application/javascript\nmodule-type: global\n\nLightweight object for managing interactions with the story and history lists.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nConstruct Story object with options:\nwiki: reference to wiki object to use to resolve tiddler titles\nstoryTitle: title of story list tiddler\nhistoryTitle: title of history list tiddler\n*/\nfunction Story(options) {\n\toptions = options || {};\n\tthis.wiki = options.wiki || $tw.wiki;\n\tthis.storyTitle = options.storyTitle || \"$:/StoryList\";\n\tthis.historyTitle = options.historyTitle || \"$:/HistoryList\";\n};\n\nStory.prototype.navigateTiddler = function(navigateTo,navigateFromTitle,navigateFromClientRect) {\n\tthis.addToStory(navigateTo,navigateFromTitle);\n\tthis.addToHistory(navigateTo,navigateFromClientRect);\n};\n\nStory.prototype.getStoryList = function() {\n\treturn this.wiki.getTiddlerList(this.storyTitle) || [];\n};\n\nStory.prototype.addToStory = function(navigateTo,navigateFromTitle,options) {\n\toptions = options || {};\n\tvar storyList = this.getStoryList();\n\t// See if the tiddler is already there\n\tvar slot = storyList.indexOf(navigateTo);\n\t// Quit if it already exists in the story river\n\tif(slot >= 0) {\n\t\treturn;\n\t}\n\t// First we try to find the position of the story element we navigated from\n\tvar fromIndex = storyList.indexOf(navigateFromTitle);\n\tif(fromIndex >= 0) {\n\t\t// The tiddler is added from inside the river\n\t\t// Determine where to insert the tiddler; Fallback is \"below\"\n\t\tswitch(options.openLinkFromInsideRiver) {\n\t\t\tcase \"top\":\n\t\t\t\tslot = 0;\n\t\t\t\tbreak;\n\t\t\tcase \"bottom\":\n\t\t\t\tslot = storyList.length;\n\t\t\t\tbreak;\n\t\t\tcase \"above\":\n\t\t\t\tslot = fromIndex;\n\t\t\t\tbreak;\n\t\t\tcase \"below\": // Intentional fall-through\n\t\t\tdefault:\n\t\t\t\tslot = fromIndex + 1;\n\t\t\t\tbreak;\n\t\t}\n\t} else {\n\t\t// The tiddler is opened from outside the river. Determine where to insert the tiddler; default is \"top\"\n\t\tif(options.openLinkFromOutsideRiver === \"bottom\") {\n\t\t\t// Insert at bottom\n\t\t\tslot = storyList.length;\n\t\t} else {\n\t\t\t// Insert at top\n\t\t\tslot = 0;\n\t\t}\n\t}\n\t// Add the tiddler\n\tstoryList.splice(slot,0,navigateTo);\n\t// Save the story\n\tthis.saveStoryList(storyList);\n};\n\nStory.prototype.saveStoryList = function(storyList) {\n\tvar storyTiddler = this.wiki.getTiddler(this.storyTitle);\n\tthis.wiki.addTiddler(new $tw.Tiddler(\n\t\tthis.wiki.getCreationFields(),\n\t\t{title: this.storyTitle},\n\t\tstoryTiddler,\n\t\t{list: storyList},\n\t\tthis.wiki.getModificationFields()\n\t));\n};\n\nStory.prototype.addToHistory = function(navigateTo,navigateFromClientRect) {\n\tvar titles = $tw.utils.isArray(navigateTo) ? navigateTo : [navigateTo];\n\t// Add a new record to the top of the history stack\n\tvar historyList = this.wiki.getTiddlerData(this.historyTitle,[]);\n\t$tw.utils.each(titles,function(title) {\n\t\thistoryList.push({title: title, fromPageRect: navigateFromClientRect});\n\t});\n\tthis.wiki.setTiddlerData(this.historyTitle,historyList,{\"current-tiddler\": titles[titles.length-1]});\n};\n\nStory.prototype.storyCloseTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyCloseAllTiddlers = function() {\n// TBD\n};\n\nStory.prototype.storyCloseOtherTiddlers = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyEditTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyDeleteTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storySaveTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyCancelTiddler = function(targetTitle) {\n// TBD\n};\n\nStory.prototype.storyNewTiddler = function(targetTitle) {\n// TBD\n};\n\nexports.Story = Story;\n\n\n})();\n",
"title": "$:/core/modules/story.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/storyviews/classic.js": {
"text": "/*\\\ntitle: $:/core/modules/storyviews/classic.js\ntype: application/javascript\nmodule-type: storyview\n\nViews the story as a linear sequence\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar easing = \"cubic-bezier(0.645, 0.045, 0.355, 1)\"; // From http://easings.net/#easeInOutCubic\n\nvar ClassicStoryView = function(listWidget) {\n\tthis.listWidget = listWidget;\n};\n\nClassicStoryView.prototype.navigateTo = function(historyInfo) {\n\tvar listElementIndex = this.listWidget.findListItem(0,historyInfo.title);\n\tif(listElementIndex === undefined) {\n\t\treturn;\n\t}\n\tvar listItemWidget = this.listWidget.children[listElementIndex],\n\t\ttargetElement = listItemWidget.findFirstDomNode();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Scroll the node into view\n\tthis.listWidget.dispatchEvent({type: \"tm-scroll\", target: targetElement});\n};\n\nClassicStoryView.prototype.insert = function(widget) {\n\tvar targetElement = widget.findFirstDomNode(),\n\t\tduration = $tw.utils.getAnimationDuration();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Get the current height of the tiddler\n\tvar computedStyle = window.getComputedStyle(targetElement),\n\t\tcurrMarginBottom = parseInt(computedStyle.marginBottom,10),\n\t\tcurrMarginTop = parseInt(computedStyle.marginTop,10),\n\t\tcurrHeight = targetElement.offsetHeight + currMarginTop;\n\t// Reset the margin once the transition is over\n\tsetTimeout(function() {\n\t\t$tw.utils.setStyle(targetElement,[\n\t\t\t{transition: \"none\"},\n\t\t\t{marginBottom: \"\"}\n\t\t]);\n\t},duration);\n\t// Set up the initial position of the element\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: \"none\"},\n\t\t{marginBottom: (-currHeight) + \"px\"},\n\t\t{opacity: \"0.0\"}\n\t]);\n\t$tw.utils.forceLayout(targetElement);\n\t// Transition to the final position\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: \"opacity \" + duration + \"ms \" + easing + \", \" +\n\t\t\t\t\t\"margin-bottom \" + duration + \"ms \" + easing},\n\t\t{marginBottom: currMarginBottom + \"px\"},\n\t\t{opacity: \"1.0\"}\n\t]);\n};\n\nClassicStoryView.prototype.remove = function(widget) {\n\tvar targetElement = widget.findFirstDomNode(),\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\tremoveElement = function() {\n\t\t\twidget.removeChildDomNodes();\n\t\t};\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\tremoveElement();\n\t\treturn;\n\t}\n\t// Get the current height of the tiddler\n\tvar currWidth = targetElement.offsetWidth,\n\t\tcomputedStyle = window.getComputedStyle(targetElement),\n\t\tcurrMarginBottom = parseInt(computedStyle.marginBottom,10),\n\t\tcurrMarginTop = parseInt(computedStyle.marginTop,10),\n\t\tcurrHeight = targetElement.offsetHeight + currMarginTop;\n\t// Remove the dom nodes of the widget at the end of the transition\n\tsetTimeout(removeElement,duration);\n\t// Animate the closure\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: \"none\"},\n\t\t{transform: \"translateX(0px)\"},\n\t\t{marginBottom: currMarginBottom + \"px\"},\n\t\t{opacity: \"1.0\"}\n\t]);\n\t$tw.utils.forceLayout(targetElement);\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms \" + easing + \", \" +\n\t\t\t\t\t\"margin-bottom \" + duration + \"ms \" + easing},\n\t\t{transform: \"translateX(-\" + currWidth + \"px)\"},\n\t\t{marginBottom: (-currHeight) + \"px\"},\n\t\t{opacity: \"0.0\"}\n\t]);\n};\n\nexports.classic = ClassicStoryView;\n\n})();",
"title": "$:/core/modules/storyviews/classic.js",
"type": "application/javascript",
"module-type": "storyview"
},
"$:/core/modules/storyviews/pop.js": {
"text": "/*\\\ntitle: $:/core/modules/storyviews/pop.js\ntype: application/javascript\nmodule-type: storyview\n\nAnimates list insertions and removals\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar PopStoryView = function(listWidget) {\n\tthis.listWidget = listWidget;\n};\n\nPopStoryView.prototype.navigateTo = function(historyInfo) {\n\tvar listElementIndex = this.listWidget.findListItem(0,historyInfo.title);\n\tif(listElementIndex === undefined) {\n\t\treturn;\n\t}\n\tvar listItemWidget = this.listWidget.children[listElementIndex],\n\t\ttargetElement = listItemWidget.findFirstDomNode();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Scroll the node into view\n\tthis.listWidget.dispatchEvent({type: \"tm-scroll\", target: targetElement});\n};\n\nPopStoryView.prototype.insert = function(widget) {\n\tvar targetElement = widget.findFirstDomNode(),\n\t\tduration = $tw.utils.getAnimationDuration();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Reset once the transition is over\n\tsetTimeout(function() {\n\t\t$tw.utils.setStyle(targetElement,[\n\t\t\t{transition: \"none\"},\n\t\t\t{transform: \"none\"}\n\t\t]);\n\t},duration);\n\t// Set up the initial position of the element\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: \"none\"},\n\t\t{transform: \"scale(2)\"},\n\t\t{opacity: \"0.0\"}\n\t]);\n\t$tw.utils.forceLayout(targetElement);\n\t// Transition to the final position\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms ease-in-out\"},\n\t\t{transform: \"scale(1)\"},\n\t\t{opacity: \"1.0\"}\n\t]);\n};\n\nPopStoryView.prototype.remove = function(widget) {\n\tvar targetElement = widget.findFirstDomNode(),\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\tremoveElement = function() {\n\t\t\tif(targetElement.parentNode) {\n\t\t\t\twidget.removeChildDomNodes();\n\t\t\t}\n\t\t};\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\tremoveElement();\n\t\treturn;\n\t}\n\t// Remove the element at the end of the transition\n\tsetTimeout(removeElement,duration);\n\t// Animate the closure\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: \"none\"},\n\t\t{transform: \"scale(1)\"},\n\t\t{opacity: \"1.0\"}\n\t]);\n\t$tw.utils.forceLayout(targetElement);\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms ease-in-out\"},\n\t\t{transform: \"scale(0.1)\"},\n\t\t{opacity: \"0.0\"}\n\t]);\n};\n\nexports.pop = PopStoryView;\n\n})();\n",
"title": "$:/core/modules/storyviews/pop.js",
"type": "application/javascript",
"module-type": "storyview"
},
"$:/core/modules/storyviews/zoomin.js": {
"text": "/*\\\ntitle: $:/core/modules/storyviews/zoomin.js\ntype: application/javascript\nmodule-type: storyview\n\nZooms between individual tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar easing = \"cubic-bezier(0.645, 0.045, 0.355, 1)\"; // From http://easings.net/#easeInOutCubic\n\nvar ZoominListView = function(listWidget) {\n\tvar self = this;\n\tthis.listWidget = listWidget;\n\t// Get the index of the tiddler that is at the top of the history\n\tvar history = this.listWidget.wiki.getTiddlerDataCached(this.listWidget.historyTitle,[]),\n\t\ttargetTiddler;\n\tif(history.length > 0) {\n\t\ttargetTiddler = history[history.length-1].title;\n\t}\n\t// Make all the tiddlers position absolute, and hide all but the top (or first) one\n\t$tw.utils.each(this.listWidget.children,function(itemWidget,index) {\n\t\tvar domNode = itemWidget.findFirstDomNode();\n\t\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\t\tif(!(domNode instanceof Element)) {\n\t\t\treturn;\n\t\t}\n\t\tif((targetTiddler && targetTiddler !== itemWidget.parseTreeNode.itemTitle) || (!targetTiddler && index)) {\n\t\t\tdomNode.style.display = \"none\";\n\t\t} else {\n\t\t\tself.currentTiddlerDomNode = domNode;\n\t\t}\n\t\t$tw.utils.addClass(domNode,\"tc-storyview-zoomin-tiddler\");\n\t});\n};\n\nZoominListView.prototype.navigateTo = function(historyInfo) {\n\tvar duration = $tw.utils.getAnimationDuration(),\n\t\tlistElementIndex = this.listWidget.findListItem(0,historyInfo.title);\n\tif(listElementIndex === undefined) {\n\t\treturn;\n\t}\n\tvar listItemWidget = this.listWidget.children[listElementIndex],\n\t\ttargetElement = listItemWidget.findFirstDomNode();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Make the new tiddler be position absolute and visible so that we can measure it\n\t$tw.utils.addClass(targetElement,\"tc-storyview-zoomin-tiddler\");\n\t$tw.utils.setStyle(targetElement,[\n\t\t{display: \"block\"},\n\t\t{transformOrigin: \"0 0\"},\n\t\t{transform: \"translateX(0px) translateY(0px) scale(1)\"},\n\t\t{transition: \"none\"},\n\t\t{opacity: \"0.0\"}\n\t]);\n\t// Get the position of the source node, or use the centre of the window as the source position\n\tvar sourceBounds = historyInfo.fromPageRect || {\n\t\t\tleft: window.innerWidth/2 - 2,\n\t\t\ttop: window.innerHeight/2 - 2,\n\t\t\twidth: window.innerWidth/8,\n\t\t\theight: window.innerHeight/8\n\t\t};\n\t// Try to find the title node in the target tiddler\n\tvar titleDomNode = findTitleDomNode(listItemWidget) || listItemWidget.findFirstDomNode(),\n\t\tzoomBounds = titleDomNode.getBoundingClientRect();\n\t// Compute the transform for the target tiddler to make the title lie over the source rectange\n\tvar targetBounds = targetElement.getBoundingClientRect(),\n\t\tscale = sourceBounds.width / zoomBounds.width,\n\t\tx = sourceBounds.left - targetBounds.left - (zoomBounds.left - targetBounds.left) * scale,\n\t\ty = sourceBounds.top - targetBounds.top - (zoomBounds.top - targetBounds.top) * scale;\n\t// Transform the target tiddler to its starting position\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transform: \"translateX(\" + x + \"px) translateY(\" + y + \"px) scale(\" + scale + \")\"}\n\t]);\n\t// Force layout\n\t$tw.utils.forceLayout(targetElement);\n\t// Apply the ending transitions with a timeout to ensure that the previously applied transformations are applied first\n\tvar self = this,\n\t\tprevCurrentTiddler = this.currentTiddlerDomNode;\n\tthis.currentTiddlerDomNode = targetElement;\n\t// Transform the target tiddler to its natural size\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", opacity \" + duration + \"ms \" + easing},\n\t\t{opacity: \"1.0\"},\n\t\t{transform: \"translateX(0px) translateY(0px) scale(1)\"},\n\t\t{zIndex: \"500\"},\n\t]);\n\t// Transform the previous tiddler out of the way and then hide it\n\tif(prevCurrentTiddler && prevCurrentTiddler !== targetElement) {\n\t\tscale = zoomBounds.width / sourceBounds.width;\n\t\tx = zoomBounds.left - targetBounds.left - (sourceBounds.left - targetBounds.left) * scale;\n\t\ty = zoomBounds.top - targetBounds.top - (sourceBounds.top - targetBounds.top) * scale;\n\t\t$tw.utils.setStyle(prevCurrentTiddler,[\n\t\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", opacity \" + duration + \"ms \" + easing},\n\t\t\t{opacity: \"0.0\"},\n\t\t\t{transformOrigin: \"0 0\"},\n\t\t\t{transform: \"translateX(\" + x + \"px) translateY(\" + y + \"px) scale(\" + scale + \")\"},\n\t\t\t{zIndex: \"0\"}\n\t\t]);\n\t\t// Hide the tiddler when the transition has finished\n\t\tsetTimeout(function() {\n\t\t\tif(self.currentTiddlerDomNode !== prevCurrentTiddler) {\n\t\t\t\tprevCurrentTiddler.style.display = \"none\";\n\t\t\t}\n\t\t},duration);\n\t}\n\t// Scroll the target into view\n//\t$tw.pageScroller.scrollIntoView(targetElement);\n};\n\n/*\nFind the first child DOM node of a widget that has the class \"tc-title\"\n*/\nfunction findTitleDomNode(widget,targetClass) {\n\ttargetClass = targetClass || \"tc-title\";\n\tvar domNode = widget.findFirstDomNode();\n\tif(domNode && domNode.querySelector) {\n\t\treturn domNode.querySelector(\".\" + targetClass);\n\t}\n\treturn null;\n}\n\nZoominListView.prototype.insert = function(widget) {\n\tvar targetElement = widget.findFirstDomNode();\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\treturn;\n\t}\n\t// Make the newly inserted node position absolute and hidden\n\t$tw.utils.addClass(targetElement,\"tc-storyview-zoomin-tiddler\");\n\t$tw.utils.setStyle(targetElement,[\n\t\t{display: \"none\"}\n\t]);\n};\n\nZoominListView.prototype.remove = function(widget) {\n\tvar targetElement = widget.findFirstDomNode(),\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\tremoveElement = function() {\n\t\t\twidget.removeChildDomNodes();\n\t\t};\n\t// Abandon if the list entry isn't a DOM element (it might be a text node)\n\tif(!(targetElement instanceof Element)) {\n\t\tremoveElement();\n\t\treturn;\n\t}\n\t// Abandon if hidden\n\tif(targetElement.style.display != \"block\" ) {\n\t\tremoveElement();\n\t\treturn;\n\t}\n\t// Set up the tiddler that is being closed\n\t$tw.utils.addClass(targetElement,\"tc-storyview-zoomin-tiddler\");\n\t$tw.utils.setStyle(targetElement,[\n\t\t{display: \"block\"},\n\t\t{transformOrigin: \"50% 50%\"},\n\t\t{transform: \"translateX(0px) translateY(0px) scale(1)\"},\n\t\t{transition: \"none\"},\n\t\t{zIndex: \"0\"}\n\t]);\n\t// We'll move back to the previous or next element in the story\n\tvar toWidget = widget.previousSibling();\n\tif(!toWidget) {\n\t\ttoWidget = widget.nextSibling();\n\t}\n\tvar toWidgetDomNode = toWidget && toWidget.findFirstDomNode();\n\t// Set up the tiddler we're moving back in\n\tif(toWidgetDomNode) {\n\t\t$tw.utils.addClass(toWidgetDomNode,\"tc-storyview-zoomin-tiddler\");\n\t\t$tw.utils.setStyle(toWidgetDomNode,[\n\t\t\t{display: \"block\"},\n\t\t\t{transformOrigin: \"50% 50%\"},\n\t\t\t{transform: \"translateX(0px) translateY(0px) scale(10)\"},\n\t\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", opacity \" + duration + \"ms \" + easing},\n\t\t\t{opacity: \"0\"},\n\t\t\t{zIndex: \"500\"}\n\t\t]);\n\t\tthis.currentTiddlerDomNode = toWidgetDomNode;\n\t}\n\t// Animate them both\n\t// Force layout\n\t$tw.utils.forceLayout(this.listWidget.parentDomNode);\n\t// First, the tiddler we're closing\n\t$tw.utils.setStyle(targetElement,[\n\t\t{transformOrigin: \"50% 50%\"},\n\t\t{transform: \"translateX(0px) translateY(0px) scale(0.1)\"},\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms \" + easing + \", opacity \" + duration + \"ms \" + easing},\n\t\t{opacity: \"0\"},\n\t\t{zIndex: \"0\"}\n\t]);\n\tsetTimeout(removeElement,duration);\n\t// Now the tiddler we're going back to\n\tif(toWidgetDomNode) {\n\t\t$tw.utils.setStyle(toWidgetDomNode,[\n\t\t\t{transform: \"translateX(0px) translateY(0px) scale(1)\"},\n\t\t\t{opacity: \"1\"}\n\t\t]);\n\t}\n\treturn true; // Indicate that we'll delete the DOM node\n};\n\nexports.zoomin = ZoominListView;\n\n})();\n",
"title": "$:/core/modules/storyviews/zoomin.js",
"type": "application/javascript",
"module-type": "storyview"
},
"$:/core/modules/syncer.js": {
"text": "/*\\\ntitle: $:/core/modules/syncer.js\ntype: application/javascript\nmodule-type: global\n\nThe syncer tracks changes to the store. If a syncadaptor is used then individual tiddlers are synchronised through it. If there is no syncadaptor then the entire wiki is saved via saver modules.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nDefaults\n*/\nSyncer.prototype.titleIsLoggedIn = \"$:/status/IsLoggedIn\";\nSyncer.prototype.titleUserName = \"$:/status/UserName\";\nSyncer.prototype.titleSyncFilter = \"$:/config/SyncFilter\";\nSyncer.prototype.titleSavedNotification = \"$:/language/Notifications/Save/Done\";\nSyncer.prototype.taskTimerInterval = 1 * 1000; // Interval for sync timer\nSyncer.prototype.throttleInterval = 1 * 1000; // Defer saving tiddlers if they've changed in the last 1s...\nSyncer.prototype.fallbackInterval = 10 * 1000; // Unless the task is older than 10s\nSyncer.prototype.pollTimerInterval = 60 * 1000; // Interval for polling for changes from the adaptor\n\n/*\nInstantiate the syncer with the following options:\nsyncadaptor: reference to syncadaptor to be used\nwiki: wiki to be synced\n*/\nfunction Syncer(options) {\n\tvar self = this;\n\tthis.wiki = options.wiki;\n\tthis.syncadaptor = options.syncadaptor;\n\tthis.titleIsLoggedIn = options.titleIsLoggedIn || this.titleIsLoggedIn;\n\tthis.titleUserName = options.titleUserName || this.titleUserName;\n\tthis.titleSyncFilter = options.titleSyncFilter || this.titleSyncFilter;\n\tthis.titleSavedNotification = options.titleSavedNotification || this.titleSavedNotification;\n\tthis.taskTimerInterval = options.taskTimerInterval || this.taskTimerInterval;\n\tthis.throttleInterval = options.throttleInterval || this.throttleInterval;\n\tthis.fallbackInterval = options.fallbackInterval || this.fallbackInterval;\n\tthis.pollTimerInterval = options.pollTimerInterval || this.pollTimerInterval;\n\t// Make a logger\n\tthis.logger = new $tw.utils.Logger(\"syncer\" + ($tw.browser ? \"-browser\" : \"\") + ($tw.node ? \"-server\" : \"\") + (this.syncadaptor.name ? (\"-\" + this.syncadaptor.name) : \"\"));\n\t// Compile the dirty tiddler filter\n\tthis.filterFn = this.wiki.compileFilter(this.wiki.getTiddlerText(this.titleSyncFilter));\n\t// Record information for known tiddlers\n\tthis.readTiddlerInfo();\n\t// Tasks are {type: \"load\"/\"save\"/\"delete\", title:, queueTime:, lastModificationTime:}\n\tthis.taskQueue = {}; // Hashmap of tasks yet to be performed\n\tthis.taskInProgress = {}; // Hash of tasks in progress\n\tthis.taskTimerId = null; // Timer for task dispatch\n\tthis.pollTimerId = null; // Timer for polling server\n\t// Listen out for changes to tiddlers\n\tthis.wiki.addEventListener(\"change\",function(changes) {\n\t\tself.syncToServer(changes);\n\t});\n\t// Browser event handlers\n\tif($tw.browser) {\n\t\t// Set up our beforeunload handler\n\t\t$tw.addUnloadTask(function(event) {\n\t\t\tvar confirmationMessage;\n\t\t\tif(self.isDirty()) {\n\t\t\t\tconfirmationMessage = $tw.language.getString(\"UnsavedChangesWarning\");\n\t\t\t\tevent.returnValue = confirmationMessage; // Gecko\n\t\t\t}\n\t\t\treturn confirmationMessage;\n\t\t});\n\t\t// Listen out for login/logout/refresh events in the browser\n\t\t$tw.rootWidget.addEventListener(\"tm-login\",function() {\n\t\t\tself.handleLoginEvent();\n\t\t});\n\t\t$tw.rootWidget.addEventListener(\"tm-logout\",function() {\n\t\t\tself.handleLogoutEvent();\n\t\t});\n\t\t$tw.rootWidget.addEventListener(\"tm-server-refresh\",function() {\n\t\t\tself.handleRefreshEvent();\n\t\t});\n\t}\n\t// Listen out for lazyLoad events\n\tthis.wiki.addEventListener(\"lazyLoad\",function(title) {\n\t\tself.handleLazyLoadEvent(title);\n\t});\n\t// Get the login status\n\tthis.getStatus(function(err,isLoggedIn) {\n\t\t// Do a sync from the server\n\t\tself.syncFromServer();\n\t});\n}\n\n/*\nRead (or re-read) the latest tiddler info from the store\n*/\nSyncer.prototype.readTiddlerInfo = function() {\n\t// Hashmap by title of {revision:,changeCount:,adaptorInfo:}\n\tthis.tiddlerInfo = {};\n\t// Record information for known tiddlers\n\tvar self = this,\n\t\ttiddlers = this.filterFn.call(this.wiki);\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = self.wiki.getTiddler(title);\n\t\tself.tiddlerInfo[title] = {\n\t\t\trevision: tiddler.fields.revision,\n\t\t\tadaptorInfo: self.syncadaptor && self.syncadaptor.getTiddlerInfo(tiddler),\n\t\t\tchangeCount: self.wiki.getChangeCount(title),\n\t\t\thasBeenLazyLoaded: false\n\t\t};\n\t});\n};\n\n/*\nCreate an tiddlerInfo structure if it doesn't already exist\n*/\nSyncer.prototype.createTiddlerInfo = function(title) {\n\tif(!$tw.utils.hop(this.tiddlerInfo,title)) {\n\t\tthis.tiddlerInfo[title] = {\n\t\t\trevision: null,\n\t\t\tadaptorInfo: {},\n\t\t\tchangeCount: -1,\n\t\t\thasBeenLazyLoaded: false\n\t\t};\n\t}\n};\n\n/*\nChecks whether the wiki is dirty (ie the window shouldn't be closed)\n*/\nSyncer.prototype.isDirty = function() {\n\treturn (this.numTasksInQueue() > 0) || (this.numTasksInProgress() > 0);\n};\n\n/*\nUpdate the document body with the class \"tc-dirty\" if the wiki has unsaved/unsynced changes\n*/\nSyncer.prototype.updateDirtyStatus = function() {\n\tif($tw.browser) {\n\t\t$tw.utils.toggleClass(document.body,\"tc-dirty\",this.isDirty());\n\t}\n};\n\n/*\nSave an incoming tiddler in the store, and updates the associated tiddlerInfo\n*/\nSyncer.prototype.storeTiddler = function(tiddlerFields,hasBeenLazyLoaded) {\n\t// Save the tiddler\n\tvar tiddler = new $tw.Tiddler(this.wiki.getTiddler(tiddlerFields.title),tiddlerFields);\n\tthis.wiki.addTiddler(tiddler);\n\t// Save the tiddler revision and changeCount details\n\tthis.tiddlerInfo[tiddlerFields.title] = {\n\t\trevision: tiddlerFields.revision,\n\t\tadaptorInfo: this.syncadaptor.getTiddlerInfo(tiddler),\n\t\tchangeCount: this.wiki.getChangeCount(tiddlerFields.title),\n\t\thasBeenLazyLoaded: hasBeenLazyLoaded !== undefined ? hasBeenLazyLoaded : true\n\t};\n};\n\nSyncer.prototype.getStatus = function(callback) {\n\tvar self = this;\n\t// Check if the adaptor supports getStatus()\n\tif(this.syncadaptor && this.syncadaptor.getStatus) {\n\t\t// Mark us as not logged in\n\t\tthis.wiki.addTiddler({title: this.titleIsLoggedIn,text: \"no\"});\n\t\t// Get login status\n\t\tthis.syncadaptor.getStatus(function(err,isLoggedIn,username) {\n\t\t\tif(err) {\n\t\t\t\tself.logger.alert(err);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\t// Set the various status tiddlers\n\t\t\tself.wiki.addTiddler({title: self.titleIsLoggedIn,text: isLoggedIn ? \"yes\" : \"no\"});\n\t\t\tif(isLoggedIn) {\n\t\t\t\tself.wiki.addTiddler({title: self.titleUserName,text: username || \"\"});\n\t\t\t} else {\n\t\t\t\tself.wiki.deleteTiddler(self.titleUserName);\n\t\t\t}\n\t\t\t// Invoke the callback\n\t\t\tif(callback) {\n\t\t\t\tcallback(err,isLoggedIn,username);\n\t\t\t}\n\t\t});\n\t} else {\n\t\tcallback(null,true,\"UNAUTHENTICATED\");\n\t}\n};\n\n/*\nSynchronise from the server by reading the skinny tiddler list and queuing up loads for any tiddlers that we don't already have up to date\n*/\nSyncer.prototype.syncFromServer = function() {\n\tif(this.syncadaptor && this.syncadaptor.getSkinnyTiddlers) {\n\t\tthis.logger.log(\"Retrieving skinny tiddler list\");\n\t\tvar self = this;\n\t\tif(this.pollTimerId) {\n\t\t\tclearTimeout(this.pollTimerId);\n\t\t\tthis.pollTimerId = null;\n\t\t}\n\t\tthis.syncadaptor.getSkinnyTiddlers(function(err,tiddlers) {\n\t\t\t// Trigger the next sync\n\t\t\tself.pollTimerId = setTimeout(function() {\n\t\t\t\tself.pollTimerId = null;\n\t\t\t\tself.syncFromServer.call(self);\n\t\t\t},self.pollTimerInterval);\n\t\t\t// Check for errors\n\t\t\tif(err) {\n\t\t\t\tself.logger.alert($tw.language.getString(\"Error/RetrievingSkinny\") + \":\",err);\n\t\t\t\treturn;\n\t\t\t}\n\t\t\t// Process each incoming tiddler\n\t\t\tfor(var t=0; t<tiddlers.length; t++) {\n\t\t\t\t// Get the incoming tiddler fields, and the existing tiddler\n\t\t\t\tvar tiddlerFields = tiddlers[t],\n\t\t\t\t\tincomingRevision = tiddlerFields.revision + \"\",\n\t\t\t\t\ttiddler = self.wiki.getTiddler(tiddlerFields.title),\n\t\t\t\t\ttiddlerInfo = self.tiddlerInfo[tiddlerFields.title],\n\t\t\t\t\tcurrRevision = tiddlerInfo ? tiddlerInfo.revision : null;\n\t\t\t\t// Ignore the incoming tiddler if it's the same as the revision we've already got\n\t\t\t\tif(currRevision !== incomingRevision) {\n\t\t\t\t\t// Do a full load if we've already got a fat version of the tiddler\n\t\t\t\t\tif(tiddler && tiddler.fields.text !== undefined) {\n\t\t\t\t\t\t// Do a full load of this tiddler\n\t\t\t\t\t\tself.enqueueSyncTask({\n\t\t\t\t\t\t\ttype: \"load\",\n\t\t\t\t\t\t\ttitle: tiddlerFields.title\n\t\t\t\t\t\t});\n\t\t\t\t\t} else {\n\t\t\t\t\t\t// Load the skinny version of the tiddler\n\t\t\t\t\t\tself.storeTiddler(tiddlerFields,false);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n};\n\n/*\nSynchronise a set of changes to the server\n*/\nSyncer.prototype.syncToServer = function(changes) {\n\tvar self = this,\n\t\tnow = Date.now(),\n\t\tfilteredChanges = this.filterFn.call(this.wiki,function(callback) {\n\t\t\t$tw.utils.each(changes,function(change,title) {\n\t\t\t\tvar tiddler = self.wiki.getTiddler(title);\n\t\t\t\tcallback(tiddler,title);\n\t\t\t});\n\t\t});\n\t$tw.utils.each(changes,function(change,title,object) {\n\t\t// Process the change if it is a deletion of a tiddler we're already syncing, or is on the filtered change list\n\t\tif((change.deleted && $tw.utils.hop(self.tiddlerInfo,title)) || filteredChanges.indexOf(title) !== -1) {\n\t\t\t// Queue a task to sync this tiddler\n\t\t\tself.enqueueSyncTask({\n\t\t\t\ttype: change.deleted ? \"delete\" : \"save\",\n\t\t\t\ttitle: title\n\t\t\t});\n\t\t}\n\t});\n};\n\n/*\nLazily load a skinny tiddler if we can\n*/\nSyncer.prototype.handleLazyLoadEvent = function(title) {\n\t// Don't lazy load the same tiddler twice\n\tvar info = this.tiddlerInfo[title];\n\tif(!info || !info.hasBeenLazyLoaded) {\n\t\tthis.createTiddlerInfo(title);\n\t\tthis.tiddlerInfo[title].hasBeenLazyLoaded = true;\n\t\t// Queue up a sync task to load this tiddler\n\t\tthis.enqueueSyncTask({\n\t\t\ttype: \"load\",\n\t\t\ttitle: title\n\t\t});\t\t\n\t}\n};\n\n/*\nDispay a password prompt and allow the user to login\n*/\nSyncer.prototype.handleLoginEvent = function() {\n\tvar self = this;\n\tthis.getStatus(function(err,isLoggedIn,username) {\n\t\tif(!isLoggedIn) {\n\t\t\t$tw.passwordPrompt.createPrompt({\n\t\t\t\tserviceName: $tw.language.getString(\"LoginToTiddlySpace\"),\n\t\t\t\tcallback: function(data) {\n\t\t\t\t\tself.login(data.username,data.password,function(err,isLoggedIn) {\n\t\t\t\t\t\tself.syncFromServer();\n\t\t\t\t\t});\n\t\t\t\t\treturn true; // Get rid of the password prompt\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\t});\n};\n\n/*\nAttempt to login to TiddlyWeb.\n\tusername: username\n\tpassword: password\n\tcallback: invoked with arguments (err,isLoggedIn)\n*/\nSyncer.prototype.login = function(username,password,callback) {\n\tthis.logger.log(\"Attempting to login as\",username);\n\tvar self = this;\n\tif(this.syncadaptor.login) {\n\t\tthis.syncadaptor.login(username,password,function(err) {\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tself.getStatus(function(err,isLoggedIn,username) {\n\t\t\t\tif(callback) {\n\t\t\t\t\tcallback(null,isLoggedIn);\n\t\t\t\t}\n\t\t\t});\n\t\t});\n\t} else {\n\t\tcallback(null,true);\n\t}\n};\n\n/*\nAttempt to log out of TiddlyWeb\n*/\nSyncer.prototype.handleLogoutEvent = function() {\n\tthis.logger.log(\"Attempting to logout\");\n\tvar self = this;\n\tif(this.syncadaptor.logout) {\n\t\tthis.syncadaptor.logout(function(err) {\n\t\t\tif(err) {\n\t\t\t\tself.logger.alert(err);\n\t\t\t} else {\n\t\t\t\tself.getStatus();\n\t\t\t}\n\t\t});\n\t}\n};\n\n/*\nImmediately refresh from the server\n*/\nSyncer.prototype.handleRefreshEvent = function() {\n\tthis.syncFromServer();\n};\n\n/*\nQueue up a sync task. If there is already a pending task for the tiddler, just update the last modification time\n*/\nSyncer.prototype.enqueueSyncTask = function(task) {\n\tvar self = this,\n\t\tnow = Date.now();\n\t// Set the timestamps on this task\n\ttask.queueTime = now;\n\ttask.lastModificationTime = now;\n\t// Fill in some tiddlerInfo if the tiddler is one we haven't seen before\n\tthis.createTiddlerInfo(task.title);\n\t// Bail if this is a save and the tiddler is already at the changeCount that the server has\n\tif(task.type === \"save\" && this.wiki.getChangeCount(task.title) <= this.tiddlerInfo[task.title].changeCount) {\n\t\treturn;\n\t}\n\t// Check if this tiddler is already in the queue\n\tif($tw.utils.hop(this.taskQueue,task.title)) {\n\t\t// this.logger.log(\"Re-queueing up sync task with type:\",task.type,\"title:\",task.title);\n\t\tvar existingTask = this.taskQueue[task.title];\n\t\t// If so, just update the last modification time\n\t\texistingTask.lastModificationTime = task.lastModificationTime;\n\t\t// If the new task is a save then we upgrade the existing task to a save. Thus a pending load is turned into a save if the tiddler changes locally in the meantime. But a pending save is not modified to become a load\n\t\tif(task.type === \"save\" || task.type === \"delete\") {\n\t\t\texistingTask.type = task.type;\n\t\t}\n\t} else {\n\t\t// this.logger.log(\"Queuing up sync task with type:\",task.type,\"title:\",task.title);\n\t\t// If it is not in the queue, insert it\n\t\tthis.taskQueue[task.title] = task;\n\t\tthis.updateDirtyStatus();\n\t}\n\t// Process the queue\n\t$tw.utils.nextTick(function() {self.processTaskQueue.call(self);});\n};\n\n/*\nReturn the number of tasks in progress\n*/\nSyncer.prototype.numTasksInProgress = function() {\n\treturn $tw.utils.count(this.taskInProgress);\n};\n\n/*\nReturn the number of tasks in the queue\n*/\nSyncer.prototype.numTasksInQueue = function() {\n\treturn $tw.utils.count(this.taskQueue);\n};\n\n/*\nTrigger a timeout if one isn't already outstanding\n*/\nSyncer.prototype.triggerTimeout = function() {\n\tvar self = this;\n\tif(!this.taskTimerId) {\n\t\tthis.taskTimerId = setTimeout(function() {\n\t\t\tself.taskTimerId = null;\n\t\t\tself.processTaskQueue.call(self);\n\t\t},self.taskTimerInterval);\n\t}\n};\n\n/*\nProcess the task queue, performing the next task if appropriate\n*/\nSyncer.prototype.processTaskQueue = function() {\n\tvar self = this;\n\t// Only process a task if the sync adaptor is fully initialised and we're not already performing a task. If we are already performing a task then we'll dispatch the next one when it completes\n\tif((!this.syncadaptor.isReady || this.syncadaptor.isReady()) && this.numTasksInProgress() === 0) {\n\t\t// Choose the next task to perform\n\t\tvar task = this.chooseNextTask();\n\t\t// Perform the task if we had one\n\t\tif(task) {\n\t\t\t// Remove the task from the queue and add it to the in progress list\n\t\t\tdelete this.taskQueue[task.title];\n\t\t\tthis.taskInProgress[task.title] = task;\n\t\t\tthis.updateDirtyStatus();\n\t\t\t// Dispatch the task\n\t\t\tthis.dispatchTask(task,function(err) {\n\t\t\t\tif(err) {\n\t\t\t\t\tself.logger.alert(\"Sync error while processing '\" + task.title + \"':\\n\" + err);\n\t\t\t\t}\n\t\t\t\t// Mark that this task is no longer in progress\n\t\t\t\tdelete self.taskInProgress[task.title];\n\t\t\t\tself.updateDirtyStatus();\n\t\t\t\t// Process the next task\n\t\t\t\tself.processTaskQueue.call(self);\n\t\t\t});\n\t\t} else {\n\t\t\t// Make sure we've set a time if there wasn't a task to perform, but we've still got tasks in the queue\n\t\t\tif(this.numTasksInQueue() > 0) {\n\t\t\t\tthis.triggerTimeout();\n\t\t\t}\n\t\t}\n\t}\n};\n\n/*\nChoose the next applicable task\n*/\nSyncer.prototype.chooseNextTask = function() {\n\tvar self = this,\n\t\tcandidateTask = null,\n\t\tnow = Date.now();\n\t// Select the best candidate task\n\t$tw.utils.each(this.taskQueue,function(task,title) {\n\t\t// Exclude the task if there's one of the same name in progress\n\t\tif($tw.utils.hop(self.taskInProgress,title)) {\n\t\t\treturn;\n\t\t}\n\t\t// Exclude the task if it is a save and the tiddler has been modified recently, but not hit the fallback time\n\t\tif(task.type === \"save\" && (now - task.lastModificationTime) < self.throttleInterval &&\n\t\t\t(now - task.queueTime) < self.fallbackInterval) {\n\t\t\treturn;\n\t\t}\n\t\t// Exclude the task if it is newer than the current best candidate\n\t\tif(candidateTask && candidateTask.queueTime < task.queueTime) {\n\t\t\treturn;\n\t\t}\n\t\t// Now this is our best candidate\n\t\tcandidateTask = task;\n\t});\n\treturn candidateTask;\n};\n\n/*\nDispatch a task and invoke the callback\n*/\nSyncer.prototype.dispatchTask = function(task,callback) {\n\tvar self = this;\n\tif(task.type === \"save\") {\n\t\tvar changeCount = this.wiki.getChangeCount(task.title),\n\t\t\ttiddler = this.wiki.getTiddler(task.title);\n\t\tthis.logger.log(\"Dispatching 'save' task:\",task.title);\n\t\tif(tiddler) {\n\t\t\tthis.syncadaptor.saveTiddler(tiddler,function(err,adaptorInfo,revision) {\n\t\t\t\tif(err) {\n\t\t\t\t\treturn callback(err);\n\t\t\t\t}\n\t\t\t\t// Adjust the info stored about this tiddler\n\t\t\t\tself.tiddlerInfo[task.title] = {\n\t\t\t\t\tchangeCount: changeCount,\n\t\t\t\t\tadaptorInfo: adaptorInfo,\n\t\t\t\t\trevision: revision\n\t\t\t\t};\n\t\t\t\t// Invoke the callback\n\t\t\t\tcallback(null);\n\t\t\t},{\n\t\t\t\ttiddlerInfo: self.tiddlerInfo[task.title]\n\t\t\t});\n\t\t} else {\n\t\t\tthis.logger.log(\" Not Dispatching 'save' task:\",task.title,\"tiddler does not exist\");\n\t\t\treturn callback(null);\n\t\t}\n\t} else if(task.type === \"load\") {\n\t\t// Load the tiddler\n\t\tthis.logger.log(\"Dispatching 'load' task:\",task.title);\n\t\tthis.syncadaptor.loadTiddler(task.title,function(err,tiddlerFields) {\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\t// Store the tiddler\n\t\t\tif(tiddlerFields) {\n\t\t\t\tself.storeTiddler(tiddlerFields,true);\n\t\t\t}\n\t\t\t// Invoke the callback\n\t\t\tcallback(null);\n\t\t});\n\t} else if(task.type === \"delete\") {\n\t\t// Delete the tiddler\n\t\tthis.logger.log(\"Dispatching 'delete' task:\",task.title);\n\t\tthis.syncadaptor.deleteTiddler(task.title,function(err) {\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tdelete self.tiddlerInfo[task.title];\n\t\t\t// Invoke the callback\n\t\t\tcallback(null);\n\t\t},{\n\t\t\ttiddlerInfo: self.tiddlerInfo[task.title]\n\t\t});\n\t}\n};\n\nexports.Syncer = Syncer;\n\n})();\n",
"title": "$:/core/modules/syncer.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/tiddler.js": {
"text": "/*\\\ntitle: $:/core/modules/tiddler.js\ntype: application/javascript\nmodule-type: tiddlermethod\n\nExtension methods for the $tw.Tiddler object (constructor and methods required at boot time are in boot/boot.js)\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.hasTag = function(tag) {\n\treturn this.fields.tags && this.fields.tags.indexOf(tag) !== -1;\n};\n\nexports.isPlugin = function() {\n\treturn this.fields.type === \"application/json\" && this.hasField(\"plugin-type\");\n};\n\nexports.isDraft = function() {\n\treturn this.hasField(\"draft.of\");\n};\n\nexports.getFieldString = function(field) {\n\tvar value = this.fields[field];\n\t// Check for a missing field\n\tif(value === undefined || value === null) {\n\t\treturn \"\";\n\t}\n\t// Parse the field with the associated module (if any)\n\tvar fieldModule = $tw.Tiddler.fieldModules[field];\n\tif(fieldModule && fieldModule.stringify) {\n\t\treturn fieldModule.stringify.call(this,value);\n\t} else {\n\t\treturn value.toString();\n\t}\n};\n\n/*\nGet all the fields as a hashmap of strings. Options:\n\texclude: an array of field names to exclude\n*/\nexports.getFieldStrings = function(options) {\n\toptions = options || {};\n\tvar exclude = options.exclude || [];\n\tvar fields = {};\n\tfor(var field in this.fields) {\n\t\tif($tw.utils.hop(this.fields,field)) {\n\t\t\tif(exclude.indexOf(field) === -1) {\n\t\t\t\tfields[field] = this.getFieldString(field);\n\t\t\t}\n\t\t}\n\t}\n\treturn fields;\n};\n\n/*\nGet all the fields as a name:value block. Options:\n\texclude: an array of field names to exclude\n*/\nexports.getFieldStringBlock = function(options) {\n\toptions = options || {};\n\tvar exclude = options.exclude || [];\n\tvar fields = [];\n\tfor(var field in this.fields) {\n\t\tif($tw.utils.hop(this.fields,field)) {\n\t\t\tif(exclude.indexOf(field) === -1) {\n\t\t\t\tfields.push(field + \": \" + this.getFieldString(field));\n\t\t\t}\n\t\t}\n\t}\n\treturn fields.join(\"\\n\");\n};\n\n/*\nCompare two tiddlers for equality\ntiddler: the tiddler to compare\nexcludeFields: array of field names to exclude from the comparison\n*/\nexports.isEqual = function(tiddler,excludeFields) {\n\tif(!(tiddler instanceof $tw.Tiddler)) {\n\t\treturn false;\n\t}\n\texcludeFields = excludeFields || [];\n\tvar self = this,\n\t\tdifferences = []; // Fields that have differences\n\t// Add to the differences array\n\tfunction addDifference(fieldName) {\n\t\t// Check for this field being excluded\n\t\tif(excludeFields.indexOf(fieldName) === -1) {\n\t\t\t// Save the field as a difference\n\t\t\t$tw.utils.pushTop(differences,fieldName);\n\t\t}\n\t}\n\t// Returns true if the two values of this field are equal\n\tfunction isFieldValueEqual(fieldName) {\n\t\tvar valueA = self.fields[fieldName],\n\t\t\tvalueB = tiddler.fields[fieldName];\n\t\t// Check for identical string values\n\t\tif(typeof(valueA) === \"string\" && typeof(valueB) === \"string\" && valueA === valueB) {\n\t\t\treturn true;\n\t\t}\n\t\t// Check for identical array values\n\t\tif($tw.utils.isArray(valueA) && $tw.utils.isArray(valueB) && $tw.utils.isArrayEqual(valueA,valueB)) {\n\t\t\treturn true;\n\t\t}\n\t\t// Otherwise the fields must be different\n\t\treturn false;\n\t}\n\t// Compare our fields\n\tfor(var fieldName in this.fields) {\n\t\tif(!isFieldValueEqual(fieldName)) {\n\t\t\taddDifference(fieldName);\n\t\t}\n\t}\n\t// There's a difference for every field in the other tiddler that we don't have\n\tfor(fieldName in tiddler.fields) {\n\t\tif(!(fieldName in this.fields)) {\n\t\t\taddDifference(fieldName);\n\t\t}\n\t}\n\t// Return whether there were any differences\n\treturn differences.length === 0;\n};\n\nexports.getFieldDay = function(field) {\n\tif(this.cache && this.cache.day && $tw.utils.hop(this.cache.day,field) ) {\n\t\treturn this.cache.day[field];\n\t}\n\tvar day = \"\";\n\tif(this.fields[field]) {\n\t\tday = (new Date($tw.utils.parseDate(this.fields[field]))).setHours(0,0,0,0);\n\t}\n\tthis.cache.day = this.cache.day || {};\n\tthis.cache.day[field] = day;\n\treturn day;\n};\n\n})();\n",
"title": "$:/core/modules/tiddler.js",
"type": "application/javascript",
"module-type": "tiddlermethod"
},
"$:/core/modules/upgraders/plugins.js": {
"text": "/*\\\ntitle: $:/core/modules/upgraders/plugins.js\ntype: application/javascript\nmodule-type: upgrader\n\nUpgrader module that checks that plugins are newer than any already installed version\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar UPGRADE_LIBRARY_TITLE = \"$:/UpgradeLibrary\";\n\nvar BLOCKED_PLUGINS = {\n\t\"$:/themes/tiddlywiki/stickytitles\": {\n\t\tversions: [\"*\"]\n\t},\n\t\"$:/plugins/tiddlywiki/fullscreen\": {\n\t\tversions: [\"*\"]\n\t}\n};\n\nexports.upgrade = function(wiki,titles,tiddlers) {\n\tvar self = this,\n\t\tmessages = {},\n\t\tupgradeLibrary,\n\t\tgetLibraryTiddler = function(title) {\n\t\t\tif(!upgradeLibrary) {\n\t\t\t\tupgradeLibrary = wiki.getTiddlerData(UPGRADE_LIBRARY_TITLE,{});\n\t\t\t\tupgradeLibrary.tiddlers = upgradeLibrary.tiddlers || {};\n\t\t\t}\n\t\t\treturn upgradeLibrary.tiddlers[title];\n\t\t};\n\n\t// Go through all the incoming tiddlers\n\t$tw.utils.each(titles,function(title) {\n\t\tvar incomingTiddler = tiddlers[title];\n\t\t// Check if we're dealing with a plugin\n\t\tif(incomingTiddler && incomingTiddler[\"plugin-type\"] && incomingTiddler.version) {\n\t\t\t// Upgrade the incoming plugin if it is in the upgrade library\n\t\t\tvar libraryTiddler = getLibraryTiddler(title);\n\t\t\tif(libraryTiddler && libraryTiddler[\"plugin-type\"] && libraryTiddler.version) {\n\t\t\t\ttiddlers[title] = libraryTiddler;\n\t\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/Plugins/Upgraded\",{variables: {incoming: incomingTiddler.version, upgraded: libraryTiddler.version}});\n\t\t\t\treturn;\n\t\t\t}\n\t\t\t// Suppress the incoming plugin if it is older than the currently installed one\n\t\t\tvar existingTiddler = wiki.getTiddler(title);\n\t\t\tif(existingTiddler && existingTiddler.hasField(\"plugin-type\") && existingTiddler.hasField(\"version\")) {\n\t\t\t\t// Reject the incoming plugin by blanking all its fields\n\t\t\t\tif($tw.utils.checkVersions(existingTiddler.fields.version,incomingTiddler.version)) {\n\t\t\t\t\ttiddlers[title] = Object.create(null);\n\t\t\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/Plugins/Suppressed/Version\",{variables: {incoming: incomingTiddler.version, existing: existingTiddler.fields.version}});\n\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif(incomingTiddler && incomingTiddler[\"plugin-type\"]) {\n\t\t\t// Check whether the plugin is on the blocked list\n\t\t\tvar blockInfo = BLOCKED_PLUGINS[title];\n\t\t\tif(blockInfo) {\n\t\t\t\tif(blockInfo.versions.indexOf(\"*\") !== -1 || (incomingTiddler.version && blockInfo.versions.indexOf(incomingTiddler.version) !== -1)) {\n\t\t\t\t\ttiddlers[title] = Object.create(null);\n\t\t\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/Plugins/Suppressed/Incompatible\");\n\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\treturn messages;\n};\n\n})();\n",
"title": "$:/core/modules/upgraders/plugins.js",
"type": "application/javascript",
"module-type": "upgrader"
},
"$:/core/modules/upgraders/system.js": {
"text": "/*\\\ntitle: $:/core/modules/upgraders/system.js\ntype: application/javascript\nmodule-type: upgrader\n\nUpgrader module that suppresses certain system tiddlers that shouldn't be imported\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar DONT_IMPORT_LIST = [\"$:/StoryList\",\"$:/HistoryList\"],\n\tDONT_IMPORT_PREFIX_LIST = [\"$:/temp/\",\"$:/state/\"];\n\nexports.upgrade = function(wiki,titles,tiddlers) {\n\tvar self = this,\n\t\tmessages = {};\n\t// Check for tiddlers on our list\n\t$tw.utils.each(titles,function(title) {\n\t\tif(DONT_IMPORT_LIST.indexOf(title) !== -1) {\n\t\t\ttiddlers[title] = Object.create(null);\n\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/System/Suppressed\");\n\t\t} else {\n\t\t\tfor(var t=0; t<DONT_IMPORT_PREFIX_LIST.length; t++) {\n\t\t\t\tvar prefix = DONT_IMPORT_PREFIX_LIST[t];\n\t\t\t\tif(title.substr(0,prefix.length) === prefix) {\n\t\t\t\t\ttiddlers[title] = Object.create(null);\n\t\t\t\t\tmessages[title] = $tw.language.getString(\"Import/Upgrader/State/Suppressed\");\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\treturn messages;\n};\n\n})();\n",
"title": "$:/core/modules/upgraders/system.js",
"type": "application/javascript",
"module-type": "upgrader"
},
"$:/core/modules/upgraders/themetweaks.js": {
"text": "/*\\\ntitle: $:/core/modules/upgraders/themetweaks.js\ntype: application/javascript\nmodule-type: upgrader\n\nUpgrader module that handles the change in theme tweak storage introduced in 5.0.14-beta.\n\nPreviously, theme tweaks were stored in two data tiddlers:\n\n* $:/themes/tiddlywiki/vanilla/metrics\n* $:/themes/tiddlywiki/vanilla/settings\n\nNow, each tweak is stored in its own separate tiddler.\n\nThis upgrader copies any values from the old format to the new. The old data tiddlers are not deleted in case they have been used to store additional indexes.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar MAPPINGS = {\n\t\"$:/themes/tiddlywiki/vanilla/metrics\": {\n\t\t\"fontsize\": \"$:/themes/tiddlywiki/vanilla/metrics/fontsize\",\n\t\t\"lineheight\": \"$:/themes/tiddlywiki/vanilla/metrics/lineheight\",\n\t\t\"storyleft\": \"$:/themes/tiddlywiki/vanilla/metrics/storyleft\",\n\t\t\"storytop\": \"$:/themes/tiddlywiki/vanilla/metrics/storytop\",\n\t\t\"storyright\": \"$:/themes/tiddlywiki/vanilla/metrics/storyright\",\n\t\t\"storywidth\": \"$:/themes/tiddlywiki/vanilla/metrics/storywidth\",\n\t\t\"tiddlerwidth\": \"$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth\"\n\t},\n\t\"$:/themes/tiddlywiki/vanilla/settings\": {\n\t\t\"fontfamily\": \"$:/themes/tiddlywiki/vanilla/settings/fontfamily\"\n\t}\n};\n\nexports.upgrade = function(wiki,titles,tiddlers) {\n\tvar self = this,\n\t\tmessages = {};\n\t// Check for tiddlers on our list\n\t$tw.utils.each(titles,function(title) {\n\t\tvar mapping = MAPPINGS[title];\n\t\tif(mapping) {\n\t\t\tvar tiddler = new $tw.Tiddler(tiddlers[title]),\n\t\t\t\ttiddlerData = wiki.getTiddlerDataCached(tiddler,{});\n\t\t\tfor(var index in mapping) {\n\t\t\t\tvar mappedTitle = mapping[index];\n\t\t\t\tif(!tiddlers[mappedTitle] || tiddlers[mappedTitle].title !== mappedTitle) {\n\t\t\t\t\ttiddlers[mappedTitle] = {\n\t\t\t\t\t\ttitle: mappedTitle,\n\t\t\t\t\t\ttext: tiddlerData[index]\n\t\t\t\t\t};\n\t\t\t\t\tmessages[mappedTitle] = $tw.language.getString(\"Import/Upgrader/ThemeTweaks/Created\",{variables: {\n\t\t\t\t\t\tfrom: title + \"##\" + index\n\t\t\t\t\t}});\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t});\n\treturn messages;\n};\n\n})();\n",
"title": "$:/core/modules/upgraders/themetweaks.js",
"type": "application/javascript",
"module-type": "upgrader"
},
"$:/core/modules/utils/crypto.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/crypto.js\ntype: application/javascript\nmodule-type: utils\n\nUtility functions related to crypto.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nLook for an encrypted store area in the text of a TiddlyWiki file\n*/\nexports.extractEncryptedStoreArea = function(text) {\n\tvar encryptedStoreAreaStartMarker = \"<pre id=\\\"encryptedStoreArea\\\" type=\\\"text/plain\\\" style=\\\"display:none;\\\">\",\n\t\tencryptedStoreAreaStart = text.indexOf(encryptedStoreAreaStartMarker);\n\tif(encryptedStoreAreaStart !== -1) {\n\t\tvar encryptedStoreAreaEnd = text.indexOf(\"</pre>\",encryptedStoreAreaStart);\n\t\tif(encryptedStoreAreaEnd !== -1) {\n\t\t\treturn $tw.utils.htmlDecode(text.substring(encryptedStoreAreaStart + encryptedStoreAreaStartMarker.length,encryptedStoreAreaEnd-1));\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nAttempt to extract the tiddlers from an encrypted store area using the current password. If the password is not provided then the password in the password store will be used\n*/\nexports.decryptStoreArea = function(encryptedStoreArea,password) {\n\tvar decryptedText = $tw.crypto.decrypt(encryptedStoreArea,password);\n\tif(decryptedText) {\n\t\tvar json = JSON.parse(decryptedText),\n\t\t\ttiddlers = [];\n\t\tfor(var title in json) {\n\t\t\tif(title !== \"$:/isEncrypted\") {\n\t\t\t\ttiddlers.push(json[title]);\n\t\t\t}\n\t\t}\n\t\treturn tiddlers;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n\n/*\nAttempt to extract the tiddlers from an encrypted store area using the current password. If that fails, the user is prompted for a password.\nencryptedStoreArea: text of the TiddlyWiki encrypted store area\ncallback: function(tiddlers) called with the array of decrypted tiddlers\n\nThe following configuration settings are supported:\n\n$tw.config.usePasswordVault: causes any password entered by the user to also be put into the system password vault\n*/\nexports.decryptStoreAreaInteractive = function(encryptedStoreArea,callback,options) {\n\t// Try to decrypt with the current password\n\tvar tiddlers = $tw.utils.decryptStoreArea(encryptedStoreArea);\n\tif(tiddlers) {\n\t\tcallback(tiddlers);\n\t} else {\n\t\t// Prompt for a new password and keep trying\n\t\t$tw.passwordPrompt.createPrompt({\n\t\t\tserviceName: \"Enter a password to decrypt the imported TiddlyWiki\",\n\t\t\tnoUserName: true,\n\t\t\tcanCancel: true,\n\t\t\tsubmitText: \"Decrypt\",\n\t\t\tcallback: function(data) {\n\t\t\t\t// Exit if the user cancelled\n\t\t\t\tif(!data) {\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t\t// Attempt to decrypt the tiddlers\n\t\t\t\tvar tiddlers = $tw.utils.decryptStoreArea(encryptedStoreArea,data.password);\n\t\t\t\tif(tiddlers) {\n\t\t\t\t\tif($tw.config.usePasswordVault) {\n\t\t\t\t\t\t$tw.crypto.setPassword(data.password);\n\t\t\t\t\t}\n\t\t\t\t\tcallback(tiddlers);\n\t\t\t\t\t// Exit and remove the password prompt\n\t\t\t\t\treturn true;\n\t\t\t\t} else {\n\t\t\t\t\t// We didn't decrypt everything, so continue to prompt for password\n\t\t\t\t\treturn false;\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/utils/crypto.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/animations/slide.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/animations/slide.js\ntype: application/javascript\nmodule-type: animation\n\nA simple slide animation that varies the height of the element\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nfunction slideOpen(domNode,options) {\n\toptions = options || {};\n\tvar duration = options.duration || $tw.utils.getAnimationDuration();\n\t// Get the current height of the domNode\n\tvar computedStyle = window.getComputedStyle(domNode),\n\t\tcurrMarginBottom = parseInt(computedStyle.marginBottom,10),\n\t\tcurrMarginTop = parseInt(computedStyle.marginTop,10),\n\t\tcurrPaddingBottom = parseInt(computedStyle.paddingBottom,10),\n\t\tcurrPaddingTop = parseInt(computedStyle.paddingTop,10),\n\t\tcurrHeight = domNode.offsetHeight;\n\t// Reset the margin once the transition is over\n\tsetTimeout(function() {\n\t\t$tw.utils.setStyle(domNode,[\n\t\t\t{transition: \"none\"},\n\t\t\t{marginBottom: \"\"},\n\t\t\t{marginTop: \"\"},\n\t\t\t{paddingBottom: \"\"},\n\t\t\t{paddingTop: \"\"},\n\t\t\t{height: \"auto\"},\n\t\t\t{opacity: \"\"}\n\t\t]);\n\t\tif(options.callback) {\n\t\t\toptions.callback();\n\t\t}\n\t},duration);\n\t// Set up the initial position of the element\n\t$tw.utils.setStyle(domNode,[\n\t\t{transition: \"none\"},\n\t\t{marginTop: \"0px\"},\n\t\t{marginBottom: \"0px\"},\n\t\t{paddingTop: \"0px\"},\n\t\t{paddingBottom: \"0px\"},\n\t\t{height: \"0px\"},\n\t\t{opacity: \"0\"}\n\t]);\n\t$tw.utils.forceLayout(domNode);\n\t// Transition to the final position\n\t$tw.utils.setStyle(domNode,[\n\t\t{transition: \"margin-top \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"margin-bottom \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"padding-top \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"padding-bottom \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"height \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms ease-in-out\"},\n\t\t{marginBottom: currMarginBottom + \"px\"},\n\t\t{marginTop: currMarginTop + \"px\"},\n\t\t{paddingBottom: currPaddingBottom + \"px\"},\n\t\t{paddingTop: currPaddingTop + \"px\"},\n\t\t{height: currHeight + \"px\"},\n\t\t{opacity: \"1\"}\n\t]);\n}\n\nfunction slideClosed(domNode,options) {\n\toptions = options || {};\n\tvar duration = options.duration || $tw.utils.getAnimationDuration(),\n\t\tcurrHeight = domNode.offsetHeight;\n\t// Clear the properties we've set when the animation is over\n\tsetTimeout(function() {\n\t\t$tw.utils.setStyle(domNode,[\n\t\t\t{transition: \"none\"},\n\t\t\t{marginBottom: \"\"},\n\t\t\t{marginTop: \"\"},\n\t\t\t{paddingBottom: \"\"},\n\t\t\t{paddingTop: \"\"},\n\t\t\t{height: \"auto\"},\n\t\t\t{opacity: \"\"}\n\t\t]);\n\t\tif(options.callback) {\n\t\t\toptions.callback();\n\t\t}\n\t},duration);\n\t// Set up the initial position of the element\n\t$tw.utils.setStyle(domNode,[\n\t\t{height: currHeight + \"px\"},\n\t\t{opacity: \"1\"}\n\t]);\n\t$tw.utils.forceLayout(domNode);\n\t// Transition to the final position\n\t$tw.utils.setStyle(domNode,[\n\t\t{transition: \"margin-top \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"margin-bottom \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"padding-top \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"padding-bottom \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"height \" + duration + \"ms ease-in-out, \" +\n\t\t\t\t\t\"opacity \" + duration + \"ms ease-in-out\"},\n\t\t{marginTop: \"0px\"},\n\t\t{marginBottom: \"0px\"},\n\t\t{paddingTop: \"0px\"},\n\t\t{paddingBottom: \"0px\"},\n\t\t{height: \"0px\"},\n\t\t{opacity: \"0\"}\n\t]);\n}\n\nexports.slide = {\n\topen: slideOpen,\n\tclose: slideClosed\n};\n\n})();\n",
"title": "$:/core/modules/utils/dom/animations/slide.js",
"type": "application/javascript",
"module-type": "animation"
},
"$:/core/modules/utils/dom/animator.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/animator.js\ntype: application/javascript\nmodule-type: utils\n\nOrchestrates animations and transitions\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nfunction Animator() {\n\t// Get the registered animation modules\n\tthis.animations = {};\n\t$tw.modules.applyMethods(\"animation\",this.animations);\n}\n\nAnimator.prototype.perform = function(type,domNode,options) {\n\toptions = options || {};\n\t// Find an animation that can handle this type\n\tvar chosenAnimation;\n\t$tw.utils.each(this.animations,function(animation,name) {\n\t\tif($tw.utils.hop(animation,type)) {\n\t\t\tchosenAnimation = animation[type];\n\t\t}\n\t});\n\tif(!chosenAnimation) {\n\t\tchosenAnimation = function(domNode,options) {\n\t\t\tif(options.callback) {\n\t\t\t\toptions.callback();\n\t\t\t}\n\t\t};\n\t}\n\t// Call the animation\n\tchosenAnimation(domNode,options);\n};\n\nexports.Animator = Animator;\n\n})();\n",
"title": "$:/core/modules/utils/dom/animator.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/browser.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/browser.js\ntype: application/javascript\nmodule-type: utils\n\nBrowser feature detection\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nSet style properties of an element\n\telement: dom node\n\tstyles: ordered array of {name: value} pairs\n*/\nexports.setStyle = function(element,styles) {\n\tif(element.nodeType === 1) { // Element.ELEMENT_NODE\n\t\tfor(var t=0; t<styles.length; t++) {\n\t\t\tfor(var styleName in styles[t]) {\n\t\t\t\telement.style[$tw.utils.convertStyleNameToPropertyName(styleName)] = styles[t][styleName];\n\t\t\t}\n\t\t}\n\t}\n};\n\n/*\nConverts a standard CSS property name into the local browser-specific equivalent. For example:\n\t\"background-color\" --> \"backgroundColor\"\n\t\"transition\" --> \"webkitTransition\"\n*/\n\nvar styleNameCache = {}; // We'll cache the style name conversions\n\nexports.convertStyleNameToPropertyName = function(styleName) {\n\t// Return from the cache if we can\n\tif(styleNameCache[styleName]) {\n\t\treturn styleNameCache[styleName];\n\t}\n\t// Convert it by first removing any hyphens\n\tvar propertyName = $tw.utils.unHyphenateCss(styleName);\n\t// Then check if it needs a prefix\n\tif($tw.browser && document.body.style[propertyName] === undefined) {\n\t\tvar prefixes = [\"O\",\"MS\",\"Moz\",\"webkit\"];\n\t\tfor(var t=0; t<prefixes.length; t++) {\n\t\t\tvar prefixedName = prefixes[t] + propertyName.substr(0,1).toUpperCase() + propertyName.substr(1);\n\t\t\tif(document.body.style[prefixedName] !== undefined) {\n\t\t\t\tpropertyName = prefixedName;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\t// Put it in the cache too\n\tstyleNameCache[styleName] = propertyName;\n\treturn propertyName;\n};\n\n/*\nConverts a JS format CSS property name back into the dashed form used in CSS declarations. For example:\n\t\"backgroundColor\" --> \"background-color\"\n\t\"webkitTransform\" --> \"-webkit-transform\"\n*/\nexports.convertPropertyNameToStyleName = function(propertyName) {\n\t// Rehyphenate the name\n\tvar styleName = $tw.utils.hyphenateCss(propertyName);\n\t// If there's a webkit prefix, add a dash (other browsers have uppercase prefixes, and so get the dash automatically)\n\tif(styleName.indexOf(\"webkit\") === 0) {\n\t\tstyleName = \"-\" + styleName;\n\t} else if(styleName.indexOf(\"-m-s\") === 0) {\n\t\tstyleName = \"-ms\" + styleName.substr(4);\n\t}\n\treturn styleName;\n};\n\n/*\nRound trip a stylename to a property name and back again. For example:\n\t\"transform\" --> \"webkitTransform\" --> \"-webkit-transform\"\n*/\nexports.roundTripPropertyName = function(propertyName) {\n\treturn $tw.utils.convertPropertyNameToStyleName($tw.utils.convertStyleNameToPropertyName(propertyName));\n};\n\n/*\nConverts a standard event name into the local browser specific equivalent. For example:\n\t\"animationEnd\" --> \"webkitAnimationEnd\"\n*/\n\nvar eventNameCache = {}; // We'll cache the conversions\n\nvar eventNameMappings = {\n\t\"transitionEnd\": {\n\t\tcorrespondingCssProperty: \"transition\",\n\t\tmappings: {\n\t\t\ttransition: \"transitionend\",\n\t\t\tOTransition: \"oTransitionEnd\",\n\t\t\tMSTransition: \"msTransitionEnd\",\n\t\t\tMozTransition: \"transitionend\",\n\t\t\twebkitTransition: \"webkitTransitionEnd\"\n\t\t}\n\t},\n\t\"animationEnd\": {\n\t\tcorrespondingCssProperty: \"animation\",\n\t\tmappings: {\n\t\t\tanimation: \"animationend\",\n\t\t\tOAnimation: \"oAnimationEnd\",\n\t\t\tMSAnimation: \"msAnimationEnd\",\n\t\t\tMozAnimation: \"animationend\",\n\t\t\twebkitAnimation: \"webkitAnimationEnd\"\n\t\t}\n\t}\n};\n\nexports.convertEventName = function(eventName) {\n\tif(eventNameCache[eventName]) {\n\t\treturn eventNameCache[eventName];\n\t}\n\tvar newEventName = eventName,\n\t\tmappings = eventNameMappings[eventName];\n\tif(mappings) {\n\t\tvar convertedProperty = $tw.utils.convertStyleNameToPropertyName(mappings.correspondingCssProperty);\n\t\tif(mappings.mappings[convertedProperty]) {\n\t\t\tnewEventName = mappings.mappings[convertedProperty];\n\t\t}\n\t}\n\t// Put it in the cache too\n\teventNameCache[eventName] = newEventName;\n\treturn newEventName;\n};\n\n/*\nReturn the names of the fullscreen APIs\n*/\nexports.getFullScreenApis = function() {\n\tvar d = document,\n\t\tdb = d.body,\n\t\tresult = {\n\t\t\"_requestFullscreen\": db.webkitRequestFullscreen !== undefined ? \"webkitRequestFullscreen\" :\n\t\t\t\t\t\t\tdb.mozRequestFullScreen !== undefined ? \"mozRequestFullScreen\" :\n\t\t\t\t\t\t\tdb.msRequestFullscreen !== undefined ? \"msRequestFullscreen\" :\n\t\t\t\t\t\t\tdb.requestFullscreen !== undefined ? \"requestFullscreen\" : \"\",\n\t\t\"_exitFullscreen\": d.webkitExitFullscreen !== undefined ? \"webkitExitFullscreen\" :\n\t\t\t\t\t\t\td.mozCancelFullScreen !== undefined ? \"mozCancelFullScreen\" :\n\t\t\t\t\t\t\td.msExitFullscreen !== undefined ? \"msExitFullscreen\" :\n\t\t\t\t\t\t\td.exitFullscreen !== undefined ? \"exitFullscreen\" : \"\",\n\t\t\"_fullscreenElement\": d.webkitFullscreenElement !== undefined ? \"webkitFullscreenElement\" :\n\t\t\t\t\t\t\td.mozFullScreenElement !== undefined ? \"mozFullScreenElement\" :\n\t\t\t\t\t\t\td.msFullscreenElement !== undefined ? \"msFullscreenElement\" :\n\t\t\t\t\t\t\td.fullscreenElement !== undefined ? \"fullscreenElement\" : \"\",\n\t\t\"_fullscreenChange\": d.webkitFullscreenElement !== undefined ? \"webkitfullscreenchange\" :\n\t\t\t\t\t\t\td.mozFullScreenElement !== undefined ? \"mozfullscreenchange\" :\n\t\t\t\t\t\t\td.msFullscreenElement !== undefined ? \"MSFullscreenChange\" :\n\t\t\t\t\t\t\td.fullscreenElement !== undefined ? \"fullscreenchange\" : \"\"\n\t};\n\tif(!result._requestFullscreen || !result._exitFullscreen || !result._fullscreenElement || !result._fullscreenChange) {\n\t\treturn null;\n\t} else {\n\t\treturn result;\n\t}\n};\n\n})();\n",
"title": "$:/core/modules/utils/dom/browser.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/csscolorparser.js": {
"text": "// (c) Dean McNamee <dean@gmail.com>, 2012.\n//\n// https://github.com/deanm/css-color-parser-js\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to\n// deal in the Software without restriction, including without limitation the\n// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n// sell copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n//\n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n//\n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n// IN THE SOFTWARE.\n\n// http://www.w3.org/TR/css3-color/\nvar kCSSColorTable = {\n \"transparent\": [0,0,0,0], \"aliceblue\": [240,248,255,1],\n \"antiquewhite\": [250,235,215,1], \"aqua\": [0,255,255,1],\n \"aquamarine\": [127,255,212,1], \"azure\": [240,255,255,1],\n \"beige\": [245,245,220,1], \"bisque\": [255,228,196,1],\n \"black\": [0,0,0,1], \"blanchedalmond\": [255,235,205,1],\n \"blue\": [0,0,255,1], \"blueviolet\": [138,43,226,1],\n \"brown\": [165,42,42,1], \"burlywood\": [222,184,135,1],\n \"cadetblue\": [95,158,160,1], \"chartreuse\": [127,255,0,1],\n \"chocolate\": [210,105,30,1], \"coral\": [255,127,80,1],\n \"cornflowerblue\": [100,149,237,1], \"cornsilk\": [255,248,220,1],\n \"crimson\": [220,20,60,1], \"cyan\": [0,255,255,1],\n \"darkblue\": [0,0,139,1], \"darkcyan\": [0,139,139,1],\n \"darkgoldenrod\": [184,134,11,1], \"darkgray\": [169,169,169,1],\n \"darkgreen\": [0,100,0,1], \"darkgrey\": [169,169,169,1],\n \"darkkhaki\": [189,183,107,1], \"darkmagenta\": [139,0,139,1],\n \"darkolivegreen\": [85,107,47,1], \"darkorange\": [255,140,0,1],\n \"darkorchid\": [153,50,204,1], \"darkred\": [139,0,0,1],\n \"darksalmon\": [233,150,122,1], \"darkseagreen\": [143,188,143,1],\n \"darkslateblue\": [72,61,139,1], \"darkslategray\": [47,79,79,1],\n \"darkslategrey\": [47,79,79,1], \"darkturquoise\": [0,206,209,1],\n \"darkviolet\": [148,0,211,1], \"deeppink\": [255,20,147,1],\n \"deepskyblue\": [0,191,255,1], \"dimgray\": [105,105,105,1],\n \"dimgrey\": [105,105,105,1], \"dodgerblue\": [30,144,255,1],\n \"firebrick\": [178,34,34,1], \"floralwhite\": [255,250,240,1],\n \"forestgreen\": [34,139,34,1], \"fuchsia\": [255,0,255,1],\n \"gainsboro\": [220,220,220,1], \"ghostwhite\": [248,248,255,1],\n \"gold\": [255,215,0,1], \"goldenrod\": [218,165,32,1],\n \"gray\": [128,128,128,1], \"green\": [0,128,0,1],\n \"greenyellow\": [173,255,47,1], \"grey\": [128,128,128,1],\n \"honeydew\": [240,255,240,1], \"hotpink\": [255,105,180,1],\n \"indianred\": [205,92,92,1], \"indigo\": [75,0,130,1],\n \"ivory\": [255,255,240,1], \"khaki\": [240,230,140,1],\n \"lavender\": [230,230,250,1], \"lavenderblush\": [255,240,245,1],\n \"lawngreen\": [124,252,0,1], \"lemonchiffon\": [255,250,205,1],\n \"lightblue\": [173,216,230,1], \"lightcoral\": [240,128,128,1],\n \"lightcyan\": [224,255,255,1], \"lightgoldenrodyellow\": [250,250,210,1],\n \"lightgray\": [211,211,211,1], \"lightgreen\": [144,238,144,1],\n \"lightgrey\": [211,211,211,1], \"lightpink\": [255,182,193,1],\n \"lightsalmon\": [255,160,122,1], \"lightseagreen\": [32,178,170,1],\n \"lightskyblue\": [135,206,250,1], \"lightslategray\": [119,136,153,1],\n \"lightslategrey\": [119,136,153,1], \"lightsteelblue\": [176,196,222,1],\n \"lightyellow\": [255,255,224,1], \"lime\": [0,255,0,1],\n \"limegreen\": [50,205,50,1], \"linen\": [250,240,230,1],\n \"magenta\": [255,0,255,1], \"maroon\": [128,0,0,1],\n \"mediumaquamarine\": [102,205,170,1], \"mediumblue\": [0,0,205,1],\n \"mediumorchid\": [186,85,211,1], \"mediumpurple\": [147,112,219,1],\n \"mediumseagreen\": [60,179,113,1], \"mediumslateblue\": [123,104,238,1],\n \"mediumspringgreen\": [0,250,154,1], \"mediumturquoise\": [72,209,204,1],\n \"mediumvioletred\": [199,21,133,1], \"midnightblue\": [25,25,112,1],\n \"mintcream\": [245,255,250,1], \"mistyrose\": [255,228,225,1],\n \"moccasin\": [255,228,181,1], \"navajowhite\": [255,222,173,1],\n \"navy\": [0,0,128,1], \"oldlace\": [253,245,230,1],\n \"olive\": [128,128,0,1], \"olivedrab\": [107,142,35,1],\n \"orange\": [255,165,0,1], \"orangered\": [255,69,0,1],\n \"orchid\": [218,112,214,1], \"palegoldenrod\": [238,232,170,1],\n \"palegreen\": [152,251,152,1], \"paleturquoise\": [175,238,238,1],\n \"palevioletred\": [219,112,147,1], \"papayawhip\": [255,239,213,1],\n \"peachpuff\": [255,218,185,1], \"peru\": [205,133,63,1],\n \"pink\": [255,192,203,1], \"plum\": [221,160,221,1],\n \"powderblue\": [176,224,230,1], \"purple\": [128,0,128,1],\n \"red\": [255,0,0,1], \"rosybrown\": [188,143,143,1],\n \"royalblue\": [65,105,225,1], \"saddlebrown\": [139,69,19,1],\n \"salmon\": [250,128,114,1], \"sandybrown\": [244,164,96,1],\n \"seagreen\": [46,139,87,1], \"seashell\": [255,245,238,1],\n \"sienna\": [160,82,45,1], \"silver\": [192,192,192,1],\n \"skyblue\": [135,206,235,1], \"slateblue\": [106,90,205,1],\n \"slategray\": [112,128,144,1], \"slategrey\": [112,128,144,1],\n \"snow\": [255,250,250,1], \"springgreen\": [0,255,127,1],\n \"steelblue\": [70,130,180,1], \"tan\": [210,180,140,1],\n \"teal\": [0,128,128,1], \"thistle\": [216,191,216,1],\n \"tomato\": [255,99,71,1], \"turquoise\": [64,224,208,1],\n \"violet\": [238,130,238,1], \"wheat\": [245,222,179,1],\n \"white\": [255,255,255,1], \"whitesmoke\": [245,245,245,1],\n \"yellow\": [255,255,0,1], \"yellowgreen\": [154,205,50,1]}\n\nfunction clamp_css_byte(i) { // Clamp to integer 0 .. 255.\n i = Math.round(i); // Seems to be what Chrome does (vs truncation).\n return i < 0 ? 0 : i > 255 ? 255 : i;\n}\n\nfunction clamp_css_float(f) { // Clamp to float 0.0 .. 1.0.\n return f < 0 ? 0 : f > 1 ? 1 : f;\n}\n\nfunction parse_css_int(str) { // int or percentage.\n if (str[str.length - 1] === '%')\n return clamp_css_byte(parseFloat(str) / 100 * 255);\n return clamp_css_byte(parseInt(str));\n}\n\nfunction parse_css_float(str) { // float or percentage.\n if (str[str.length - 1] === '%')\n return clamp_css_float(parseFloat(str) / 100);\n return clamp_css_float(parseFloat(str));\n}\n\nfunction css_hue_to_rgb(m1, m2, h) {\n if (h < 0) h += 1;\n else if (h > 1) h -= 1;\n\n if (h * 6 < 1) return m1 + (m2 - m1) * h * 6;\n if (h * 2 < 1) return m2;\n if (h * 3 < 2) return m1 + (m2 - m1) * (2/3 - h) * 6;\n return m1;\n}\n\nfunction parseCSSColor(css_str) {\n // Remove all whitespace, not compliant, but should just be more accepting.\n var str = css_str.replace(/ /g, '').toLowerCase();\n\n // Color keywords (and transparent) lookup.\n if (str in kCSSColorTable) return kCSSColorTable[str].slice(); // dup.\n\n // #abc and #abc123 syntax.\n if (str[0] === '#') {\n if (str.length === 4) {\n var iv = parseInt(str.substr(1), 16); // TODO(deanm): Stricter parsing.\n if (!(iv >= 0 && iv <= 0xfff)) return null; // Covers NaN.\n return [((iv & 0xf00) >> 4) | ((iv & 0xf00) >> 8),\n (iv & 0xf0) | ((iv & 0xf0) >> 4),\n (iv & 0xf) | ((iv & 0xf) << 4),\n 1];\n } else if (str.length === 7) {\n var iv = parseInt(str.substr(1), 16); // TODO(deanm): Stricter parsing.\n if (!(iv >= 0 && iv <= 0xffffff)) return null; // Covers NaN.\n return [(iv & 0xff0000) >> 16,\n (iv & 0xff00) >> 8,\n iv & 0xff,\n 1];\n }\n\n return null;\n }\n\n var op = str.indexOf('('), ep = str.indexOf(')');\n if (op !== -1 && ep + 1 === str.length) {\n var fname = str.substr(0, op);\n var params = str.substr(op+1, ep-(op+1)).split(',');\n var alpha = 1; // To allow case fallthrough.\n switch (fname) {\n case 'rgba':\n if (params.length !== 4) return null;\n alpha = parse_css_float(params.pop());\n // Fall through.\n case 'rgb':\n if (params.length !== 3) return null;\n return [parse_css_int(params[0]),\n parse_css_int(params[1]),\n parse_css_int(params[2]),\n alpha];\n case 'hsla':\n if (params.length !== 4) return null;\n alpha = parse_css_float(params.pop());\n // Fall through.\n case 'hsl':\n if (params.length !== 3) return null;\n var h = (((parseFloat(params[0]) % 360) + 360) % 360) / 360; // 0 .. 1\n // NOTE(deanm): According to the CSS spec s/l should only be\n // percentages, but we don't bother and let float or percentage.\n var s = parse_css_float(params[1]);\n var l = parse_css_float(params[2]);\n var m2 = l <= 0.5 ? l * (s + 1) : l + s - l * s;\n var m1 = l * 2 - m2;\n return [clamp_css_byte(css_hue_to_rgb(m1, m2, h+1/3) * 255),\n clamp_css_byte(css_hue_to_rgb(m1, m2, h) * 255),\n clamp_css_byte(css_hue_to_rgb(m1, m2, h-1/3) * 255),\n alpha];\n default:\n return null;\n }\n }\n\n return null;\n}\n\ntry { exports.parseCSSColor = parseCSSColor } catch(e) { }\n",
"title": "$:/core/modules/utils/dom/csscolorparser.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom.js\ntype: application/javascript\nmodule-type: utils\n\nVarious static DOM-related utility functions.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nDetermines whether element 'a' contains element 'b'\nCode thanks to John Resig, http://ejohn.org/blog/comparing-document-position/\n*/\nexports.domContains = function(a,b) {\n\treturn a.contains ?\n\t\ta !== b && a.contains(b) :\n\t\t!!(a.compareDocumentPosition(b) & 16);\n};\n\nexports.removeChildren = function(node) {\n\twhile(node.hasChildNodes()) {\n\t\tnode.removeChild(node.firstChild);\n\t}\n};\n\nexports.hasClass = function(el,className) {\n\treturn el && el.className && el.className.toString().split(\" \").indexOf(className) !== -1;\n};\n\nexports.addClass = function(el,className) {\n\tvar c = el.className.split(\" \");\n\tif(c.indexOf(className) === -1) {\n\t\tc.push(className);\n\t}\n\tel.className = c.join(\" \");\n};\n\nexports.removeClass = function(el,className) {\n\tvar c = el.className.split(\" \"),\n\t\tp = c.indexOf(className);\n\tif(p !== -1) {\n\t\tc.splice(p,1);\n\t\tel.className = c.join(\" \");\n\t}\n};\n\nexports.toggleClass = function(el,className,status) {\n\tif(status === undefined) {\n\t\tstatus = !exports.hasClass(el,className);\n\t}\n\tif(status) {\n\t\texports.addClass(el,className);\n\t} else {\n\t\texports.removeClass(el,className);\n\t}\n};\n\n/*\nGet the first parent element that has scrollbars or use the body as fallback.\n*/\nexports.getScrollContainer = function(el) {\n\tvar doc = el.ownerDocument;\n\twhile(el.parentNode) {\t\n\t\tel = el.parentNode;\n\t\tif(el.scrollTop) {\n\t\t\treturn el;\n\t\t}\n\t}\n\treturn doc.body;\n};\n\n/*\nGet the scroll position of the viewport\nReturns:\n\t{\n\t\tx: horizontal scroll position in pixels,\n\t\ty: vertical scroll position in pixels\n\t}\n*/\nexports.getScrollPosition = function() {\n\tif(\"scrollX\" in window) {\n\t\treturn {x: window.scrollX, y: window.scrollY};\n\t} else {\n\t\treturn {x: document.documentElement.scrollLeft, y: document.documentElement.scrollTop};\n\t}\n};\n\n/*\nAdjust the height of a textarea to fit its content, preserving scroll position, and return the height\n*/\nexports.resizeTextAreaToFit = function(domNode,minHeight) {\n\t// Get the scroll container and register the current scroll position\n\tvar container = $tw.utils.getScrollContainer(domNode),\n\t\tscrollTop = container.scrollTop;\n // Measure the specified minimum height\n\tdomNode.style.height = minHeight;\n\tvar measuredHeight = domNode.offsetHeight;\n\t// Set its height to auto so that it snaps to the correct height\n\tdomNode.style.height = \"auto\";\n\t// Calculate the revised height\n\tvar newHeight = Math.max(domNode.scrollHeight + domNode.offsetHeight - domNode.clientHeight,measuredHeight);\n\t// Only try to change the height if it has changed\n\tif(newHeight !== domNode.offsetHeight) {\n\t\tdomNode.style.height = newHeight + \"px\";\n\t\t// Make sure that the dimensions of the textarea are recalculated\n\t\t$tw.utils.forceLayout(domNode);\n\t\t// Set the container to the position we registered at the beginning\n\t\tcontainer.scrollTop = scrollTop;\n\t}\n\treturn newHeight;\n};\n\n/*\nGets the bounding rectangle of an element in absolute page coordinates\n*/\nexports.getBoundingPageRect = function(element) {\n\tvar scrollPos = $tw.utils.getScrollPosition(),\n\t\tclientRect = element.getBoundingClientRect();\n\treturn {\n\t\tleft: clientRect.left + scrollPos.x,\n\t\twidth: clientRect.width,\n\t\tright: clientRect.right + scrollPos.x,\n\t\ttop: clientRect.top + scrollPos.y,\n\t\theight: clientRect.height,\n\t\tbottom: clientRect.bottom + scrollPos.y\n\t};\n};\n\n/*\nSaves a named password in the browser\n*/\nexports.savePassword = function(name,password) {\n\ttry {\n\t\tif(window.localStorage) {\n\t\t\tlocalStorage.setItem(\"tw5-password-\" + name,password);\n\t\t}\n\t} catch(e) {\n\t}\n};\n\n/*\nRetrieve a named password from the browser\n*/\nexports.getPassword = function(name) {\n\ttry {\n\t\treturn window.localStorage ? localStorage.getItem(\"tw5-password-\" + name) : \"\";\n\t} catch(e) {\n\t\treturn \"\";\n\t}\n};\n\n/*\nForce layout of a dom node and its descendents\n*/\nexports.forceLayout = function(element) {\n\tvar dummy = element.offsetWidth;\n};\n\n/*\nPulse an element for debugging purposes\n*/\nexports.pulseElement = function(element) {\n\t// Event handler to remove the class at the end\n\telement.addEventListener($tw.browser.animationEnd,function handler(event) {\n\t\telement.removeEventListener($tw.browser.animationEnd,handler,false);\n\t\t$tw.utils.removeClass(element,\"pulse\");\n\t},false);\n\t// Apply the pulse class\n\t$tw.utils.removeClass(element,\"pulse\");\n\t$tw.utils.forceLayout(element);\n\t$tw.utils.addClass(element,\"pulse\");\n};\n\n/*\nAttach specified event handlers to a DOM node\ndomNode: where to attach the event handlers\nevents: array of event handlers to be added (see below)\nEach entry in the events array is an object with these properties:\nhandlerFunction: optional event handler function\nhandlerObject: optional event handler object\nhandlerMethod: optionally specifies object handler method name (defaults to `handleEvent`)\n*/\nexports.addEventListeners = function(domNode,events) {\n\t$tw.utils.each(events,function(eventInfo) {\n\t\tvar handler;\n\t\tif(eventInfo.handlerFunction) {\n\t\t\thandler = eventInfo.handlerFunction;\n\t\t} else if(eventInfo.handlerObject) {\n\t\t\tif(eventInfo.handlerMethod) {\n\t\t\t\thandler = function(event) {\n\t\t\t\t\teventInfo.handlerObject[eventInfo.handlerMethod].call(eventInfo.handlerObject,event);\n\t\t\t\t};\t\n\t\t\t} else {\n\t\t\t\thandler = eventInfo.handlerObject;\n\t\t\t}\n\t\t}\n\t\tdomNode.addEventListener(eventInfo.name,handler,false);\n\t});\n};\n\n/*\nGet the computed styles applied to an element as an array of strings of individual CSS properties\n*/\nexports.getComputedStyles = function(domNode) {\n\tvar textAreaStyles = window.getComputedStyle(domNode,null),\n\t\tstyleDefs = [],\n\t\tname;\n\tfor(var t=0; t<textAreaStyles.length; t++) {\n\t\tname = textAreaStyles[t];\n\t\tstyleDefs.push(name + \": \" + textAreaStyles.getPropertyValue(name) + \";\");\n\t}\n\treturn styleDefs;\n};\n\n/*\nApply a set of styles passed as an array of strings of individual CSS properties\n*/\nexports.setStyles = function(domNode,styleDefs) {\n\tdomNode.style.cssText = styleDefs.join(\"\");\n};\n\n/*\nCopy the computed styles from a source element to a destination element\n*/\nexports.copyStyles = function(srcDomNode,dstDomNode) {\n\t$tw.utils.setStyles(dstDomNode,$tw.utils.getComputedStyles(srcDomNode));\n};\n\n})();\n",
"title": "$:/core/modules/utils/dom.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/dragndrop.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/dragndrop.js\ntype: application/javascript\nmodule-type: utils\n\nBrowser data transfer utilities, used with the clipboard and drag and drop\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nOptions:\n\ndomNode: dom node to make draggable\ndragImageType: \"pill\" or \"dom\"\ndragTiddlerFn: optional function to retrieve the title of tiddler to drag\ndragFilterFn: optional function to retreive the filter defining a list of tiddlers to drag\nwidget: widget to use as the contect for the filter\n*/\nexports.makeDraggable = function(options) {\n\tvar dragImageType = options.dragImageType || \"dom\",\n\t\tdragImage,\n\t\tdomNode = options.domNode;\n\t// Make the dom node draggable (not necessary for anchor tags)\n\tif((domNode.tagName || \"\").toLowerCase() !== \"a\") {\n\t\tdomNode.setAttribute(\"draggable\",\"true\");\t\t\n\t}\n\t// Add event handlers\n\t$tw.utils.addEventListeners(domNode,[\n\t\t{name: \"dragstart\", handlerFunction: function(event) {\n\t\t\t// Collect the tiddlers being dragged\n\t\t\tvar dragTiddler = options.dragTiddlerFn && options.dragTiddlerFn(),\n\t\t\t\tdragFilter = options.dragFilterFn && options.dragFilterFn(),\n\t\t\t\ttitles = dragTiddler ? [dragTiddler] : [];\n\t\t\tif(dragFilter) {\n\t\t\t\ttitles.push.apply(titles,options.widget.wiki.filterTiddlers(dragFilter,options.widget));\n\t\t\t}\n\t\t\tvar titleString = $tw.utils.stringifyList(titles);\n\t\t\t// Check that we've something to drag\n\t\t\tif(titles.length > 0 && event.target === domNode) {\n\t\t\t\t// Mark the drag in progress\n\t\t\t\t$tw.dragInProgress = domNode;\n\t\t\t\t// Set the dragging class on the element being dragged\n\t\t\t\t$tw.utils.addClass(event.target,\"tc-dragging\");\n\t\t\t\t// Create the drag image elements\n\t\t\t\tdragImage = options.widget.document.createElement(\"div\");\n\t\t\t\tdragImage.className = \"tc-tiddler-dragger\";\n\t\t\t\tvar inner = options.widget.document.createElement(\"div\");\n\t\t\t\tinner.className = \"tc-tiddler-dragger-inner\";\n\t\t\t\tinner.appendChild(options.widget.document.createTextNode(\n\t\t\t\t\ttitles.length === 1 ? \n\t\t\t\t\t\ttitles[0] :\n\t\t\t\t\t\ttitles.length + \" tiddlers\"\n\t\t\t\t));\n\t\t\t\tdragImage.appendChild(inner);\n\t\t\t\toptions.widget.document.body.appendChild(dragImage);\n\t\t\t\t// Set the data transfer properties\n\t\t\t\tvar dataTransfer = event.dataTransfer;\n\t\t\t\t// Set up the image\n\t\t\t\tdataTransfer.effectAllowed = \"all\";\n\t\t\t\tif(dataTransfer.setDragImage) {\n\t\t\t\t\tif(dragImageType === \"pill\") {\n\t\t\t\t\t\tdataTransfer.setDragImage(dragImage.firstChild,-16,-16);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tvar r = domNode.getBoundingClientRect();\n\t\t\t\t\t\tdataTransfer.setDragImage(domNode,event.clientX-r.left,event.clientY-r.top);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Set up the data transfer\n\t\t\t\tif(dataTransfer.clearData) {\n\t\t\t\t\tdataTransfer.clearData();\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\tvar jsonData = [];\n\t\t\t\tif(titles.length > 1) {\n\t\t\t\t\ttitles.forEach(function(title) {\n\t\t\t\t\t\tjsonData.push(options.widget.wiki.getTiddlerAsJson(title));\n\t\t\t\t\t});\n\t\t\t\t\tjsonData = \"[\" + jsonData.join(\",\") + \"]\";\n\t\t\t\t} else {\n\t\t\t\t\tjsonData = options.widget.wiki.getTiddlerAsJson(titles[0]);\n\t\t\t\t}\n\t\t\t\t// IE doesn't like these content types\n\t\t\t\tif(!$tw.browser.isIE) {\n\t\t\t\t\tdataTransfer.setData(\"text/vnd.tiddler\",jsonData);\n\t\t\t\t\tdataTransfer.setData(\"text/plain\",titleString);\n\t\t\t\t\tdataTransfer.setData(\"text/x-moz-url\",\"data:text/vnd.tiddler,\" + encodeURIComponent(jsonData));\n\t\t\t\t}\n\t\t\t\tdataTransfer.setData(\"URL\",\"data:text/vnd.tiddler,\" + encodeURIComponent(jsonData));\n\t\t\t\tdataTransfer.setData(\"Text\",titleString);\n\t\t\t\tevent.stopPropagation();\n\t\t\t}\n\t\t\treturn false;\n\t\t}},\n\t\t{name: \"dragend\", handlerFunction: function(event) {\n\t\t\tif(event.target === domNode) {\n\t\t\t\t$tw.dragInProgress = null;\n\t\t\t\t// Remove the dragging class on the element being dragged\n\t\t\t\t$tw.utils.removeClass(event.target,\"tc-dragging\");\n\t\t\t\t// Delete the drag image element\n\t\t\t\tif(dragImage) {\n\t\t\t\t\tdragImage.parentNode.removeChild(dragImage);\n\t\t\t\t\tdragImage = null;\n\t\t\t\t}\n\t\t\t}\n\t\t\treturn false;\n\t\t}}\n\t]);\n};\n\nexports.importDataTransfer = function(dataTransfer,fallbackTitle,callback) {\n\t// Try each provided data type in turn\n\tfor(var t=0; t<importDataTypes.length; t++) {\n\t\tif(!$tw.browser.isIE || importDataTypes[t].IECompatible) {\n\t\t\t// Get the data\n\t\t\tvar dataType = importDataTypes[t];\n\t\t\t\tvar data = dataTransfer.getData(dataType.type);\n\t\t\t// Import the tiddlers in the data\n\t\t\tif(data !== \"\" && data !== null) {\n\t\t\t\tif($tw.log.IMPORT) {\n\t\t\t\t\tconsole.log(\"Importing data type '\" + dataType.type + \"', data: '\" + data + \"'\")\n\t\t\t\t}\n\t\t\t\tvar tiddlerFields = dataType.toTiddlerFieldsArray(data,fallbackTitle);\n\t\t\t\tcallback(tiddlerFields);\n\t\t\t\treturn;\n\t\t\t}\n\t\t}\n\t}\n};\n\nvar importDataTypes = [\n\t{type: \"text/vnd.tiddler\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn parseJSONTiddlers(data,fallbackTitle);\n\t}},\n\t{type: \"URL\", IECompatible: true, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\t// Check for tiddler data URI\n\t\tvar match = decodeURIComponent(data).match(/^data\\:text\\/vnd\\.tiddler,(.*)/i);\n\t\tif(match) {\n\t\t\treturn parseJSONTiddlers(match[1],fallbackTitle);\n\t\t} else {\n\t\t\treturn [{title: fallbackTitle, text: data}]; // As URL string\n\t\t}\n\t}},\n\t{type: \"text/x-moz-url\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\t// Check for tiddler data URI\n\t\tvar match = decodeURIComponent(data).match(/^data\\:text\\/vnd\\.tiddler,(.*)/i);\n\t\tif(match) {\n\t\t\treturn parseJSONTiddlers(match[1],fallbackTitle);\n\t\t} else {\n\t\t\treturn [{title: fallbackTitle, text: data}]; // As URL string\n\t\t}\n\t}},\n\t{type: \"text/html\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn [{title: fallbackTitle, text: data}];\n\t}},\n\t{type: \"text/plain\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn [{title: fallbackTitle, text: data}];\n\t}},\n\t{type: \"Text\", IECompatible: true, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn [{title: fallbackTitle, text: data}];\n\t}},\n\t{type: \"text/uri-list\", IECompatible: false, toTiddlerFieldsArray: function(data,fallbackTitle) {\n\t\treturn [{title: fallbackTitle, text: data}];\n\t}}\n];\n\nfunction parseJSONTiddlers(json,fallbackTitle) {\n\tvar data = JSON.parse(json);\n\tif(!$tw.utils.isArray(data)) {\n\t\tdata = [data];\n\t}\n\tdata.forEach(function(fields) {\n\t\tfields.title = fields.title || fallbackTitle;\n\t});\n\treturn data;\n};\n\n})();\n",
"title": "$:/core/modules/utils/dom/dragndrop.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/http.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/http.js\ntype: application/javascript\nmodule-type: utils\n\nBrowser HTTP support\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nA quick and dirty HTTP function; to be refactored later. Options are:\n\turl: URL to retrieve\n\ttype: GET, PUT, POST etc\n\tcallback: function invoked with (err,data)\n\treturnProp: string name of the property to return as first argument of callback\n*/\nexports.httpRequest = function(options) {\n\tvar type = options.type || \"GET\",\n\t\theaders = options.headers || {accept: \"application/json\"},\n\t\treturnProp = options.returnProp || \"responseText\",\n\t\trequest = new XMLHttpRequest(),\n\t\tdata = \"\",\n\t\tf,results;\n\t// Massage the data hashmap into a string\n\tif(options.data) {\n\t\tif(typeof options.data === \"string\") { // Already a string\n\t\t\tdata = options.data;\n\t\t} else { // A hashmap of strings\n\t\t\tresults = [];\n\t\t\t$tw.utils.each(options.data,function(dataItem,dataItemTitle) {\n\t\t\t\tresults.push(dataItemTitle + \"=\" + encodeURIComponent(dataItem));\n\t\t\t});\n\t\t\tdata = results.join(\"&\");\n\t\t}\n\t}\n\t// Set up the state change handler\n\trequest.onreadystatechange = function() {\n\t\tif(this.readyState === 4) {\n\t\t\tif(this.status === 200 || this.status === 201 || this.status === 204) {\n\t\t\t\t// Success!\n\t\t\t\toptions.callback(null,this[returnProp],this);\n\t\t\t\treturn;\n\t\t\t}\n\t\t// Something went wrong\n\t\toptions.callback($tw.language.getString(\"Error/XMLHttpRequest\") + \": \" + this.status);\n\t\t}\n\t};\n\t// Make the request\n\trequest.open(type,options.url,true);\n\tif(headers) {\n\t\t$tw.utils.each(headers,function(header,headerTitle,object) {\n\t\t\trequest.setRequestHeader(headerTitle,header);\n\t\t});\n\t}\n\tif(data && !$tw.utils.hop(headers,\"Content-type\")) {\n\t\trequest.setRequestHeader(\"Content-type\",\"application/x-www-form-urlencoded; charset=UTF-8\");\n\t}\n\ttry {\n\t\trequest.send(data);\n\t} catch(e) {\n\t\toptions.callback(e);\n\t}\n\treturn request;\n};\n\n})();\n",
"title": "$:/core/modules/utils/dom/http.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/keyboard.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/keyboard.js\ntype: application/javascript\nmodule-type: utils\n\nKeyboard utilities; now deprecated. Instead, use $tw.keyboardManager\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n[\"parseKeyDescriptor\",\"checkKeyDescriptor\"].forEach(function(method) {\n\texports[method] = function() {\n\t\tif($tw.keyboardManager) {\n\t\t\treturn $tw.keyboardManager[method].apply($tw.keyboardManager,Array.prototype.slice.call(arguments,0));\n\t\t} else {\n\t\t\treturn null\n\t\t}\n\t};\n});\n\n})();\n",
"title": "$:/core/modules/utils/dom/keyboard.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/modal.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/modal.js\ntype: application/javascript\nmodule-type: utils\n\nModal message mechanism\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nvar Modal = function(wiki) {\n\tthis.wiki = wiki;\n\tthis.modalCount = 0;\n};\n\n/*\nDisplay a modal dialogue\n\ttitle: Title of tiddler to display\n\toptions: see below\nOptions include:\n\tdownloadLink: Text of a big download link to include\n*/\nModal.prototype.display = function(title,options) {\n\toptions = options || {};\n\tvar self = this,\n\t\trefreshHandler,\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\ttiddler = this.wiki.getTiddler(title);\n\t// Don't do anything if the tiddler doesn't exist\n\tif(!tiddler) {\n\t\treturn;\n\t}\n\t// Create the variables\n\tvar variables = $tw.utils.extend({currentTiddler: title},options.variables);\n\t// Create the wrapper divs\n\tvar wrapper = document.createElement(\"div\"),\n\t\tmodalBackdrop = document.createElement(\"div\"),\n\t\tmodalWrapper = document.createElement(\"div\"),\n\t\tmodalHeader = document.createElement(\"div\"),\n\t\theaderTitle = document.createElement(\"h3\"),\n\t\tmodalBody = document.createElement(\"div\"),\n\t\tmodalLink = document.createElement(\"a\"),\n\t\tmodalFooter = document.createElement(\"div\"),\n\t\tmodalFooterHelp = document.createElement(\"span\"),\n\t\tmodalFooterButtons = document.createElement(\"span\");\n\t// Up the modal count and adjust the body class\n\tthis.modalCount++;\n\tthis.adjustPageClass();\n\t// Add classes\n\t$tw.utils.addClass(wrapper,\"tc-modal-wrapper\");\n\t$tw.utils.addClass(modalBackdrop,\"tc-modal-backdrop\");\n\t$tw.utils.addClass(modalWrapper,\"tc-modal\");\n\t$tw.utils.addClass(modalHeader,\"tc-modal-header\");\n\t$tw.utils.addClass(modalBody,\"tc-modal-body\");\n\t$tw.utils.addClass(modalFooter,\"tc-modal-footer\");\n\t// Join them together\n\twrapper.appendChild(modalBackdrop);\n\twrapper.appendChild(modalWrapper);\n\tmodalHeader.appendChild(headerTitle);\n\tmodalWrapper.appendChild(modalHeader);\n\tmodalWrapper.appendChild(modalBody);\n\tmodalFooter.appendChild(modalFooterHelp);\n\tmodalFooter.appendChild(modalFooterButtons);\n\tmodalWrapper.appendChild(modalFooter);\n\t// Render the title of the message\n\tvar headerWidgetNode = this.wiki.makeTranscludeWidget(title,{\n\t\tfield: \"subtitle\",\n\t\tmode: \"inline\",\n\t\tchildren: [{\n\t\t\ttype: \"text\",\n\t\t\tattributes: {\n\t\t\t\ttext: {\n\t\t\t\t\ttype: \"string\",\n\t\t\t\t\tvalue: title\n\t\t}}}],\n\t\tparentWidget: $tw.rootWidget,\n\t\tdocument: document,\n\t\tvariables: variables,\n\t\timportPageMacros: true\n\t});\n\theaderWidgetNode.render(headerTitle,null);\n\t// Render the body of the message\n\tvar bodyWidgetNode = this.wiki.makeTranscludeWidget(title,{\n\t\tparentWidget: $tw.rootWidget,\n\t\tdocument: document,\n\t\tvariables: variables,\n\t\timportPageMacros: true\n\t});\n\tbodyWidgetNode.render(modalBody,null);\n\t// Setup the link if present\n\tif(options.downloadLink) {\n\t\tmodalLink.href = options.downloadLink;\n\t\tmodalLink.appendChild(document.createTextNode(\"Right-click to save changes\"));\n\t\tmodalBody.appendChild(modalLink);\n\t}\n\t// Render the footer of the message\n\tif(tiddler && tiddler.fields && tiddler.fields.help) {\n\t\tvar link = document.createElement(\"a\");\n\t\tlink.setAttribute(\"href\",tiddler.fields.help);\n\t\tlink.setAttribute(\"target\",\"_blank\");\n\t\tlink.setAttribute(\"rel\",\"noopener noreferrer\");\n\t\tlink.appendChild(document.createTextNode(\"Help\"));\n\t\tmodalFooterHelp.appendChild(link);\n\t\tmodalFooterHelp.style.float = \"left\";\n\t}\n\tvar footerWidgetNode = this.wiki.makeTranscludeWidget(title,{\n\t\tfield: \"footer\",\n\t\tmode: \"inline\",\n\t\tchildren: [{\n\t\t\ttype: \"button\",\n\t\t\tattributes: {\n\t\t\t\tmessage: {\n\t\t\t\t\ttype: \"string\",\n\t\t\t\t\tvalue: \"tm-close-tiddler\"\n\t\t\t\t}\n\t\t\t},\n\t\t\tchildren: [{\n\t\t\t\ttype: \"text\",\n\t\t\t\tattributes: {\n\t\t\t\t\ttext: {\n\t\t\t\t\t\ttype: \"string\",\n\t\t\t\t\t\tvalue: $tw.language.getString(\"Buttons/Close/Caption\")\n\t\t\t}}}\n\t\t]}],\n\t\tparentWidget: $tw.rootWidget,\n\t\tdocument: document,\n\t\tvariables: variables,\n\t\timportPageMacros: true\n\t});\n\tfooterWidgetNode.render(modalFooterButtons,null);\n\t// Set up the refresh handler\n\trefreshHandler = function(changes) {\n\t\theaderWidgetNode.refresh(changes,modalHeader,null);\n\t\tbodyWidgetNode.refresh(changes,modalBody,null);\n\t\tfooterWidgetNode.refresh(changes,modalFooterButtons,null);\n\t};\n\tthis.wiki.addEventListener(\"change\",refreshHandler);\n\t// Add the close event handler\n\tvar closeHandler = function(event) {\n\t\t// Remove our refresh handler\n\t\tself.wiki.removeEventListener(\"change\",refreshHandler);\n\t\t// Decrease the modal count and adjust the body class\n\t\tself.modalCount--;\n\t\tself.adjustPageClass();\n\t\t// Force layout and animate the modal message away\n\t\t$tw.utils.forceLayout(modalBackdrop);\n\t\t$tw.utils.forceLayout(modalWrapper);\n\t\t$tw.utils.setStyle(modalBackdrop,[\n\t\t\t{opacity: \"0\"}\n\t\t]);\n\t\t$tw.utils.setStyle(modalWrapper,[\n\t\t\t{transform: \"translateY(\" + window.innerHeight + \"px)\"}\n\t\t]);\n\t\t// Set up an event for the transition end\n\t\twindow.setTimeout(function() {\n\t\t\tif(wrapper.parentNode) {\n\t\t\t\t// Remove the modal message from the DOM\n\t\t\t\tdocument.body.removeChild(wrapper);\n\t\t\t}\n\t\t},duration);\n\t\t// Don't let anyone else handle the tm-close-tiddler message\n\t\treturn false;\n\t};\n\theaderWidgetNode.addEventListener(\"tm-close-tiddler\",closeHandler,false);\n\tbodyWidgetNode.addEventListener(\"tm-close-tiddler\",closeHandler,false);\n\tfooterWidgetNode.addEventListener(\"tm-close-tiddler\",closeHandler,false);\n\t// Set the initial styles for the message\n\t$tw.utils.setStyle(modalBackdrop,[\n\t\t{opacity: \"0\"}\n\t]);\n\t$tw.utils.setStyle(modalWrapper,[\n\t\t{transformOrigin: \"0% 0%\"},\n\t\t{transform: \"translateY(\" + (-window.innerHeight) + \"px)\"}\n\t]);\n\t// Put the message into the document\n\tdocument.body.appendChild(wrapper);\n\t// Set up animation for the styles\n\t$tw.utils.setStyle(modalBackdrop,[\n\t\t{transition: \"opacity \" + duration + \"ms ease-out\"}\n\t]);\n\t$tw.utils.setStyle(modalWrapper,[\n\t\t{transition: $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms ease-in-out\"}\n\t]);\n\t// Force layout\n\t$tw.utils.forceLayout(modalBackdrop);\n\t$tw.utils.forceLayout(modalWrapper);\n\t// Set final animated styles\n\t$tw.utils.setStyle(modalBackdrop,[\n\t\t{opacity: \"0.7\"}\n\t]);\n\t$tw.utils.setStyle(modalWrapper,[\n\t\t{transform: \"translateY(0px)\"}\n\t]);\n};\n\nModal.prototype.adjustPageClass = function() {\n\tif($tw.pageContainer) {\n\t\t$tw.utils.toggleClass($tw.pageContainer,\"tc-modal-displayed\",this.modalCount > 0);\n\t}\n};\n\nexports.Modal = Modal;\n\n})();\n",
"title": "$:/core/modules/utils/dom/modal.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/notifier.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/notifier.js\ntype: application/javascript\nmodule-type: utils\n\nNotifier mechanism\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nvar Notifier = function(wiki) {\n\tthis.wiki = wiki;\n};\n\n/*\nDisplay a notification\n\ttitle: Title of tiddler containing the notification text\n\toptions: see below\nOptions include:\n*/\nNotifier.prototype.display = function(title,options) {\n\toptions = options || {};\n\t// Create the wrapper divs\n\tvar self = this,\n\t\tnotification = document.createElement(\"div\"),\n\t\ttiddler = this.wiki.getTiddler(title),\n\t\tduration = $tw.utils.getAnimationDuration(),\n\t\trefreshHandler;\n\t// Don't do anything if the tiddler doesn't exist\n\tif(!tiddler) {\n\t\treturn;\n\t}\n\t// Add classes\n\t$tw.utils.addClass(notification,\"tc-notification\");\n\t// Create the variables\n\tvar variables = $tw.utils.extend({currentTiddler: title},options.variables);\n\t// Render the body of the notification\n\tvar widgetNode = this.wiki.makeTranscludeWidget(title,{\n\t\tparentWidget: $tw.rootWidget,\n\t\tdocument: document,\n\t\tvariables: variables,\n\t\timportPageMacros: true});\n\twidgetNode.render(notification,null);\n\trefreshHandler = function(changes) {\n\t\twidgetNode.refresh(changes,notification,null);\n\t};\n\tthis.wiki.addEventListener(\"change\",refreshHandler);\n\t// Set the initial styles for the notification\n\t$tw.utils.setStyle(notification,[\n\t\t{opacity: \"0\"},\n\t\t{transformOrigin: \"0% 0%\"},\n\t\t{transform: \"translateY(\" + (-window.innerHeight) + \"px)\"},\n\t\t{transition: \"opacity \" + duration + \"ms ease-out, \" + $tw.utils.roundTripPropertyName(\"transform\") + \" \" + duration + \"ms ease-in-out\"}\n\t]);\n\t// Add the notification to the DOM\n\tdocument.body.appendChild(notification);\n\t// Force layout\n\t$tw.utils.forceLayout(notification);\n\t// Set final animated styles\n\t$tw.utils.setStyle(notification,[\n\t\t{opacity: \"1.0\"},\n\t\t{transform: \"translateY(0px)\"}\n\t]);\n\t// Set a timer to remove the notification\n\twindow.setTimeout(function() {\n\t\t// Remove our change event handler\n\t\tself.wiki.removeEventListener(\"change\",refreshHandler);\n\t\t// Force layout and animate the notification away\n\t\t$tw.utils.forceLayout(notification);\n\t\t$tw.utils.setStyle(notification,[\n\t\t\t{opacity: \"0.0\"},\n\t\t\t{transform: \"translateX(\" + (notification.offsetWidth) + \"px)\"}\n\t\t]);\n\t\t// Remove the modal message from the DOM once the transition ends\n\t\tsetTimeout(function() {\n\t\t\tif(notification.parentNode) {\n\t\t\t\tdocument.body.removeChild(notification);\n\t\t\t}\n\t\t},duration);\n\t},$tw.config.preferences.notificationDuration);\n};\n\nexports.Notifier = Notifier;\n\n})();\n",
"title": "$:/core/modules/utils/dom/notifier.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/popup.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/popup.js\ntype: application/javascript\nmodule-type: utils\n\nModule that creates a $tw.utils.Popup object prototype that manages popups in the browser\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nCreates a Popup object with these options:\n\trootElement: the DOM element to which the popup zapper should be attached\n*/\nvar Popup = function(options) {\n\toptions = options || {};\n\tthis.rootElement = options.rootElement || document.documentElement;\n\tthis.popups = []; // Array of {title:,wiki:,domNode:} objects\n};\n\n/*\nTrigger a popup open or closed. Parameters are in a hashmap:\n\ttitle: title of the tiddler where the popup details are stored\n\tdomNode: dom node to which the popup will be positioned\n\twiki: wiki\n\tforce: if specified, forces the popup state to true or false (instead of toggling it)\n*/\nPopup.prototype.triggerPopup = function(options) {\n\t// Check if this popup is already active\n\tvar index = this.findPopup(options.title);\n\t// Compute the new state\n\tvar state = index === -1;\n\tif(options.force !== undefined) {\n\t\tstate = options.force;\n\t}\n\t// Show or cancel the popup according to the new state\n\tif(state) {\n\t\tthis.show(options);\n\t} else {\n\t\tthis.cancel(index);\n\t}\n};\n\nPopup.prototype.findPopup = function(title) {\n\tvar index = -1;\n\tfor(var t=0; t<this.popups.length; t++) {\n\t\tif(this.popups[t].title === title) {\n\t\t\tindex = t;\n\t\t}\n\t}\n\treturn index;\n};\n\nPopup.prototype.handleEvent = function(event) {\n\tif(event.type === \"click\") {\n\t\t// Find out what was clicked on\n\t\tvar info = this.popupInfo(event.target),\n\t\t\tcancelLevel = info.popupLevel - 1;\n\t\t// Don't remove the level that was clicked on if we clicked on a handle\n\t\tif(info.isHandle) {\n\t\t\tcancelLevel++;\n\t\t}\n\t\t// Cancel\n\t\tthis.cancel(cancelLevel);\n\t}\n};\n\n/*\nFind the popup level containing a DOM node. Returns:\npopupLevel: count of the number of nested popups containing the specified element\nisHandle: true if the specified element is within a popup handle\n*/\nPopup.prototype.popupInfo = function(domNode) {\n\tvar isHandle = false,\n\t\tpopupCount = 0,\n\t\tnode = domNode;\n\t// First check ancestors to see if we're within a popup handle\n\twhile(node) {\n\t\tif($tw.utils.hasClass(node,\"tc-popup-handle\")) {\n\t\t\tisHandle = true;\n\t\t\tpopupCount++;\n\t\t}\n\t\tif($tw.utils.hasClass(node,\"tc-popup-keep\")) {\n\t\t\tisHandle = true;\n\t\t}\n\t\tnode = node.parentNode;\n\t}\n\t// Then count the number of ancestor popups\n\tnode = domNode;\n\twhile(node) {\n\t\tif($tw.utils.hasClass(node,\"tc-popup\")) {\n\t\t\tpopupCount++;\n\t\t}\n\t\tnode = node.parentNode;\n\t}\n\tvar info = {\n\t\tpopupLevel: popupCount,\n\t\tisHandle: isHandle\n\t};\n\treturn info;\n};\n\n/*\nDisplay a popup by adding it to the stack\n*/\nPopup.prototype.show = function(options) {\n\t// Find out what was clicked on\n\tvar info = this.popupInfo(options.domNode);\n\t// Cancel any higher level popups\n\tthis.cancel(info.popupLevel);\n\t// Store the popup details if not already there\n\tif(this.findPopup(options.title) === -1) {\n\t\tthis.popups.push({\n\t\t\ttitle: options.title,\n\t\t\twiki: options.wiki,\n\t\t\tdomNode: options.domNode\n\t\t});\n\t}\n\t// Set the state tiddler\n\toptions.wiki.setTextReference(options.title,\n\t\t\t\"(\" + options.domNode.offsetLeft + \",\" + options.domNode.offsetTop + \",\" + \n\t\t\t\toptions.domNode.offsetWidth + \",\" + options.domNode.offsetHeight + \")\");\n\t// Add the click handler if we have any popups\n\tif(this.popups.length > 0) {\n\t\tthis.rootElement.addEventListener(\"click\",this,true);\t\t\n\t}\n};\n\n/*\nCancel all popups at or above a specified level or DOM node\nlevel: popup level to cancel (0 cancels all popups)\n*/\nPopup.prototype.cancel = function(level) {\n\tvar numPopups = this.popups.length;\n\tlevel = Math.max(0,Math.min(level,numPopups));\n\tfor(var t=level; t<numPopups; t++) {\n\t\tvar popup = this.popups.pop();\n\t\tif(popup.title) {\n\t\t\tpopup.wiki.deleteTiddler(popup.title);\n\t\t}\n\t}\n\tif(this.popups.length === 0) {\n\t\tthis.rootElement.removeEventListener(\"click\",this,false);\n\t}\n};\n\n/*\nReturns true if the specified title and text identifies an active popup\n*/\nPopup.prototype.readPopupState = function(text) {\n\tvar popupLocationRegExp = /^\\((-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+)\\)$/;\n\treturn popupLocationRegExp.test(text);\n};\n\nexports.Popup = Popup;\n\n})();\n",
"title": "$:/core/modules/utils/dom/popup.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/dom/scroller.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/dom/scroller.js\ntype: application/javascript\nmodule-type: utils\n\nModule that creates a $tw.utils.Scroller object prototype that manages scrolling in the browser\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nEvent handler for when the `tm-scroll` event hits the document body\n*/\nvar PageScroller = function() {\n\tthis.idRequestFrame = null;\n\tthis.requestAnimationFrame = window.requestAnimationFrame ||\n\t\twindow.webkitRequestAnimationFrame ||\n\t\twindow.mozRequestAnimationFrame ||\n\t\tfunction(callback) {\n\t\t\treturn window.setTimeout(callback, 1000/60);\n\t\t};\n\tthis.cancelAnimationFrame = window.cancelAnimationFrame ||\n\t\twindow.webkitCancelAnimationFrame ||\n\t\twindow.webkitCancelRequestAnimationFrame ||\n\t\twindow.mozCancelAnimationFrame ||\n\t\twindow.mozCancelRequestAnimationFrame ||\n\t\tfunction(id) {\n\t\t\twindow.clearTimeout(id);\n\t\t};\n};\n\nPageScroller.prototype.cancelScroll = function() {\n\tif(this.idRequestFrame) {\n\t\tthis.cancelAnimationFrame.call(window,this.idRequestFrame);\n\t\tthis.idRequestFrame = null;\n\t}\n};\n\n/*\nHandle an event\n*/\nPageScroller.prototype.handleEvent = function(event) {\n\tif(event.type === \"tm-scroll\") {\n\t\treturn this.scrollIntoView(event.target);\n\t}\n\treturn true;\n};\n\n/*\nHandle a scroll event hitting the page document\n*/\nPageScroller.prototype.scrollIntoView = function(element) {\n\tvar duration = $tw.utils.getAnimationDuration();\n\t// Now get ready to scroll the body\n\tthis.cancelScroll();\n\tthis.startTime = Date.now();\n\tvar scrollPosition = $tw.utils.getScrollPosition();\n\t// Get the client bounds of the element and adjust by the scroll position\n\tvar clientBounds = element.getBoundingClientRect(),\n\t\tbounds = {\n\t\t\tleft: clientBounds.left + scrollPosition.x,\n\t\t\ttop: clientBounds.top + scrollPosition.y,\n\t\t\twidth: clientBounds.width,\n\t\t\theight: clientBounds.height\n\t\t};\n\t// We'll consider the horizontal and vertical scroll directions separately via this function\n\t// targetPos/targetSize - position and size of the target element\n\t// currentPos/currentSize - position and size of the current scroll viewport\n\t// returns: new position of the scroll viewport\n\tvar getEndPos = function(targetPos,targetSize,currentPos,currentSize) {\n\t\t\tvar newPos = currentPos;\n\t\t\t// If the target is above/left of the current view, then scroll to it's top/left\n\t\t\tif(targetPos <= currentPos) {\n\t\t\t\tnewPos = targetPos;\n\t\t\t// If the target is smaller than the window and the scroll position is too far up, then scroll till the target is at the bottom of the window\n\t\t\t} else if(targetSize < currentSize && currentPos < (targetPos + targetSize - currentSize)) {\n\t\t\t\tnewPos = targetPos + targetSize - currentSize;\n\t\t\t// If the target is big, then just scroll to the top\n\t\t\t} else if(currentPos < targetPos) {\n\t\t\t\tnewPos = targetPos;\n\t\t\t// Otherwise, stay where we are\n\t\t\t} else {\n\t\t\t\tnewPos = currentPos;\n\t\t\t}\n\t\t\t// If we are scrolling within 50 pixels of the top/left then snap to zero\n\t\t\tif(newPos < 50) {\n\t\t\t\tnewPos = 0;\n\t\t\t}\n\t\t\treturn newPos;\n\t\t},\n\t\tendX = getEndPos(bounds.left,bounds.width,scrollPosition.x,window.innerWidth),\n\t\tendY = getEndPos(bounds.top,bounds.height,scrollPosition.y,window.innerHeight);\n\t// Only scroll if the position has changed\n\tif(endX !== scrollPosition.x || endY !== scrollPosition.y) {\n\t\tvar self = this,\n\t\t\tdrawFrame;\n\t\tdrawFrame = function () {\n\t\t\tvar t;\n\t\t\tif(duration <= 0) {\n\t\t\t\tt = 1;\n\t\t\t} else {\n\t\t\t\tt = ((Date.now()) - self.startTime) / duration;\t\n\t\t\t}\n\t\t\tif(t >= 1) {\n\t\t\t\tself.cancelScroll();\n\t\t\t\tt = 1;\n\t\t\t}\n\t\t\tt = $tw.utils.slowInSlowOut(t);\n\t\t\twindow.scrollTo(scrollPosition.x + (endX - scrollPosition.x) * t,scrollPosition.y + (endY - scrollPosition.y) * t);\n\t\t\tif(t < 1) {\n\t\t\t\tself.idRequestFrame = self.requestAnimationFrame.call(window,drawFrame);\n\t\t\t}\n\t\t};\n\t\tdrawFrame();\n\t}\n};\n\nexports.PageScroller = PageScroller;\n\n})();\n",
"title": "$:/core/modules/utils/dom/scroller.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/edition-info.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/edition-info.js\ntype: application/javascript\nmodule-type: utils-node\n\nInformation about the available editions\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar fs = require(\"fs\"),\n\tpath = require(\"path\");\n\nvar editionInfo;\n\nexports.getEditionInfo = function() {\n\tif(!editionInfo) {\n\t\t// Enumerate the edition paths\n\t\tvar editionPaths = $tw.getLibraryItemSearchPaths($tw.config.editionsPath,$tw.config.editionsEnvVar);\n\t\teditionInfo = {};\n\t\tfor(var editionIndex=0; editionIndex<editionPaths.length; editionIndex++) {\n\t\t\tvar editionPath = editionPaths[editionIndex];\n\t\t\t// Enumerate the folders\n\t\t\tvar entries = fs.readdirSync(editionPath);\n\t\t\tfor(var entryIndex=0; entryIndex<entries.length; entryIndex++) {\n\t\t\t\tvar entry = entries[entryIndex];\n\t\t\t\t// Check if directories have a valid tiddlywiki.info\n\t\t\t\tif(!editionInfo[entry] && $tw.utils.isDirectory(path.resolve(editionPath,entry))) {\n\t\t\t\t\tvar info;\n\t\t\t\t\ttry {\n\t\t\t\t\t\tinfo = JSON.parse(fs.readFileSync(path.resolve(editionPath,entry,\"tiddlywiki.info\"),\"utf8\"));\n\t\t\t\t\t} catch(ex) {\n\t\t\t\t\t}\n\t\t\t\t\tif(info) {\n\t\t\t\t\t\teditionInfo[entry] = info;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\treturn editionInfo;\n};\n\n})();\n",
"title": "$:/core/modules/utils/edition-info.js",
"type": "application/javascript",
"module-type": "utils-node"
},
"$:/core/modules/utils/fakedom.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/fakedom.js\ntype: application/javascript\nmodule-type: global\n\nA barebones implementation of DOM interfaces needed by the rendering mechanism.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Sequence number used to enable us to track objects for testing\nvar sequenceNumber = null;\n\nvar bumpSequenceNumber = function(object) {\n\tif(sequenceNumber !== null) {\n\t\tobject.sequenceNumber = sequenceNumber++;\n\t}\n};\n\nvar TW_TextNode = function(text) {\n\tbumpSequenceNumber(this);\n\tthis.textContent = text + \"\";\n};\n\nObject.defineProperty(TW_TextNode.prototype, \"nodeType\", {\n\tget: function() {\n\t\treturn 3;\n\t}\n});\n\nObject.defineProperty(TW_TextNode.prototype, \"formattedTextContent\", {\n\tget: function() {\n\t\treturn this.textContent.replace(/(\\r?\\n)/g,\"\");\n\t}\n});\n\nvar TW_Element = function(tag,namespace) {\n\tbumpSequenceNumber(this);\n\tthis.isTiddlyWikiFakeDom = true;\n\tthis.tag = tag;\n\tthis.attributes = {};\n\tthis.isRaw = false;\n\tthis.children = [];\n\tthis.style = {};\n\tthis.namespaceURI = namespace || \"http://www.w3.org/1999/xhtml\";\n};\n\nObject.defineProperty(TW_Element.prototype, \"nodeType\", {\n\tget: function() {\n\t\treturn 1;\n\t}\n});\n\nTW_Element.prototype.getAttribute = function(name) {\n\tif(this.isRaw) {\n\t\tthrow \"Cannot getAttribute on a raw TW_Element\";\n\t}\n\treturn this.attributes[name];\n};\n\nTW_Element.prototype.setAttribute = function(name,value) {\n\tif(this.isRaw) {\n\t\tthrow \"Cannot setAttribute on a raw TW_Element\";\n\t}\n\tthis.attributes[name] = value + \"\";\n};\n\nTW_Element.prototype.setAttributeNS = function(namespace,name,value) {\n\tthis.setAttribute(name,value);\n};\n\nTW_Element.prototype.removeAttribute = function(name) {\n\tif(this.isRaw) {\n\t\tthrow \"Cannot removeAttribute on a raw TW_Element\";\n\t}\n\tif($tw.utils.hop(this.attributes,name)) {\n\t\tdelete this.attributes[name];\n\t}\n};\n\nTW_Element.prototype.appendChild = function(node) {\n\tthis.children.push(node);\n\tnode.parentNode = this;\n};\n\nTW_Element.prototype.insertBefore = function(node,nextSibling) {\n\tif(nextSibling) {\n\t\tvar p = this.children.indexOf(nextSibling);\n\t\tif(p !== -1) {\n\t\t\tthis.children.splice(p,0,node);\n\t\t\tnode.parentNode = this;\n\t\t} else {\n\t\t\tthis.appendChild(node);\n\t\t}\n\t} else {\n\t\tthis.appendChild(node);\n\t}\n};\n\nTW_Element.prototype.removeChild = function(node) {\n\tvar p = this.children.indexOf(node);\n\tif(p !== -1) {\n\t\tthis.children.splice(p,1);\n\t}\n};\n\nTW_Element.prototype.hasChildNodes = function() {\n\treturn !!this.children.length;\n};\n\nObject.defineProperty(TW_Element.prototype, \"childNodes\", {\n\tget: function() {\n\t\treturn this.children;\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"firstChild\", {\n\tget: function() {\n\t\treturn this.children[0];\n\t}\n});\n\nTW_Element.prototype.addEventListener = function(type,listener,useCapture) {\n\t// Do nothing\n};\n\nObject.defineProperty(TW_Element.prototype, \"tagName\", {\n\tget: function() {\n\t\treturn this.tag || \"\";\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"className\", {\n\tget: function() {\n\t\treturn this.attributes[\"class\"] || \"\";\n\t},\n\tset: function(value) {\n\t\tthis.attributes[\"class\"] = value + \"\";\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"value\", {\n\tget: function() {\n\t\treturn this.attributes.value || \"\";\n\t},\n\tset: function(value) {\n\t\tthis.attributes.value = value + \"\";\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"outerHTML\", {\n\tget: function() {\n\t\tvar output = [],attr,a,v;\n\t\toutput.push(\"<\",this.tag);\n\t\tif(this.attributes) {\n\t\t\tattr = [];\n\t\t\tfor(a in this.attributes) {\n\t\t\t\tattr.push(a);\n\t\t\t}\n\t\t\tattr.sort();\n\t\t\tfor(a=0; a<attr.length; a++) {\n\t\t\t\tv = this.attributes[attr[a]];\n\t\t\t\tif(v !== undefined) {\n\t\t\t\t\toutput.push(\" \",attr[a],\"=\\\"\",$tw.utils.htmlEncode(v),\"\\\"\");\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif(this.style) {\n\t\t\tvar style = [];\n\t\t\tfor(var s in this.style) {\n\t\t\t\tstyle.push(s + \":\" + this.style[s] + \";\");\n\t\t\t}\n\t\t\tif(style.length > 0) {\n\t\t\t\toutput.push(\" style=\\\"\",style.join(\"\"),\"\\\"\")\n\t\t\t}\n\t\t}\n\t\toutput.push(\">\");\n\t\tif($tw.config.htmlVoidElements.indexOf(this.tag) === -1) {\n\t\t\toutput.push(this.innerHTML);\n\t\t\toutput.push(\"</\",this.tag,\">\");\n\t\t}\n\t\treturn output.join(\"\");\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"innerHTML\", {\n\tget: function() {\n\t\tif(this.isRaw) {\n\t\t\treturn this.rawHTML;\n\t\t} else {\n\t\t\tvar b = [];\n\t\t\t$tw.utils.each(this.children,function(node) {\n\t\t\t\tif(node instanceof TW_Element) {\n\t\t\t\t\tb.push(node.outerHTML);\n\t\t\t\t} else if(node instanceof TW_TextNode) {\n\t\t\t\t\tb.push($tw.utils.htmlEncode(node.textContent));\n\t\t\t\t}\n\t\t\t});\n\t\t\treturn b.join(\"\");\n\t\t}\n\t},\n\tset: function(value) {\n\t\tthis.isRaw = true;\n\t\tthis.rawHTML = value;\n\t\tthis.rawTextContent = null;\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"textInnerHTML\", {\n\tset: function(value) {\n\t\tif(this.isRaw) {\n\t\t\tthis.rawTextContent = value;\n\t\t} else {\n\t\t\tthrow \"Cannot set textInnerHTML of a non-raw TW_Element\";\n\t\t}\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"textContent\", {\n\tget: function() {\n\t\tif(this.isRaw) {\n\t\t\tif(this.rawTextContent === null) {\n\t\t\t\tconsole.log(booboo)\n\t\t\t\tthrow \"Cannot get textContent on a raw TW_Element\";\t\t\t\t\n\t\t\t} else {\n\t\t\t\treturn this.rawTextContent;\n\t\t\t}\n\t\t} else {\n\t\t\tvar b = [];\n\t\t\t$tw.utils.each(this.children,function(node) {\n\t\t\t\tb.push(node.textContent);\n\t\t\t});\n\t\t\treturn b.join(\"\");\n\t\t}\n\t},\n\tset: function(value) {\n\t\tthis.children = [new TW_TextNode(value)];\n\t}\n});\n\nObject.defineProperty(TW_Element.prototype, \"formattedTextContent\", {\n\tget: function() {\n\t\tif(this.isRaw) {\n\t\t\tthrow \"Cannot get formattedTextContent on a raw TW_Element\";\n\t\t} else {\n\t\t\tvar b = [],\n\t\t\t\tisBlock = $tw.config.htmlBlockElements.indexOf(this.tag) !== -1;\n\t\t\tif(isBlock) {\n\t\t\t\tb.push(\"\\n\");\n\t\t\t}\n\t\t\tif(this.tag === \"li\") {\n\t\t\t\tb.push(\"* \");\n\t\t\t}\n\t\t\t$tw.utils.each(this.children,function(node) {\n\t\t\t\tb.push(node.formattedTextContent);\n\t\t\t});\n\t\t\tif(isBlock) {\n\t\t\t\tb.push(\"\\n\");\n\t\t\t}\n\t\t\treturn b.join(\"\");\n\t\t}\n\t}\n});\n\nvar document = {\n\tsetSequenceNumber: function(value) {\n\t\tsequenceNumber = value;\n\t},\n\tcreateElementNS: function(namespace,tag) {\n\t\treturn new TW_Element(tag,namespace);\n\t},\n\tcreateElement: function(tag) {\n\t\treturn new TW_Element(tag);\n\t},\n\tcreateTextNode: function(text) {\n\t\treturn new TW_TextNode(text);\n\t},\n\tcompatMode: \"CSS1Compat\", // For KaTeX to know that we're not a browser in quirks mode\n\tisTiddlyWikiFakeDom: true\n};\n\nexports.fakeDocument = document;\n\n})();\n",
"title": "$:/core/modules/utils/fakedom.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/utils/filesystem.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/filesystem.js\ntype: application/javascript\nmodule-type: utils-node\n\nFile system utilities\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar fs = require(\"fs\"),\n\tpath = require(\"path\");\n\n/*\nRecursively (and synchronously) copy a directory and all its content\n*/\nexports.copyDirectory = function(srcPath,dstPath) {\n\t// Remove any trailing path separators\n\tsrcPath = $tw.utils.removeTrailingSeparator(srcPath);\n\tdstPath = $tw.utils.removeTrailingSeparator(dstPath);\n\t// Create the destination directory\n\tvar err = $tw.utils.createDirectory(dstPath);\n\tif(err) {\n\t\treturn err;\n\t}\n\t// Function to copy a folder full of files\n\tvar copy = function(srcPath,dstPath) {\n\t\tvar srcStats = fs.lstatSync(srcPath),\n\t\t\tdstExists = fs.existsSync(dstPath);\n\t\tif(srcStats.isFile()) {\n\t\t\t$tw.utils.copyFile(srcPath,dstPath);\n\t\t} else if(srcStats.isDirectory()) {\n\t\t\tvar items = fs.readdirSync(srcPath);\n\t\t\tfor(var t=0; t<items.length; t++) {\n\t\t\t\tvar item = items[t],\n\t\t\t\t\terr = copy(srcPath + path.sep + item,dstPath + path.sep + item);\n\t\t\t\tif(err) {\n\t\t\t\t\treturn err;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t};\n\tcopy(srcPath,dstPath);\n\treturn null;\n};\n\n/*\nCopy a file\n*/\nvar FILE_BUFFER_LENGTH = 64 * 1024,\n\tfileBuffer;\n\nexports.copyFile = function(srcPath,dstPath) {\n\t// Create buffer if required\n\tif(!fileBuffer) {\n\t\tfileBuffer = new Buffer(FILE_BUFFER_LENGTH);\n\t}\n\t// Create any directories in the destination\n\t$tw.utils.createDirectory(path.dirname(dstPath));\n\t// Copy the file\n\tvar srcFile = fs.openSync(srcPath,\"r\"),\n\t\tdstFile = fs.openSync(dstPath,\"w\"),\n\t\tbytesRead = 1,\n\t\tpos = 0;\n\twhile (bytesRead > 0) {\n\t\tbytesRead = fs.readSync(srcFile,fileBuffer,0,FILE_BUFFER_LENGTH,pos);\n\t\tfs.writeSync(dstFile,fileBuffer,0,bytesRead);\n\t\tpos += bytesRead;\n\t}\n\tfs.closeSync(srcFile);\n\tfs.closeSync(dstFile);\n\treturn null;\n};\n\n/*\nRemove trailing path separator\n*/\nexports.removeTrailingSeparator = function(dirPath) {\n\tvar len = dirPath.length;\n\tif(dirPath.charAt(len-1) === path.sep) {\n\t\tdirPath = dirPath.substr(0,len-1);\n\t}\n\treturn dirPath;\n};\n\n/*\nRecursively create a directory\n*/\nexports.createDirectory = function(dirPath) {\n\tif(dirPath.substr(dirPath.length-1,1) !== path.sep) {\n\t\tdirPath = dirPath + path.sep;\n\t}\n\tvar pos = 1;\n\tpos = dirPath.indexOf(path.sep,pos);\n\twhile(pos !== -1) {\n\t\tvar subDirPath = dirPath.substr(0,pos);\n\t\tif(!$tw.utils.isDirectory(subDirPath)) {\n\t\t\ttry {\n\t\t\t\tfs.mkdirSync(subDirPath);\n\t\t\t} catch(e) {\n\t\t\t\treturn \"Error creating directory '\" + subDirPath + \"'\";\n\t\t\t}\n\t\t}\n\t\tpos = dirPath.indexOf(path.sep,pos + 1);\n\t}\n\treturn null;\n};\n\n/*\nRecursively create directories needed to contain a specified file\n*/\nexports.createFileDirectories = function(filePath) {\n\treturn $tw.utils.createDirectory(path.dirname(filePath));\n};\n\n/*\nRecursively delete a directory\n*/\nexports.deleteDirectory = function(dirPath) {\n\tif(fs.existsSync(dirPath)) {\n\t\tvar entries = fs.readdirSync(dirPath);\n\t\tfor(var entryIndex=0; entryIndex<entries.length; entryIndex++) {\n\t\t\tvar currPath = dirPath + path.sep + entries[entryIndex];\n\t\t\tif(fs.lstatSync(currPath).isDirectory()) {\n\t\t\t\t$tw.utils.deleteDirectory(currPath);\n\t\t\t} else {\n\t\t\t\tfs.unlinkSync(currPath);\n\t\t\t}\n\t\t}\n\tfs.rmdirSync(dirPath);\n\t}\n\treturn null;\n};\n\n/*\nCheck if a path identifies a directory\n*/\nexports.isDirectory = function(dirPath) {\n\treturn fs.existsSync(dirPath) && fs.statSync(dirPath).isDirectory();\n};\n\n/*\nCheck if a path identifies a directory that is empty\n*/\nexports.isDirectoryEmpty = function(dirPath) {\n\tif(!$tw.utils.isDirectory(dirPath)) {\n\t\treturn false;\n\t}\n\tvar files = fs.readdirSync(dirPath),\n\t\tempty = true;\n\t$tw.utils.each(files,function(file,index) {\n\t\tif(file.charAt(0) !== \".\") {\n\t\t\tempty = false;\n\t\t}\n\t});\n\treturn empty;\n};\n\n/*\nRecursively delete a tree of empty directories\n*/\nexports.deleteEmptyDirs = function(dirpath,callback) {\n\tvar self = this;\n\tfs.readdir(dirpath,function(err,files) {\n\t\tif(err) {\n\t\t\treturn callback(err);\n\t\t}\n\t\tif(files.length > 0) {\n\t\t\treturn callback(null);\n\t\t}\n\t\tfs.rmdir(dirpath,function(err) {\n\t\t\tif(err) {\n\t\t\t\treturn callback(err);\n\t\t\t}\n\t\t\tself.deleteEmptyDirs(path.dirname(dirpath),callback);\n\t\t});\n\t});\n};\n\n})();\n",
"title": "$:/core/modules/utils/filesystem.js",
"type": "application/javascript",
"module-type": "utils-node"
},
"$:/core/modules/utils/logger.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/logger.js\ntype: application/javascript\nmodule-type: utils\n\nA basic logging implementation\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar ALERT_TAG = \"$:/tags/Alert\";\n\n/*\nMake a new logger\n*/\nfunction Logger(componentName) {\n\tthis.componentName = componentName || \"\";\n}\n\n/*\nLog a message\n*/\nLogger.prototype.log = function(/* args */) {\n\tif(console !== undefined && console.log !== undefined) {\n\t\treturn Function.apply.call(console.log, console, [this.componentName + \":\"].concat(Array.prototype.slice.call(arguments,0)));\n\t}\n};\n\n/*\nAlert a message\n*/\nLogger.prototype.alert = function(/* args */) {\n\t// Prepare the text of the alert\n\tvar text = Array.prototype.join.call(arguments,\" \");\n\t// Create alert tiddlers in the browser\n\tif($tw.browser) {\n\t\t// Check if there is an existing alert with the same text and the same component\n\t\tvar existingAlerts = $tw.wiki.getTiddlersWithTag(ALERT_TAG),\n\t\t\talertFields,\n\t\t\texistingCount,\n\t\t\tself = this;\n\t\t$tw.utils.each(existingAlerts,function(title) {\n\t\t\tvar tiddler = $tw.wiki.getTiddler(title);\n\t\t\tif(tiddler.fields.text === text && tiddler.fields.component === self.componentName && tiddler.fields.modified && (!alertFields || tiddler.fields.modified < alertFields.modified)) {\n\t\t\t\t\talertFields = $tw.utils.extend({},tiddler.fields);\n\t\t\t}\n\t\t});\n\t\tif(alertFields) {\n\t\t\texistingCount = alertFields.count || 1;\n\t\t} else {\n\t\t\talertFields = {\n\t\t\t\ttitle: $tw.wiki.generateNewTitle(\"$:/temp/alerts/alert\",{prefix: \"\"}),\n\t\t\t\ttext: text,\n\t\t\t\ttags: [ALERT_TAG],\n\t\t\t\tcomponent: this.componentName\n\t\t\t};\n\t\t\texistingCount = 0;\n\t\t}\n\t\talertFields.modified = new Date();\n\t\tif(++existingCount > 1) {\n\t\t\talertFields.count = existingCount;\n\t\t} else {\n\t\t\talertFields.count = undefined;\n\t\t}\n\t\t$tw.wiki.addTiddler(new $tw.Tiddler(alertFields));\n\t\t// Log the alert as well\n\t\tthis.log.apply(this,Array.prototype.slice.call(arguments,0));\n\t} else {\n\t\t// Print an orange message to the console if not in the browser\n\t\tconsole.error(\"\\x1b[1;33m\" + text + \"\\x1b[0m\");\n\t}\n};\n\nexports.Logger = Logger;\n\n})();\n",
"title": "$:/core/modules/utils/logger.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/parsetree.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/parsetree.js\ntype: application/javascript\nmodule-type: utils\n\nParse tree utility functions.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nexports.addAttributeToParseTreeNode = function(node,name,value) {\n\tnode.attributes = node.attributes || {};\n\tnode.attributes[name] = {type: \"string\", value: value};\n};\n\nexports.getAttributeValueFromParseTreeNode = function(node,name,defaultValue) {\n\tif(node.attributes && node.attributes[name] && node.attributes[name].value !== undefined) {\n\t\treturn node.attributes[name].value;\n\t}\n\treturn defaultValue;\n};\n\nexports.addClassToParseTreeNode = function(node,classString) {\n\tvar classes = [];\n\tnode.attributes = node.attributes || {};\n\tnode.attributes[\"class\"] = node.attributes[\"class\"] || {type: \"string\", value: \"\"};\n\tif(node.attributes[\"class\"].type === \"string\") {\n\t\tif(node.attributes[\"class\"].value !== \"\") {\n\t\t\tclasses = node.attributes[\"class\"].value.split(\" \");\n\t\t}\n\t\tif(classString !== \"\") {\n\t\t\t$tw.utils.pushTop(classes,classString.split(\" \"));\n\t\t}\n\t\tnode.attributes[\"class\"].value = classes.join(\" \");\n\t}\n};\n\nexports.addStyleToParseTreeNode = function(node,name,value) {\n\t\tnode.attributes = node.attributes || {};\n\t\tnode.attributes.style = node.attributes.style || {type: \"string\", value: \"\"};\n\t\tif(node.attributes.style.type === \"string\") {\n\t\t\tnode.attributes.style.value += name + \":\" + value + \";\";\n\t\t}\n};\n\nexports.findParseTreeNode = function(nodeArray,search) {\n\tfor(var t=0; t<nodeArray.length; t++) {\n\t\tif(nodeArray[t].type === search.type && nodeArray[t].tag === search.tag) {\n\t\t\treturn nodeArray[t];\n\t\t}\n\t}\n\treturn undefined;\n};\n\n/*\nHelper to get the text of a parse tree node or array of nodes\n*/\nexports.getParseTreeText = function getParseTreeText(tree) {\n\tvar output = [];\n\tif($tw.utils.isArray(tree)) {\n\t\t$tw.utils.each(tree,function(node) {\n\t\t\toutput.push(getParseTreeText(node));\n\t\t});\n\t} else {\n\t\tif(tree.type === \"text\") {\n\t\t\toutput.push(tree.text);\n\t\t}\n\t\tif(tree.children) {\n\t\t\treturn getParseTreeText(tree.children);\n\t\t}\n\t}\n\treturn output.join(\"\");\n};\n\n})();\n",
"title": "$:/core/modules/utils/parsetree.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/performance.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/performance.js\ntype: application/javascript\nmodule-type: global\n\nPerformance measurement.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nfunction Performance(enabled) {\n\tthis.enabled = !!enabled;\n\tthis.measures = {}; // Hashmap of current values of measurements\n\tthis.logger = new $tw.utils.Logger(\"performance\");\n}\n\n/*\nWrap performance reporting around a top level function\n*/\nPerformance.prototype.report = function(name,fn) {\n\tvar self = this;\n\tif(this.enabled) {\n\t\treturn function() {\n\t\t\tself.measures = {};\n\t\t\tvar startTime = $tw.utils.timer(),\n\t\t\t\tresult = fn.apply(this,arguments);\n\t\t\tself.logger.log(name + \": \" + $tw.utils.timer(startTime).toFixed(2) + \"ms\");\n\t\t\tfor(var m in self.measures) {\n\t\t\t\tself.logger.log(\"+\" + m + \": \" + self.measures[m].toFixed(2) + \"ms\");\n\t\t\t}\n\t\t\treturn result;\n\t\t};\n\t} else {\n\t\treturn fn;\n\t}\n};\n\n/*\nWrap performance measurements around a subfunction\n*/\nPerformance.prototype.measure = function(name,fn) {\n\tvar self = this;\n\tif(this.enabled) {\n\t\treturn function() {\n\t\t\tvar startTime = $tw.utils.timer(),\n\t\t\t\tresult = fn.apply(this,arguments),\n\t\t\t\tvalue = self.measures[name] || 0;\n\t\t\tself.measures[name] = value + $tw.utils.timer(startTime);\n\t\t\treturn result;\n\t\t};\n\t} else {\n\t\treturn fn;\n\t}\n};\n\nexports.Performance = Performance;\n\n})();\n",
"title": "$:/core/modules/utils/performance.js",
"type": "application/javascript",
"module-type": "global"
},
"$:/core/modules/utils/pluginmaker.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/pluginmaker.js\ntype: application/javascript\nmodule-type: utils\n\nA quick and dirty way to pack up plugins within the browser.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nRepack a plugin, and then delete any non-shadow payload tiddlers\n*/\nexports.repackPlugin = function(title,additionalTiddlers,excludeTiddlers) {\n\tadditionalTiddlers = additionalTiddlers || [];\n\texcludeTiddlers = excludeTiddlers || [];\n\t// Get the plugin tiddler\n\tvar pluginTiddler = $tw.wiki.getTiddler(title);\n\tif(!pluginTiddler) {\n\t\tthrow \"No such tiddler as \" + title;\n\t}\n\t// Extract the JSON\n\tvar jsonPluginTiddler;\n\ttry {\n\t\tjsonPluginTiddler = JSON.parse(pluginTiddler.fields.text);\n\t} catch(e) {\n\t\tthrow \"Cannot parse plugin tiddler \" + title + \"\\n\" + $tw.language.getString(\"Error/Caption\") + \": \" + e;\n\t}\n\t// Get the list of tiddlers\n\tvar tiddlers = Object.keys(jsonPluginTiddler.tiddlers);\n\t// Add the additional tiddlers\n\t$tw.utils.pushTop(tiddlers,additionalTiddlers);\n\t// Remove any excluded tiddlers\n\tfor(var t=tiddlers.length-1; t>=0; t--) {\n\t\tif(excludeTiddlers.indexOf(tiddlers[t]) !== -1) {\n\t\t\ttiddlers.splice(t,1);\n\t\t}\n\t}\n\t// Pack up the tiddlers into a block of JSON\n\tvar plugins = {};\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = $tw.wiki.getTiddler(title),\n\t\t\tfields = {};\n\t\t$tw.utils.each(tiddler.fields,function (value,name) {\n\t\t\tfields[name] = tiddler.getFieldString(name);\n\t\t});\n\t\tplugins[title] = fields;\n\t});\n\t// Retrieve and bump the version number\n\tvar pluginVersion = $tw.utils.parseVersion(pluginTiddler.getFieldString(\"version\") || \"0.0.0\") || {\n\t\t\tmajor: \"0\",\n\t\t\tminor: \"0\",\n\t\t\tpatch: \"0\"\n\t\t};\n\tpluginVersion.patch++;\n\tvar version = pluginVersion.major + \".\" + pluginVersion.minor + \".\" + pluginVersion.patch;\n\tif(pluginVersion.prerelease) {\n\t\tversion += \"-\" + pluginVersion.prerelease;\n\t}\n\tif(pluginVersion.build) {\n\t\tversion += \"+\" + pluginVersion.build;\n\t}\n\t// Save the tiddler\n\t$tw.wiki.addTiddler(new $tw.Tiddler(pluginTiddler,{text: JSON.stringify({tiddlers: plugins},null,4), version: version}));\n\t// Delete any non-shadow constituent tiddlers\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tif($tw.wiki.tiddlerExists(title)) {\n\t\t\t$tw.wiki.deleteTiddler(title);\n\t\t}\n\t});\n\t// Trigger an autosave\n\t$tw.rootWidget.dispatchEvent({type: \"tm-auto-save-wiki\"});\n\t// Return a heartwarming confirmation\n\treturn \"Plugin \" + title + \" successfully saved\";\n};\n\n})();\n",
"title": "$:/core/modules/utils/pluginmaker.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/utils/utils.js": {
"text": "/*\\\ntitle: $:/core/modules/utils/utils.js\ntype: application/javascript\nmodule-type: utils\n\nVarious static utility functions.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nDisplay a warning, in colour if we're on a terminal\n*/\nexports.warning = function(text) {\n\tconsole.log($tw.node ? \"\\x1b[1;33m\" + text + \"\\x1b[0m\" : text);\n};\n\n/*\nRepeatedly replaces a substring within a string. Like String.prototype.replace, but without any of the default special handling of $ sequences in the replace string\n*/\nexports.replaceString = function(text,search,replace) {\n\treturn text.replace(search,function() {\n\t\treturn replace;\n\t});\n};\n\n/*\nRepeats a string\n*/\nexports.repeat = function(str,count) {\n\tvar result = \"\";\n\tfor(var t=0;t<count;t++) {\n\t\tresult += str;\n\t}\n\treturn result;\n};\n\n/*\nTrim whitespace from the start and end of a string\nThanks to Steven Levithan, http://blog.stevenlevithan.com/archives/faster-trim-javascript\n*/\nexports.trim = function(str) {\n\tif(typeof str === \"string\") {\n\t\treturn str.replace(/^\\s\\s*/, '').replace(/\\s\\s*$/, '');\n\t} else {\n\t\treturn str;\n\t}\n};\n\n/*\nFind the line break preceding a given position in a string\nReturns position immediately after that line break, or the start of the string\n*/\nexports.findPrecedingLineBreak = function(text,pos) {\n\tvar result = text.lastIndexOf(\"\\n\",pos - 1);\n\tif(result === -1) {\n\t\tresult = 0;\n\t} else {\n\t\tresult++;\n\t\tif(text.charAt(result) === \"\\r\") {\n\t\t\tresult++;\n\t\t}\n\t}\n\treturn result;\n};\n\n/*\nFind the line break following a given position in a string\n*/\nexports.findFollowingLineBreak = function(text,pos) {\n\t// Cut to just past the following line break, or to the end of the text\n\tvar result = text.indexOf(\"\\n\",pos);\n\tif(result === -1) {\n\t\tresult = text.length;\n\t} else {\n\t\tif(text.charAt(result) === \"\\r\") {\n\t\t\tresult++;\n\t\t}\n\t}\n\treturn result;\n};\n\n/*\nReturn the number of keys in an object\n*/\nexports.count = function(object) {\n\treturn Object.keys(object || {}).length;\n};\n\n/*\nCheck if an array is equal by value and by reference.\n*/\nexports.isArrayEqual = function(array1,array2) {\n\tif(array1 === array2) {\n\t\treturn true;\n\t}\n\tarray1 = array1 || [];\n\tarray2 = array2 || [];\n\tif(array1.length !== array2.length) {\n\t\treturn false;\n\t}\n\treturn array1.every(function(value,index) {\n\t\treturn value === array2[index];\n\t});\n};\n\n/*\nPush entries onto an array, removing them first if they already exist in the array\n\tarray: array to modify (assumed to be free of duplicates)\n\tvalue: a single value to push or an array of values to push\n*/\nexports.pushTop = function(array,value) {\n\tvar t,p;\n\tif($tw.utils.isArray(value)) {\n\t\t// Remove any array entries that are duplicated in the new values\n\t\tif(value.length !== 0) {\n\t\t\tif(array.length !== 0) {\n\t\t\t\tif(value.length < array.length) {\n\t\t\t\t\tfor(t=0; t<value.length; t++) {\n\t\t\t\t\t\tp = array.indexOf(value[t]);\n\t\t\t\t\t\tif(p !== -1) {\n\t\t\t\t\t\t\tarray.splice(p,1);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t} else {\n\t\t\t\t\tfor(t=array.length-1; t>=0; t--) {\n\t\t\t\t\t\tp = value.indexOf(array[t]);\n\t\t\t\t\t\tif(p !== -1) {\n\t\t\t\t\t\t\tarray.splice(t,1);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\t// Push the values on top of the main array\n\t\t\tarray.push.apply(array,value);\n\t\t}\n\t} else {\n\t\tp = array.indexOf(value);\n\t\tif(p !== -1) {\n\t\t\tarray.splice(p,1);\n\t\t}\n\t\tarray.push(value);\n\t}\n\treturn array;\n};\n\n/*\nRemove entries from an array\n\tarray: array to modify\n\tvalue: a single value to remove, or an array of values to remove\n*/\nexports.removeArrayEntries = function(array,value) {\n\tvar t,p;\n\tif($tw.utils.isArray(value)) {\n\t\tfor(t=0; t<value.length; t++) {\n\t\t\tp = array.indexOf(value[t]);\n\t\t\tif(p !== -1) {\n\t\t\t\tarray.splice(p,1);\n\t\t\t}\n\t\t}\n\t} else {\n\t\tp = array.indexOf(value);\n\t\tif(p !== -1) {\n\t\t\tarray.splice(p,1);\n\t\t}\n\t}\n};\n\n/*\nCheck whether any members of a hashmap are present in another hashmap\n*/\nexports.checkDependencies = function(dependencies,changes) {\n\tvar hit = false;\n\t$tw.utils.each(changes,function(change,title) {\n\t\tif($tw.utils.hop(dependencies,title)) {\n\t\t\thit = true;\n\t\t}\n\t});\n\treturn hit;\n};\n\nexports.extend = function(object /* [, src] */) {\n\t$tw.utils.each(Array.prototype.slice.call(arguments, 1), function(source) {\n\t\tif(source) {\n\t\t\tfor(var property in source) {\n\t\t\t\tobject[property] = source[property];\n\t\t\t}\n\t\t}\n\t});\n\treturn object;\n};\n\nexports.deepCopy = function(object) {\n\tvar result,t;\n\tif($tw.utils.isArray(object)) {\n\t\t// Copy arrays\n\t\tresult = object.slice(0);\n\t} else if(typeof object === \"object\") {\n\t\tresult = {};\n\t\tfor(t in object) {\n\t\t\tif(object[t] !== undefined) {\n\t\t\t\tresult[t] = $tw.utils.deepCopy(object[t]);\n\t\t\t}\n\t\t}\n\t} else {\n\t\tresult = object;\n\t}\n\treturn result;\n};\n\nexports.extendDeepCopy = function(object,extendedProperties) {\n\tvar result = $tw.utils.deepCopy(object),t;\n\tfor(t in extendedProperties) {\n\t\tif(extendedProperties[t] !== undefined) {\n\t\t\tresult[t] = $tw.utils.deepCopy(extendedProperties[t]);\n\t\t}\n\t}\n\treturn result;\n};\n\nexports.deepFreeze = function deepFreeze(object) {\n\tvar property, key;\n\tif(object) {\n\t\tObject.freeze(object);\n\t\tfor(key in object) {\n\t\t\tproperty = object[key];\n\t\t\tif($tw.utils.hop(object,key) && (typeof property === \"object\") && !Object.isFrozen(property)) {\n\t\t\t\tdeepFreeze(property);\n\t\t\t}\n\t\t}\n\t}\n};\n\nexports.slowInSlowOut = function(t) {\n\treturn (1 - ((Math.cos(t * Math.PI) + 1) / 2));\n};\n\nexports.formatDateString = function(date,template) {\n\tvar result = \"\",\n\t\tt = template,\n\t\tmatches = [\n\t\t\t[/^0hh12/, function() {\n\t\t\t\treturn $tw.utils.pad($tw.utils.getHours12(date));\n\t\t\t}],\n\t\t\t[/^wYYYY/, function() {\n\t\t\t\treturn $tw.utils.getYearForWeekNo(date);\n\t\t\t}],\n\t\t\t[/^hh12/, function() {\n\t\t\t\treturn $tw.utils.getHours12(date);\n\t\t\t}],\n\t\t\t[/^DDth/, function() {\n\t\t\t\treturn date.getDate() + $tw.utils.getDaySuffix(date);\n\t\t\t}],\n\t\t\t[/^YYYY/, function() {\n\t\t\t\treturn date.getFullYear();\n\t\t\t}],\n\t\t\t[/^0hh/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getHours());\n\t\t\t}],\n\t\t\t[/^0mm/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getMinutes());\n\t\t\t}],\n\t\t\t[/^0ss/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getSeconds());\n\t\t\t}],\n\t\t\t[/^0DD/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getDate());\n\t\t\t}],\n\t\t\t[/^0MM/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getMonth()+1);\n\t\t\t}],\n\t\t\t[/^0WW/, function() {\n\t\t\t\treturn $tw.utils.pad($tw.utils.getWeek(date));\n\t\t\t}],\n\t\t\t[/^ddd/, function() {\n\t\t\t\treturn $tw.language.getString(\"Date/Short/Day/\" + date.getDay());\n\t\t\t}],\n\t\t\t[/^mmm/, function() {\n\t\t\t\treturn $tw.language.getString(\"Date/Short/Month/\" + (date.getMonth() + 1));\n\t\t\t}],\n\t\t\t[/^DDD/, function() {\n\t\t\t\treturn $tw.language.getString(\"Date/Long/Day/\" + date.getDay());\n\t\t\t}],\n\t\t\t[/^MMM/, function() {\n\t\t\t\treturn $tw.language.getString(\"Date/Long/Month/\" + (date.getMonth() + 1));\n\t\t\t}],\n\t\t\t[/^TZD/, function() {\n\t\t\t\tvar tz = date.getTimezoneOffset(),\n\t\t\t\tatz = Math.abs(tz);\n\t\t\t\treturn (tz < 0 ? '+' : '-') + $tw.utils.pad(Math.floor(atz / 60)) + ':' + $tw.utils.pad(atz % 60);\n\t\t\t}],\n\t\t\t[/^wYY/, function() {\n\t\t\t\treturn $tw.utils.pad($tw.utils.getYearForWeekNo(date) - 2000);\n\t\t\t}],\n\t\t\t[/^[ap]m/, function() {\n\t\t\t\treturn $tw.utils.getAmPm(date).toLowerCase();\n\t\t\t}],\n\t\t\t[/^hh/, function() {\n\t\t\t\treturn date.getHours();\n\t\t\t}],\n\t\t\t[/^mm/, function() {\n\t\t\t\treturn date.getMinutes();\n\t\t\t}],\n\t\t\t[/^ss/, function() {\n\t\t\t\treturn date.getSeconds();\n\t\t\t}],\n\t\t\t[/^[AP]M/, function() {\n\t\t\t\treturn $tw.utils.getAmPm(date).toUpperCase();\n\t\t\t}],\n\t\t\t[/^DD/, function() {\n\t\t\t\treturn date.getDate();\n\t\t\t}],\n\t\t\t[/^MM/, function() {\n\t\t\t\treturn date.getMonth() + 1;\n\t\t\t}],\n\t\t\t[/^WW/, function() {\n\t\t\t\treturn $tw.utils.getWeek(date);\n\t\t\t}],\n\t\t\t[/^YY/, function() {\n\t\t\t\treturn $tw.utils.pad(date.getFullYear() - 2000);\n\t\t\t}]\n\t\t];\n\twhile(t.length){\n\t\tvar matchString = \"\";\n\t\t$tw.utils.each(matches, function(m) {\n\t\t\tvar match = m[0].exec(t);\n\t\t\tif(match) {\n\t\t\t\tmatchString = m[1].call();\n\t\t\t\tt = t.substr(match[0].length);\n\t\t\t\treturn false;\n\t\t\t}\n\t\t});\n\t\tif(matchString) {\n\t\t\tresult += matchString;\n\t\t} else {\n\t\t\tresult += t.charAt(0);\n\t\t\tt = t.substr(1);\n\t\t}\n\t}\n\tresult = result.replace(/\\\\(.)/g,\"$1\");\n\treturn result;\n};\n\nexports.getAmPm = function(date) {\n\treturn $tw.language.getString(\"Date/Period/\" + (date.getHours() >= 12 ? \"pm\" : \"am\"));\n};\n\nexports.getDaySuffix = function(date) {\n\treturn $tw.language.getString(\"Date/DaySuffix/\" + date.getDate());\n};\n\nexports.getWeek = function(date) {\n\tvar dt = new Date(date.getTime());\n\tvar d = dt.getDay();\n\tif(d === 0) {\n\t\td = 7; // JavaScript Sun=0, ISO Sun=7\n\t}\n\tdt.setTime(dt.getTime() + (4 - d) * 86400000);// shift day to Thurs of same week to calculate weekNo\n\tvar x = new Date(dt.getFullYear(),0,1);\n\tvar n = Math.floor((dt.getTime() - x.getTime()) / 86400000);\n\treturn Math.floor(n / 7) + 1;\n};\n\nexports.getYearForWeekNo = function(date) {\n\tvar dt = new Date(date.getTime());\n\tvar d = dt.getDay();\n\tif(d === 0) {\n\t\td = 7; // JavaScript Sun=0, ISO Sun=7\n\t}\n\tdt.setTime(dt.getTime() + (4 - d) * 86400000);// shift day to Thurs of same week\n\treturn dt.getFullYear();\n};\n\nexports.getHours12 = function(date) {\n\tvar h = date.getHours();\n\treturn h > 12 ? h-12 : ( h > 0 ? h : 12 );\n};\n\n/*\nConvert a date delta in milliseconds into a string representation of \"23 seconds ago\", \"27 minutes ago\" etc.\n\tdelta: delta in milliseconds\nReturns an object with these members:\n\tdescription: string describing the delta period\n\tupdatePeriod: time in millisecond until the string will be inaccurate\n*/\nexports.getRelativeDate = function(delta) {\n\tvar futurep = false;\n\tif(delta < 0) {\n\t\tdelta = -1 * delta;\n\t\tfuturep = true;\n\t}\n\tvar units = [\n\t\t{name: \"Years\", duration: 365 * 24 * 60 * 60 * 1000},\n\t\t{name: \"Months\", duration: (365/12) * 24 * 60 * 60 * 1000},\n\t\t{name: \"Days\", duration: 24 * 60 * 60 * 1000},\n\t\t{name: \"Hours\", duration: 60 * 60 * 1000},\n\t\t{name: \"Minutes\", duration: 60 * 1000},\n\t\t{name: \"Seconds\", duration: 1000}\n\t];\n\tfor(var t=0; t<units.length; t++) {\n\t\tvar result = Math.floor(delta / units[t].duration);\n\t\tif(result >= 2) {\n\t\t\treturn {\n\t\t\t\tdelta: delta,\n\t\t\t\tdescription: $tw.language.getString(\n\t\t\t\t\t\"RelativeDate/\" + (futurep ? \"Future\" : \"Past\") + \"/\" + units[t].name,\n\t\t\t\t\t{variables:\n\t\t\t\t\t\t{period: result.toString()}\n\t\t\t\t\t}\n\t\t\t\t),\n\t\t\t\tupdatePeriod: units[t].duration\n\t\t\t};\n\t\t}\n\t}\n\treturn {\n\t\tdelta: delta,\n\t\tdescription: $tw.language.getString(\n\t\t\t\"RelativeDate/\" + (futurep ? \"Future\" : \"Past\") + \"/Second\",\n\t\t\t{variables:\n\t\t\t\t{period: \"1\"}\n\t\t\t}\n\t\t),\n\t\tupdatePeriod: 1000\n\t};\n};\n\n// Convert & to \"&\", < to \"<\", > to \">\", \" to \""\"\nexports.htmlEncode = function(s) {\n\tif(s) {\n\t\treturn s.toString().replace(/&/mg,\"&\").replace(/</mg,\"<\").replace(/>/mg,\">\").replace(/\\\"/mg,\""\");\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\n// Converts all HTML entities to their character equivalents\nexports.entityDecode = function(s) {\n\tvar converter = String.fromCodePoint || String.fromCharCode,\n\t\te = s.substr(1,s.length-2); // Strip the & and the ;\n\tif(e.charAt(0) === \"#\") {\n\t\tif(e.charAt(1) === \"x\" || e.charAt(1) === \"X\") {\n\t\t\treturn converter(parseInt(e.substr(2),16));\t\n\t\t} else {\n\t\t\treturn converter(parseInt(e.substr(1),10));\n\t\t}\n\t} else {\n\t\tvar c = $tw.config.htmlEntities[e];\n\t\tif(c) {\n\t\t\treturn converter(c);\n\t\t} else {\n\t\t\treturn s; // Couldn't convert it as an entity, just return it raw\n\t\t}\n\t}\n};\n\nexports.unescapeLineBreaks = function(s) {\n\treturn s.replace(/\\\\n/mg,\"\\n\").replace(/\\\\b/mg,\" \").replace(/\\\\s/mg,\"\\\\\").replace(/\\r/mg,\"\");\n};\n\n/*\n * Returns an escape sequence for given character. Uses \\x for characters <=\n * 0xFF to save space, \\u for the rest.\n *\n * The code needs to be in sync with th code template in the compilation\n * function for \"action\" nodes.\n */\n// Copied from peg.js, thanks to David Majda\nexports.escape = function(ch) {\n\tvar charCode = ch.charCodeAt(0);\n\tif(charCode <= 0xFF) {\n\t\treturn '\\\\x' + $tw.utils.pad(charCode.toString(16).toUpperCase());\n\t} else {\n\t\treturn '\\\\u' + $tw.utils.pad(charCode.toString(16).toUpperCase(),4);\n\t}\n};\n\n// Turns a string into a legal JavaScript string\n// Copied from peg.js, thanks to David Majda\nexports.stringify = function(s) {\n\t/*\n\t* ECMA-262, 5th ed., 7.8.4: All characters may appear literally in a string\n\t* literal except for the closing quote character, backslash, carriage return,\n\t* line separator, paragraph separator, and line feed. Any character may\n\t* appear in the form of an escape sequence.\n\t*\n\t* For portability, we also escape all non-ASCII characters.\n\t*/\n\treturn (s || \"\")\n\t\t.replace(/\\\\/g, '\\\\\\\\') // backslash\n\t\t.replace(/\"/g, '\\\\\"') // double quote character\n\t\t.replace(/'/g, \"\\\\'\") // single quote character\n\t\t.replace(/\\r/g, '\\\\r') // carriage return\n\t\t.replace(/\\n/g, '\\\\n') // line feed\n\t\t.replace(/[\\x80-\\uFFFF]/g, exports.escape); // non-ASCII characters\n};\n\n/*\nEscape the RegExp special characters with a preceding backslash\n*/\nexports.escapeRegExp = function(s) {\n return s.replace(/[\\-\\/\\\\\\^\\$\\*\\+\\?\\.\\(\\)\\|\\[\\]\\{\\}]/g, '\\\\$&');\n};\n\n// Checks whether a link target is external, i.e. not a tiddler title\nexports.isLinkExternal = function(to) {\n\tvar externalRegExp = /^(?:file|http|https|mailto|ftp|irc|news|data|skype):[^\\s<>{}\\[\\]`|\"\\\\^]+(?:\\/|\\b)/i;\n\treturn externalRegExp.test(to);\n};\n\nexports.nextTick = function(fn) {\n/*global window: false */\n\tif(typeof process === \"undefined\") {\n\t\t// Apparently it would be faster to use postMessage - http://dbaron.org/log/20100309-faster-timeouts\n\t\twindow.setTimeout(fn,4);\n\t} else {\n\t\tprocess.nextTick(fn);\n\t}\n};\n\n/*\nConvert a hyphenated CSS property name into a camel case one\n*/\nexports.unHyphenateCss = function(propName) {\n\treturn propName.replace(/-([a-z])/gi, function(match0,match1) {\n\t\treturn match1.toUpperCase();\n\t});\n};\n\n/*\nConvert a camelcase CSS property name into a dashed one (\"backgroundColor\" --> \"background-color\")\n*/\nexports.hyphenateCss = function(propName) {\n\treturn propName.replace(/([A-Z])/g, function(match0,match1) {\n\t\treturn \"-\" + match1.toLowerCase();\n\t});\n};\n\n/*\nParse a text reference of one of these forms:\n* title\n* !!field\n* title!!field\n* title##index\n* etc\nReturns an object with the following fields, all optional:\n* title: tiddler title\n* field: tiddler field name\n* index: JSON property index\n*/\nexports.parseTextReference = function(textRef) {\n\t// Separate out the title, field name and/or JSON indices\n\tvar reTextRef = /(?:(.*?)!!(.+))|(?:(.*?)##(.+))|(.*)/mg,\n\t\tmatch = reTextRef.exec(textRef),\n\t\tresult = {};\n\tif(match && reTextRef.lastIndex === textRef.length) {\n\t\t// Return the parts\n\t\tif(match[1]) {\n\t\t\tresult.title = match[1];\n\t\t}\n\t\tif(match[2]) {\n\t\t\tresult.field = match[2];\n\t\t}\n\t\tif(match[3]) {\n\t\t\tresult.title = match[3];\n\t\t}\n\t\tif(match[4]) {\n\t\t\tresult.index = match[4];\n\t\t}\n\t\tif(match[5]) {\n\t\t\tresult.title = match[5];\n\t\t}\n\t} else {\n\t\t// If we couldn't parse it\n\t\tresult.title = textRef\n\t}\n\treturn result;\n};\n\n/*\nChecks whether a string is a valid fieldname\n*/\nexports.isValidFieldName = function(name) {\n\tif(!name || typeof name !== \"string\") {\n\t\treturn false;\n\t}\n\tname = name.toLowerCase().trim();\n\tvar fieldValidatorRegEx = /^[a-z0-9\\-\\._]+$/mg;\n\treturn fieldValidatorRegEx.test(name);\n};\n\n/*\nExtract the version number from the meta tag or from the boot file\n*/\n\n// Browser version\nexports.extractVersionInfo = function() {\n\tif($tw.packageInfo) {\n\t\treturn $tw.packageInfo.version;\n\t} else {\n\t\tvar metatags = document.getElementsByTagName(\"meta\");\n\t\tfor(var t=0; t<metatags.length; t++) {\n\t\t\tvar m = metatags[t];\n\t\t\tif(m.name === \"tiddlywiki-version\") {\n\t\t\t\treturn m.content;\n\t\t\t}\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nGet the animation duration in ms\n*/\nexports.getAnimationDuration = function() {\n\treturn parseInt($tw.wiki.getTiddlerText(\"$:/config/AnimationDuration\",\"400\"),10);\n};\n\n/*\nHash a string to a number\nDerived from http://stackoverflow.com/a/15710692\n*/\nexports.hashString = function(str) {\n\treturn str.split(\"\").reduce(function(a,b) {\n\t\ta = ((a << 5) - a) + b.charCodeAt(0);\n\t\treturn a & a;\n\t},0);\n};\n\n/*\nDecode a base64 string\n*/\nexports.base64Decode = function(string64) {\n\tif($tw.browser) {\n\t\t// TODO\n\t\tthrow \"$tw.utils.base64Decode() doesn't work in the browser\";\n\t} else {\n\t\treturn (new Buffer(string64,\"base64\")).toString();\n\t}\n};\n\n/*\nConvert a hashmap into a tiddler dictionary format sequence of name:value pairs\n*/\nexports.makeTiddlerDictionary = function(data) {\n\tvar output = [];\n\tfor(var name in data) {\n\t\toutput.push(name + \": \" + data[name]);\n\t}\n\treturn output.join(\"\\n\");\n};\n\n/*\nHigh resolution microsecond timer for profiling\n*/\nexports.timer = function(base) {\n\tvar m;\n\tif($tw.node) {\n\t\tvar r = process.hrtime();\t\t\n\t\tm = r[0] * 1e3 + (r[1] / 1e6);\n\t} else if(window.performance) {\n\t\tm = performance.now();\n\t} else {\n\t\tm = Date.now();\n\t}\n\tif(typeof base !== \"undefined\") {\n\t\tm = m - base;\n\t}\n\treturn m;\n};\n\n/*\nConvert text and content type to a data URI\n*/\nexports.makeDataUri = function(text,type) {\n\ttype = type || \"text/vnd.tiddlywiki\";\n\tvar typeInfo = $tw.config.contentTypeInfo[type] || $tw.config.contentTypeInfo[\"text/plain\"],\n\t\tisBase64 = typeInfo.encoding === \"base64\",\n\t\tparts = [];\n\tparts.push(\"data:\");\n\tparts.push(type);\n\tparts.push(isBase64 ? \";base64\" : \"\");\n\tparts.push(\",\");\n\tparts.push(isBase64 ? text : encodeURIComponent(text));\n\treturn parts.join(\"\");\n};\n\n/*\nUseful for finding out the fully escaped CSS selector equivalent to a given tag. For example:\n\n$tw.utils.tagToCssSelector(\"$:/tags/Stylesheet\") --> tc-tagged-\\%24\\%3A\\%2Ftags\\%2FStylesheet\n*/\nexports.tagToCssSelector = function(tagName) {\n\treturn \"tc-tagged-\" + encodeURIComponent(tagName).replace(/[!\"#$%&'()*+,\\-./:;<=>?@[\\\\\\]^`{\\|}~,]/mg,function(c) {\n\t\treturn \"\\\\\" + c;\n\t});\n};\n\n/*\nIE does not have sign function\n*/\nexports.sign = Math.sign || function(x) {\n\tx = +x; // convert to a number\n\tif (x === 0 || isNaN(x)) {\n\t\treturn x;\n\t}\n\treturn x > 0 ? 1 : -1;\n};\n\n/*\nIE does not have an endsWith function\n*/\nexports.strEndsWith = function(str,ending,position) {\n\tif(str.endsWith) {\n\t\treturn str.endsWith(ending,position);\n\t} else {\n\t\tif (typeof position !== 'number' || !isFinite(position) || Math.floor(position) !== position || position > str.length) {\n\t\t\tposition = str.length;\n\t\t}\n\t\tposition -= ending.length;\n\t\tvar lastIndex = str.indexOf(ending, position);\n\t\treturn lastIndex !== -1 && lastIndex === position;\n\t}\n};\n\n/*\nTransliterate string from eg. Cyrillic Russian to Latin\n*/\nvar transliterationPairs = {\n\t\"Ё\":\"YO\",\n\t\"Й\":\"I\",\n\t\"Ц\":\"TS\",\n\t\"У\":\"U\",\n\t\"К\":\"K\",\n\t\"Е\":\"E\",\n\t\"Н\":\"N\",\n\t\"Г\":\"G\",\n\t\"Ш\":\"SH\",\n\t\"Щ\":\"SCH\",\n\t\"З\":\"Z\",\n\t\"Х\":\"H\",\n\t\"Ъ\":\"'\",\n\t\"ё\":\"yo\",\n\t\"й\":\"i\",\n\t\"ц\":\"ts\",\n\t\"у\":\"u\",\n\t\"к\":\"k\",\n\t\"е\":\"e\",\n\t\"н\":\"n\",\n\t\"г\":\"g\",\n\t\"ш\":\"sh\",\n\t\"щ\":\"sch\",\n\t\"з\":\"z\",\n\t\"х\":\"h\",\n\t\"ъ\":\"'\",\n\t\"Ф\":\"F\",\n\t\"Ы\":\"I\",\n\t\"В\":\"V\",\n\t\"А\":\"a\",\n\t\"П\":\"P\",\n\t\"Р\":\"R\",\n\t\"О\":\"O\",\n\t\"Л\":\"L\",\n\t\"Д\":\"D\",\n\t\"Ж\":\"ZH\",\n\t\"Э\":\"E\",\n\t\"ф\":\"f\",\n\t\"ы\":\"i\",\n\t\"в\":\"v\",\n\t\"а\":\"a\",\n\t\"п\":\"p\",\n\t\"р\":\"r\",\n\t\"о\":\"o\",\n\t\"л\":\"l\",\n\t\"д\":\"d\",\n\t\"ж\":\"zh\",\n\t\"э\":\"e\",\n\t\"Я\":\"Ya\",\n\t\"Ч\":\"CH\",\n\t\"С\":\"S\",\n\t\"М\":\"M\",\n\t\"И\":\"I\",\n\t\"Т\":\"T\",\n\t\"Ь\":\"'\",\n\t\"Б\":\"B\",\n\t\"Ю\":\"YU\",\n\t\"я\":\"ya\",\n\t\"ч\":\"ch\",\n\t\"с\":\"s\",\n\t\"м\":\"m\",\n\t\"и\":\"i\",\n\t\"т\":\"t\",\n\t\"ь\":\"'\",\n\t\"б\":\"b\",\n\t\"ю\":\"yu\"\n};\n\nexports.transliterate = function(str) {\n\treturn str.split(\"\").map(function(char) {\n\t\treturn transliterationPairs[char] || char;\n\t}).join(\"\");\n};\n\n})();\n",
"title": "$:/core/modules/utils/utils.js",
"type": "application/javascript",
"module-type": "utils"
},
"$:/core/modules/widgets/action-createtiddler.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/action-createtiddler.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to create a new tiddler with a unique name and specified fields.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar CreateTiddlerWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nCreateTiddlerWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nCreateTiddlerWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nCreateTiddlerWidget.prototype.execute = function() {\n\tthis.actionBaseTitle = this.getAttribute(\"$basetitle\");\n\tthis.actionSaveTitle = this.getAttribute(\"$savetitle\");\n\tthis.actionTimestamp = this.getAttribute(\"$timestamp\",\"yes\") === \"yes\";\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nCreateTiddlerWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif($tw.utils.count(changedAttributes) > 0) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nCreateTiddlerWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar title = this.wiki.generateNewTitle(this.actionBaseTitle),\n\t\tfields = {},\n\t\tcreationFields,\n\t\tmodificationFields;\n\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\tif(name.charAt(0) !== \"$\") {\n\t\t\tfields[name] = attribute;\n\t\t}\n\t});\n\tif(this.actionTimestamp) {\n\t\tcreationFields = this.wiki.getCreationFields();\n\t\tmodificationFields = this.wiki.getModificationFields();\n\t}\n\tvar tiddler = this.wiki.addTiddler(new $tw.Tiddler(creationFields,fields,modificationFields,{title: title}));\n\tif(this.actionSaveTitle) {\n\t\tthis.wiki.setTextReference(this.actionSaveTitle,title,this.getVariable(\"currentTiddler\"));\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-createtiddler\"] = CreateTiddlerWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/action-createtiddler.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-deletefield.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/action-deletefield.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to delete fields of a tiddler.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DeleteFieldWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDeleteFieldWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDeleteFieldWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nDeleteFieldWidget.prototype.execute = function() {\n\tthis.actionTiddler = this.getAttribute(\"$tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.actionField = this.getAttribute(\"$field\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nDeleteFieldWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$tiddler\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nDeleteFieldWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar self = this,\n\t\ttiddler = this.wiki.getTiddler(self.actionTiddler),\n\t\tremoveFields = {},\n\t\thasChanged = false;\n\tif(this.actionField) {\n\t\tremoveFields[this.actionField] = undefined;\n\t\tif(this.actionField in tiddler.fields) {\n\t\t\thasChanged = true;\n\t\t}\n\t}\n\tif(tiddler) {\n\t\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\t\tif(name.charAt(0) !== \"$\" && name !== \"title\") {\n\t\t\t\tremoveFields[name] = undefined;\n\t\t\t\thasChanged = true;\n\t\t\t}\n\t\t});\n\t\tif(hasChanged) {\n\t\t\tthis.wiki.addTiddler(new $tw.Tiddler(this.wiki.getCreationFields(),tiddler,removeFields,this.wiki.getModificationFields()));\t\t\t\n\t\t}\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-deletefield\"] = DeleteFieldWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/action-deletefield.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-deletetiddler.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/action-deletetiddler.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to delete a tiddler.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DeleteTiddlerWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDeleteTiddlerWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDeleteTiddlerWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nDeleteTiddlerWidget.prototype.execute = function() {\n\tthis.actionFilter = this.getAttribute(\"$filter\");\n\tthis.actionTiddler = this.getAttribute(\"$tiddler\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nDeleteTiddlerWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$filter\"] || changedAttributes[\"$tiddler\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nDeleteTiddlerWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar tiddlers = [];\n\tif(this.actionFilter) {\n\t\ttiddlers = this.wiki.filterTiddlers(this.actionFilter,this);\n\t}\n\tif(this.actionTiddler) {\n\t\ttiddlers.push(this.actionTiddler);\n\t}\n\tfor(var t=0; t<tiddlers.length; t++) {\n\t\tthis.wiki.deleteTiddler(tiddlers[t]);\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-deletetiddler\"] = DeleteTiddlerWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/action-deletetiddler.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-listops.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/action-listops.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to apply list operations to any tiddler field (defaults to the 'list' field of the current tiddler)\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\nvar ActionListopsWidget = function(parseTreeNode, options) {\n\tthis.initialise(parseTreeNode, options);\n};\n/**\n * Inherit from the base widget class\n */\nActionListopsWidget.prototype = new Widget();\n/**\n * Render this widget into the DOM\n */\nActionListopsWidget.prototype.render = function(parent, nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n/**\n * Compute the internal state of the widget\n */\nActionListopsWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.target = this.getAttribute(\"$tiddler\", this.getVariable(\n\t\t\"currentTiddler\"));\n\tthis.filter = this.getAttribute(\"$filter\");\n\tthis.subfilter = this.getAttribute(\"$subfilter\");\n\tthis.listField = this.getAttribute(\"$field\", \"list\");\n\tthis.listIndex = this.getAttribute(\"$index\");\n\tthis.filtertags = this.getAttribute(\"$tags\");\n};\n/**\n * \tRefresh the widget by ensuring our attributes are up to date\n */\nActionListopsWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.$tiddler || changedAttributes.$filter ||\n\t\tchangedAttributes.$subfilter || changedAttributes.$field ||\n\t\tchangedAttributes.$index || changedAttributes.$tags) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n/**\n * \tInvoke the action associated with this widget\n */\nActionListopsWidget.prototype.invokeAction = function(triggeringWidget,\n\tevent) {\n\t//Apply the specified filters to the lists\n\tvar field = this.listField,\n\t\tindex,\n\t\ttype = \"!!\",\n\t\tlist = this.listField;\n\tif(this.listIndex) {\n\t\tfield = undefined;\n\t\tindex = this.listIndex;\n\t\ttype = \"##\";\n\t\tlist = this.listIndex;\n\t}\n\tif(this.filter) {\n\t\tthis.wiki.setText(this.target, field, index, $tw.utils.stringifyList(\n\t\t\tthis.wiki\n\t\t\t.filterTiddlers(this.filter, this)));\n\t}\n\tif(this.subfilter) {\n\t\tvar subfilter = \"[list[\" + this.target + type + list + \"]] \" + this.subfilter;\n\t\tthis.wiki.setText(this.target, field, index, $tw.utils.stringifyList(\n\t\t\tthis.wiki\n\t\t\t.filterTiddlers(subfilter, this)));\n\t}\n\tif(this.filtertags) {\n\t\tvar tiddler = this.wiki.getTiddler(this.target),\n\t\t\toldtags = tiddler ? (tiddler.fields.tags || []).slice(0) : [],\n\t\t\ttagfilter = \"[list[\" + this.target + \"!!tags]] \" + this.filtertags,\n\t\t\tnewtags = this.wiki.filterTiddlers(tagfilter,this);\n\t\tif($tw.utils.stringifyList(oldtags.sort()) !== $tw.utils.stringifyList(newtags.sort())) {\n\t\t\tthis.wiki.setText(this.target,\"tags\",undefined,$tw.utils.stringifyList(newtags));\t\t\t\n\t\t}\n\t}\n\treturn true; // Action was invoked\n};\n\nexports[\"action-listops\"] = ActionListopsWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/action-listops.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-navigate.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/action-navigate.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to navigate to a tiddler\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar NavigateWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nNavigateWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nNavigateWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nNavigateWidget.prototype.execute = function() {\n\tthis.actionTo = this.getAttribute(\"$to\");\n\tthis.actionScroll = this.getAttribute(\"$scroll\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nNavigateWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$to\"] || changedAttributes[\"$scroll\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nNavigateWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar bounds = triggeringWidget && triggeringWidget.getBoundingClientRect && triggeringWidget.getBoundingClientRect(),\n\t\tsuppressNavigation = event.metaKey || event.ctrlKey || (event.button === 1);\n\tif(this.actionScroll === \"yes\") {\n\t\tsuppressNavigation = false;\n\t} else if(this.actionScroll === \"no\") {\n\t\tsuppressNavigation = true;\n\t}\n\tthis.dispatchEvent({\n\t\ttype: \"tm-navigate\",\n\t\tnavigateTo: this.actionTo === undefined ? this.getVariable(\"currentTiddler\") : this.actionTo,\n\t\tnavigateFromTitle: this.getVariable(\"storyTiddler\"),\n\t\tnavigateFromNode: triggeringWidget,\n\t\tnavigateFromClientRect: bounds && { top: bounds.top, left: bounds.left, width: bounds.width, right: bounds.right, bottom: bounds.bottom, height: bounds.height\n\t\t},\n\t\tnavigateSuppressNavigation: suppressNavigation\n\t});\n\treturn true; // Action was invoked\n};\n\nexports[\"action-navigate\"] = NavigateWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/action-navigate.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-sendmessage.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/action-sendmessage.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to send a message\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar SendMessageWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nSendMessageWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nSendMessageWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nSendMessageWidget.prototype.execute = function() {\n\tthis.actionMessage = this.getAttribute(\"$message\");\n\tthis.actionParam = this.getAttribute(\"$param\");\n\tthis.actionName = this.getAttribute(\"$name\");\n\tthis.actionValue = this.getAttribute(\"$value\",\"\");\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nSendMessageWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(Object.keys(changedAttributes).length) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nSendMessageWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\t// Get the string parameter\n\tvar param = this.actionParam;\n\t// Assemble the attributes as a hashmap\n\tvar paramObject = Object.create(null);\n\tvar count = 0;\n\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\tif(name.charAt(0) !== \"$\") {\n\t\t\tparamObject[name] = attribute;\n\t\t\tcount++;\n\t\t}\n\t});\n\t// Add name/value pair if present\n\tif(this.actionName) {\n\t\tparamObject[this.actionName] = this.actionValue;\n\t}\n\t// Dispatch the message\n\tthis.dispatchEvent({\n\t\ttype: this.actionMessage,\n\t\tparam: param,\n\t\tparamObject: paramObject,\n\t\ttiddlerTitle: this.getVariable(\"currentTiddler\"),\n\t\tnavigateFromTitle: this.getVariable(\"storyTiddler\"),\n\t\tevent: event\n\t});\n\treturn true; // Action was invoked\n};\n\nexports[\"action-sendmessage\"] = SendMessageWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/action-sendmessage.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/action-setfield.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/action-setfield.js\ntype: application/javascript\nmodule-type: widget\n\nAction widget to set a single field or index on a tiddler.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar SetFieldWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nSetFieldWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nSetFieldWidget.prototype.render = function(parent,nextSibling) {\n\tthis.computeAttributes();\n\tthis.execute();\n};\n\n/*\nCompute the internal state of the widget\n*/\nSetFieldWidget.prototype.execute = function() {\n\tthis.actionTiddler = this.getAttribute(\"$tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.actionField = this.getAttribute(\"$field\");\n\tthis.actionIndex = this.getAttribute(\"$index\");\n\tthis.actionValue = this.getAttribute(\"$value\");\n\tthis.actionTimestamp = this.getAttribute(\"$timestamp\",\"yes\") === \"yes\";\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nSetFieldWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"$tiddler\"] || changedAttributes[\"$field\"] || changedAttributes[\"$index\"] || changedAttributes[\"$value\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nInvoke the action associated with this widget\n*/\nSetFieldWidget.prototype.invokeAction = function(triggeringWidget,event) {\n\tvar self = this,\n\t\toptions = {};\n\toptions.suppressTimestamp = !this.actionTimestamp;\n\tif((typeof this.actionField == \"string\") || (typeof this.actionIndex == \"string\") || (typeof this.actionValue == \"string\")) {\n\t\tthis.wiki.setText(this.actionTiddler,this.actionField,this.actionIndex,this.actionValue,options);\n\t}\n\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\tif(name.charAt(0) !== \"$\") {\n\t\t\tself.wiki.setText(self.actionTiddler,name,undefined,attribute,options);\n\t\t}\n\t});\n\treturn true; // Action was invoked\n};\n\nexports[\"action-setfield\"] = SetFieldWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/action-setfield.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/browse.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/browse.js\ntype: application/javascript\nmodule-type: widget\n\nBrowse widget for browsing for files to import\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar BrowseWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nBrowseWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nBrowseWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create element\n\tvar domNode = this.document.createElement(\"input\");\n\tdomNode.setAttribute(\"type\",\"file\");\n\tif(this.browseMultiple) {\n\t\tdomNode.setAttribute(\"multiple\",\"multiple\");\n\t}\n\tif(this.tooltip) {\n\t\tdomNode.setAttribute(\"title\",this.tooltip);\n\t}\n\t// Nw.js supports \"nwsaveas\" to force a \"save as\" dialogue that allows a new or existing file to be selected\n\tif(this.nwsaveas) {\n\t\tdomNode.setAttribute(\"nwsaveas\",this.nwsaveas);\n\t}\n\t// Nw.js supports \"webkitdirectory\" to allow a directory to be selected\n\tif(this.webkitdirectory) {\n\t\tdomNode.setAttribute(\"webkitdirectory\",this.webkitdirectory);\n\t}\n\t// Add a click event handler\n\tdomNode.addEventListener(\"change\",function (event) {\n\t\tif(self.message) {\n\t\t\tself.dispatchEvent({type: self.message, param: self.param, files: event.target.files});\n\t\t} else {\n\t\t\tself.wiki.readFiles(event.target.files,function(tiddlerFieldsArray) {\n\t\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify(tiddlerFieldsArray)});\n\t\t\t});\n\t\t}\n\t\treturn false;\n\t},false);\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nBrowseWidget.prototype.execute = function() {\n\tthis.browseMultiple = this.getAttribute(\"multiple\");\n\tthis.message = this.getAttribute(\"message\");\n\tthis.param = this.getAttribute(\"param\");\n\tthis.tooltip = this.getAttribute(\"tooltip\");\n\tthis.nwsaveas = this.getAttribute(\"nwsaveas\");\n\tthis.webkitdirectory = this.getAttribute(\"webkitdirectory\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nBrowseWidget.prototype.refresh = function(changedTiddlers) {\n\treturn false;\n};\n\nexports.browse = BrowseWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/browse.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/button.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/button.js\ntype: application/javascript\nmodule-type: widget\n\nButton widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ButtonWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nButtonWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nButtonWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create element\n\tvar tag = \"button\";\n\tif(this.buttonTag && $tw.config.htmlUnsafeElements.indexOf(this.buttonTag) === -1) {\n\t\ttag = this.buttonTag;\n\t}\n\tvar domNode = this.document.createElement(tag);\n\t// Assign classes\n\tvar classes = this[\"class\"].split(\" \") || [],\n\t\tisPoppedUp = this.popup && this.isPoppedUp();\n\tif(this.selectedClass) {\n\t\tif(this.set && this.setTo && this.isSelected()) {\n\t\t\t$tw.utils.pushTop(classes,this.selectedClass.split(\" \"));\n\t\t}\n\t\tif(isPoppedUp) {\n\t\t\t$tw.utils.pushTop(classes,this.selectedClass.split(\" \"));\n\t\t}\n\t}\n\tif(isPoppedUp) {\n\t\t$tw.utils.pushTop(classes,\"tc-popup-handle\");\n\t}\n\tdomNode.className = classes.join(\" \");\n\t// Assign other attributes\n\tif(this.style) {\n\t\tdomNode.setAttribute(\"style\",this.style);\n\t}\n\tif(this.tooltip) {\n\t\tdomNode.setAttribute(\"title\",this.tooltip);\n\t}\n\tif(this[\"aria-label\"]) {\n\t\tdomNode.setAttribute(\"aria-label\",this[\"aria-label\"]);\n\t}\n\t// Add a click event handler\n\tdomNode.addEventListener(\"click\",function (event) {\n\t\tvar handled = false;\n\t\tif(self.invokeActions(this,event)) {\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.to) {\n\t\t\tself.navigateTo(event);\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.message) {\n\t\t\tself.dispatchMessage(event);\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.popup) {\n\t\t\tself.triggerPopup(event);\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.set) {\n\t\t\tself.setTiddler();\n\t\t\thandled = true;\n\t\t}\n\t\tif(self.actions) {\n\t\t\tself.invokeActionString(self.actions,self,event);\n\t\t}\n\t\tif(handled) {\n\t\t\tevent.preventDefault();\n\t\t\tevent.stopPropagation();\n\t\t}\n\t\treturn handled;\n\t},false);\n\t// Make it draggable if required\n\tif(this.dragTiddler || this.dragFilter) {\n\t\t$tw.utils.makeDraggable({\n\t\t\tdomNode: domNode,\n\t\t\tdragTiddlerFn: function() {return self.dragTiddler;},\n\t\t\tdragFilterFn: function() {return self.dragFilter;},\n\t\t\twidget: this\n\t\t});\n\t}\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nWe don't allow actions to propagate because we trigger actions ourselves\n*/\nButtonWidget.prototype.allowActionPropagation = function() {\n\treturn false;\n};\n\nButtonWidget.prototype.getBoundingClientRect = function() {\n\treturn this.domNodes[0].getBoundingClientRect();\n};\n\nButtonWidget.prototype.isSelected = function() {\n return this.wiki.getTextReference(this.set,this.defaultSetValue,this.getVariable(\"currentTiddler\")) === this.setTo;\n};\n\nButtonWidget.prototype.isPoppedUp = function() {\n\tvar tiddler = this.wiki.getTiddler(this.popup);\n\tvar result = tiddler && tiddler.fields.text ? $tw.popup.readPopupState(tiddler.fields.text) : false;\n\treturn result;\n};\n\nButtonWidget.prototype.navigateTo = function(event) {\n\tvar bounds = this.getBoundingClientRect();\n\tthis.dispatchEvent({\n\t\ttype: \"tm-navigate\",\n\t\tnavigateTo: this.to,\n\t\tnavigateFromTitle: this.getVariable(\"storyTiddler\"),\n\t\tnavigateFromNode: this,\n\t\tnavigateFromClientRect: { top: bounds.top, left: bounds.left, width: bounds.width, right: bounds.right, bottom: bounds.bottom, height: bounds.height\n\t\t},\n\t\tnavigateSuppressNavigation: event.metaKey || event.ctrlKey || (event.button === 1),\n\t\tevent: event\n\t});\n};\n\nButtonWidget.prototype.dispatchMessage = function(event) {\n\tthis.dispatchEvent({type: this.message, param: this.param, tiddlerTitle: this.getVariable(\"currentTiddler\"), event: event});\n};\n\nButtonWidget.prototype.triggerPopup = function(event) {\n\t$tw.popup.triggerPopup({\n\t\tdomNode: this.domNodes[0],\n\t\ttitle: this.popup,\n\t\twiki: this.wiki\n\t});\n};\n\nButtonWidget.prototype.setTiddler = function() {\n\tthis.wiki.setTextReference(this.set,this.setTo,this.getVariable(\"currentTiddler\"));\n};\n\n/*\nCompute the internal state of the widget\n*/\nButtonWidget.prototype.execute = function() {\n\t// Get attributes\n\tthis.actions = this.getAttribute(\"actions\");\n\tthis.to = this.getAttribute(\"to\");\n\tthis.message = this.getAttribute(\"message\");\n\tthis.param = this.getAttribute(\"param\");\n\tthis.set = this.getAttribute(\"set\");\n\tthis.setTo = this.getAttribute(\"setTo\");\n\tthis.popup = this.getAttribute(\"popup\");\n\tthis.hover = this.getAttribute(\"hover\");\n\tthis[\"class\"] = this.getAttribute(\"class\",\"\");\n\tthis[\"aria-label\"] = this.getAttribute(\"aria-label\");\n\tthis.tooltip = this.getAttribute(\"tooltip\");\n\tthis.style = this.getAttribute(\"style\");\n\tthis.selectedClass = this.getAttribute(\"selectedClass\");\n\tthis.defaultSetValue = this.getAttribute(\"default\",\"\");\n\tthis.buttonTag = this.getAttribute(\"tag\");\n\tthis.dragTiddler = this.getAttribute(\"dragTiddler\");\n\tthis.dragFilter = this.getAttribute(\"dragFilter\");\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nButtonWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.to || changedAttributes.message || changedAttributes.param || changedAttributes.set || changedAttributes.setTo || changedAttributes.popup || changedAttributes.hover || changedAttributes[\"class\"] || changedAttributes.selectedClass || changedAttributes.style || changedAttributes.dragFilter || changedAttributes.dragTiddler || (this.set && changedTiddlers[this.set]) || (this.popup && changedTiddlers[this.popup])) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.button = ButtonWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/button.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/checkbox.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/checkbox.js\ntype: application/javascript\nmodule-type: widget\n\nCheckbox widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar CheckboxWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nCheckboxWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nCheckboxWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Create our elements\n\tthis.labelDomNode = this.document.createElement(\"label\");\n\tthis.labelDomNode.setAttribute(\"class\",this.checkboxClass);\n\tthis.inputDomNode = this.document.createElement(\"input\");\n\tthis.inputDomNode.setAttribute(\"type\",\"checkbox\");\n\tif(this.getValue()) {\n\t\tthis.inputDomNode.setAttribute(\"checked\",\"true\");\n\t}\n\tthis.labelDomNode.appendChild(this.inputDomNode);\n\tthis.spanDomNode = this.document.createElement(\"span\");\n\tthis.labelDomNode.appendChild(this.spanDomNode);\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(this.inputDomNode,[\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleChangeEvent\"}\n\t]);\n\t// Insert the label into the DOM and render any children\n\tparent.insertBefore(this.labelDomNode,nextSibling);\n\tthis.renderChildren(this.spanDomNode,null);\n\tthis.domNodes.push(this.labelDomNode);\n};\n\nCheckboxWidget.prototype.getValue = function() {\n\tvar tiddler = this.wiki.getTiddler(this.checkboxTitle);\n\tif(tiddler) {\n\t\tif(this.checkboxTag) {\n\t\t\tif(this.checkboxInvertTag) {\n\t\t\t\treturn !tiddler.hasTag(this.checkboxTag);\n\t\t\t} else {\n\t\t\t\treturn tiddler.hasTag(this.checkboxTag);\n\t\t\t}\n\t\t}\n\t\tif(this.checkboxField) {\n\t\t\tvar value;\n\t\t\tif($tw.utils.hop(tiddler.fields,this.checkboxField)) {\n\t\t\t\tvalue = tiddler.fields[this.checkboxField] || \"\";\n\t\t\t} else {\n\t\t\t\tvalue = this.checkboxDefault || \"\";\n\t\t\t}\n\t\t\tif(value === this.checkboxChecked) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t\tif(value === this.checkboxUnchecked) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t}\n\t\tif(this.checkboxIndex) {\n\t\t\tvar value = this.wiki.extractTiddlerDataItem(tiddler,this.checkboxIndex,this.checkboxDefault || \"\");\n\t\t\tif(value === this.checkboxChecked) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t\tif(value === this.checkboxUnchecked) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t}\n\t} else {\n\t\tif(this.checkboxTag) {\n\t\t\treturn false;\n\t\t}\n\t\tif(this.checkboxField) {\n\t\t\tif(this.checkboxDefault === this.checkboxChecked) {\n\t\t\t\treturn true;\n\t\t\t}\n\t\t\tif(this.checkboxDefault === this.checkboxUnchecked) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t}\n\t}\n\treturn false;\n};\n\nCheckboxWidget.prototype.handleChangeEvent = function(event) {\n\tvar checked = this.inputDomNode.checked,\n\t\ttiddler = this.wiki.getTiddler(this.checkboxTitle),\n\t\tfallbackFields = {text: \"\"},\n\t\tnewFields = {title: this.checkboxTitle},\n\t\thasChanged = false,\n\t\ttagCheck = false,\n\t\thasTag = tiddler && tiddler.hasTag(this.checkboxTag),\n\t\tvalue = checked ? this.checkboxChecked : this.checkboxUnchecked;\n\tif(this.checkboxTag && this.checkboxInvertTag === \"yes\") {\n\t\ttagCheck = hasTag === checked;\n\t} else {\n\t\ttagCheck = hasTag !== checked;\n\t}\n\t// Set the tag if specified\n\tif(this.checkboxTag && (!tiddler || tagCheck)) {\n\t\tnewFields.tags = tiddler ? (tiddler.fields.tags || []).slice(0) : [];\n\t\tvar pos = newFields.tags.indexOf(this.checkboxTag);\n\t\tif(pos !== -1) {\n\t\t\tnewFields.tags.splice(pos,1);\n\t\t}\n\t\tif(this.checkboxInvertTag === \"yes\" && !checked) {\n\t\t\tnewFields.tags.push(this.checkboxTag);\n\t\t} else if(this.checkboxInvertTag !== \"yes\" && checked) {\n\t\t\tnewFields.tags.push(this.checkboxTag);\n\t\t}\n\t\thasChanged = true;\n\t}\n\t// Set the field if specified\n\tif(this.checkboxField) {\n\t\tif(!tiddler || tiddler.fields[this.checkboxField] !== value) {\n\t\t\tnewFields[this.checkboxField] = value;\n\t\t\thasChanged = true;\n\t\t}\n\t}\n\t// Set the index if specified\n\tif(this.checkboxIndex) {\n\t\tvar indexValue = this.wiki.extractTiddlerDataItem(this.checkboxTitle,this.checkboxIndex);\n\t\tif(!tiddler || indexValue !== value) {\n\t\t\thasChanged = true;\n\t\t}\n\t}\n\tif(hasChanged) {\n\t\tif(this.checkboxIndex) {\n\t\t\tthis.wiki.setText(this.checkboxTitle,\"\",this.checkboxIndex,value);\n\t\t} else {\n\t\t\tthis.wiki.addTiddler(new $tw.Tiddler(this.wiki.getCreationFields(),fallbackFields,tiddler,newFields,this.wiki.getModificationFields()));\n\t\t}\n\t}\n\t// Trigger actions\n\tif(this.checkboxActions) {\n\t\tthis.invokeActionString(this.checkboxActions,this,event);\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nCheckboxWidget.prototype.execute = function() {\n\t// Get the parameters from the attributes\n\tthis.checkboxActions = this.getAttribute(\"actions\");\n\tthis.checkboxTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.checkboxTag = this.getAttribute(\"tag\");\n\tthis.checkboxField = this.getAttribute(\"field\");\n\tthis.checkboxIndex = this.getAttribute(\"index\");\n\tthis.checkboxChecked = this.getAttribute(\"checked\");\n\tthis.checkboxUnchecked = this.getAttribute(\"unchecked\");\n\tthis.checkboxDefault = this.getAttribute(\"default\");\n\tthis.checkboxClass = this.getAttribute(\"class\",\"\");\n\tthis.checkboxInvertTag = this.getAttribute(\"invertTag\",\"\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nCheckboxWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.tag || changedAttributes.invertTag || changedAttributes.field || changedAttributes.index || changedAttributes.checked || changedAttributes.unchecked || changedAttributes[\"default\"] || changedAttributes[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\tvar refreshed = false;\n\t\tif(changedTiddlers[this.checkboxTitle]) {\n\t\t\tthis.inputDomNode.checked = this.getValue();\n\t\t\trefreshed = true;\n\t\t}\n\t\treturn this.refreshChildren(changedTiddlers) || refreshed;\n\t}\n};\n\nexports.checkbox = CheckboxWidget;\n\n})();",
"title": "$:/core/modules/widgets/checkbox.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/codeblock.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/codeblock.js\ntype: application/javascript\nmodule-type: widget\n\nCode block node widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar CodeBlockWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nCodeBlockWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nCodeBlockWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar codeNode = this.document.createElement(\"code\"),\n\t\tdomNode = this.document.createElement(\"pre\");\n\tcodeNode.appendChild(this.document.createTextNode(this.getAttribute(\"code\")));\n\tdomNode.appendChild(codeNode);\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.domNodes.push(domNode);\n\tif(this.postRender) {\n\t\tthis.postRender();\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nCodeBlockWidget.prototype.execute = function() {\n\tthis.language = this.getAttribute(\"language\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nCodeBlockWidget.prototype.refresh = function(changedTiddlers) {\n\treturn false;\n};\n\nexports.codeblock = CodeBlockWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/codeblock.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/count.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/count.js\ntype: application/javascript\nmodule-type: widget\n\nCount widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar CountWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nCountWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nCountWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar textNode = this.document.createTextNode(this.currentCount);\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nCountWidget.prototype.execute = function() {\n\t// Get parameters from our attributes\n\tthis.filter = this.getAttribute(\"filter\");\n\t// Execute the filter\n\tif(this.filter) {\n\t\tthis.currentCount = this.wiki.filterTiddlers(this.filter,this).length;\n\t} else {\n\t\tthis.currentCount = undefined;\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nCountWidget.prototype.refresh = function(changedTiddlers) {\n\t// Re-execute the filter to get the count\n\tthis.computeAttributes();\n\tvar oldCount = this.currentCount;\n\tthis.execute();\n\tif(this.currentCount !== oldCount) {\n\t\t// Regenerate and rerender the widget and replace the existing DOM node\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\n\t}\n\n};\n\nexports.count = CountWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/count.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/draggable.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/draggable.js\ntype: application/javascript\nmodule-type: widget\n\nDraggable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DraggableWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDraggableWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDraggableWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Sanitise the specified tag\n\tvar tag = this.draggableTag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"div\";\n\t}\n\t// Create our element\n\tvar domNode = this.document.createElement(tag);\n\t// Assign classes\n\tvar classes = [\"tc-draggable\"];\n\tif(this.draggableClasses) {\n\t\tclasses.push(this.draggableClasses);\n\t}\n\tdomNode.setAttribute(\"class\",classes.join(\" \"));\n\t// Add event handlers\n\t$tw.utils.makeDraggable({\n\t\tdomNode: domNode,\n\t\tdragTiddlerFn: function() {return self.getAttribute(\"tiddler\");},\n\t\tdragFilterFn: function() {return self.getAttribute(\"filter\");},\n\t\twidget: this\n\t});\n\t// Insert the link into the DOM and render any children\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nDraggableWidget.prototype.execute = function() {\n\t// Pick up our attributes\n\tthis.draggableTag = this.getAttribute(\"tag\",\"div\");\n\tthis.draggableClasses = this.getAttribute(\"class\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nDraggableWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedTiddlers.tag || changedTiddlers[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.draggable = DraggableWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/draggable.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/droppable.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/droppable.js\ntype: application/javascript\nmodule-type: widget\n\nDroppable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DroppableWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDroppableWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDroppableWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar tag = this.parseTreeNode.isBlock ? \"div\" : \"span\";\n\tif(this.droppableTag && $tw.config.htmlUnsafeElements.indexOf(this.droppableTag) === -1) {\n\t\ttag = this.droppableTag;\n\t}\n\t// Create element and assign classes\n\tvar domNode = this.document.createElement(tag),\n\t\tclasses = (this[\"class\"] || \"\").split(\" \");\n\tclasses.push(\"tc-droppable\");\n\tdomNode.className = classes.join(\" \");\n\t// Add event handlers\n\t$tw.utils.addEventListeners(domNode,[\n\t\t{name: \"dragenter\", handlerObject: this, handlerMethod: \"handleDragEnterEvent\"},\n\t\t{name: \"dragover\", handlerObject: this, handlerMethod: \"handleDragOverEvent\"},\n\t\t{name: \"dragleave\", handlerObject: this, handlerMethod: \"handleDragLeaveEvent\"},\n\t\t{name: \"drop\", handlerObject: this, handlerMethod: \"handleDropEvent\"}\n\t]);\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n\t// Stack of outstanding enter/leave events\n\tthis.currentlyEntered = [];\n};\n\nDroppableWidget.prototype.enterDrag = function(event) {\n\tif(this.currentlyEntered.indexOf(event.target) === -1) {\n\t\tthis.currentlyEntered.push(event.target);\n\t}\n\t// If we're entering for the first time we need to apply highlighting\n\t$tw.utils.addClass(this.domNodes[0],\"tc-dragover\");\n};\n\nDroppableWidget.prototype.leaveDrag = function(event) {\n\tvar pos = this.currentlyEntered.indexOf(event.target);\n\tif(pos !== -1) {\n\t\tthis.currentlyEntered.splice(pos,1);\n\t}\n\t// Remove highlighting if we're leaving externally. The hacky second condition is to resolve a problem with Firefox whereby there is an erroneous dragenter event if the node being dragged is within the dropzone\n\tif(this.currentlyEntered.length === 0 || (this.currentlyEntered.length === 1 && this.currentlyEntered[0] === $tw.dragInProgress)) {\n\t\tthis.currentlyEntered = [];\n\t\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n\t}\n};\n\nDroppableWidget.prototype.handleDragEnterEvent = function(event) {\n\tthis.enterDrag(event);\n\t// Tell the browser that we're ready to handle the drop\n\tevent.preventDefault();\n\t// Tell the browser not to ripple the drag up to any parent drop handlers\n\tevent.stopPropagation();\n\treturn false;\n};\n\nDroppableWidget.prototype.handleDragOverEvent = function(event) {\n\t// Check for being over a TEXTAREA or INPUT\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) !== -1) {\n\t\treturn false;\n\t}\n\t// Tell the browser that we're still interested in the drop\n\tevent.preventDefault();\n\t// Set the drop effect\n\tevent.dataTransfer.dropEffect = this.droppableEffect;\n\treturn false;\n};\n\nDroppableWidget.prototype.handleDragLeaveEvent = function(event) {\n\tthis.leaveDrag(event);\n\treturn false;\n};\n\nDroppableWidget.prototype.handleDropEvent = function(event) {\n\tvar self = this;\n\tthis.leaveDrag(event);\n\t// Check for being over a TEXTAREA or INPUT\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) !== -1) {\n\t\treturn false;\n\t}\n\tvar dataTransfer = event.dataTransfer;\n\t// Remove highlighting\n\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n\t// Try to import the various data types we understand\n\t$tw.utils.importDataTransfer(dataTransfer,null,function(fieldsArray) {\n\t\tfieldsArray.forEach(function(fields) {\n\t\t\tself.performActions(fields.title || fields.text,event);\n\t\t});\n\t});\n\t// Tell the browser that we handled the drop\n\tevent.preventDefault();\n\t// Stop the drop ripple up to any parent handlers\n\tevent.stopPropagation();\n\treturn false;\n};\n\nDroppableWidget.prototype.performActions = function(title,event) {\n\tif(this.droppableActions) {\n\t\tthis.invokeActionString(this.droppableActions,this,event,{actionTiddler: title});\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nDroppableWidget.prototype.execute = function() {\n\tthis.droppableActions = this.getAttribute(\"actions\");\n\tthis.droppableEffect = this.getAttribute(\"effect\",\"copy\");\n\tthis.droppableTag = this.getAttribute(\"tag\");\n\tthis.droppableClass = this.getAttribute(\"class\");\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nDroppableWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"class\"] || changedAttributes.tag) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.droppable = DroppableWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/droppable.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/dropzone.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/dropzone.js\ntype: application/javascript\nmodule-type: widget\n\nDropzone widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar DropZoneWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nDropZoneWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nDropZoneWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create element\n\tvar domNode = this.document.createElement(\"div\");\n\tdomNode.className = \"tc-dropzone\";\n\t// Add event handlers\n\t$tw.utils.addEventListeners(domNode,[\n\t\t{name: \"dragenter\", handlerObject: this, handlerMethod: \"handleDragEnterEvent\"},\n\t\t{name: \"dragover\", handlerObject: this, handlerMethod: \"handleDragOverEvent\"},\n\t\t{name: \"dragleave\", handlerObject: this, handlerMethod: \"handleDragLeaveEvent\"},\n\t\t{name: \"drop\", handlerObject: this, handlerMethod: \"handleDropEvent\"},\n\t\t{name: \"paste\", handlerObject: this, handlerMethod: \"handlePasteEvent\"}\n\t]);\n\tdomNode.addEventListener(\"click\",function (event) {\n\t},false);\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n\t// Stack of outstanding enter/leave events\n\tthis.currentlyEntered = [];\n};\n\nDropZoneWidget.prototype.enterDrag = function(event) {\n\tif(this.currentlyEntered.indexOf(event.target) === -1) {\n\t\tthis.currentlyEntered.push(event.target);\n\t}\n\t// If we're entering for the first time we need to apply highlighting\n\t$tw.utils.addClass(this.domNodes[0],\"tc-dragover\");\n};\n\nDropZoneWidget.prototype.leaveDrag = function(event) {\n\tvar pos = this.currentlyEntered.indexOf(event.target);\n\tif(pos !== -1) {\n\t\tthis.currentlyEntered.splice(pos,1);\n\t}\n\t// Remove highlighting if we're leaving externally\n\tif(this.currentlyEntered.length === 0) {\n\t\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n\t}\n};\n\nDropZoneWidget.prototype.handleDragEnterEvent = function(event) {\n\t// Check for this window being the source of the drag\n\tif($tw.dragInProgress) {\n\t\treturn false;\n\t}\n\tthis.enterDrag(event);\n\t// Tell the browser that we're ready to handle the drop\n\tevent.preventDefault();\n\t// Tell the browser not to ripple the drag up to any parent drop handlers\n\tevent.stopPropagation();\n};\n\nDropZoneWidget.prototype.handleDragOverEvent = function(event) {\n\t// Check for being over a TEXTAREA or INPUT\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) !== -1) {\n\t\treturn false;\n\t}\n\t// Check for this window being the source of the drag\n\tif($tw.dragInProgress) {\n\t\treturn false;\n\t}\n\t// Tell the browser that we're still interested in the drop\n\tevent.preventDefault();\n\tevent.dataTransfer.dropEffect = \"copy\"; // Explicitly show this is a copy\n};\n\nDropZoneWidget.prototype.handleDragLeaveEvent = function(event) {\n\tthis.leaveDrag(event);\n};\n\nDropZoneWidget.prototype.handleDropEvent = function(event) {\n\tvar self = this;\n\tthis.leaveDrag(event);\n\t// Check for being over a TEXTAREA or INPUT\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) !== -1) {\n\t\treturn false;\n\t}\n\t// Check for this window being the source of the drag\n\tif($tw.dragInProgress) {\n\t\treturn false;\n\t}\n\tvar self = this,\n\t\tdataTransfer = event.dataTransfer;\n\t// Remove highlighting\n\t$tw.utils.removeClass(this.domNodes[0],\"tc-dragover\");\n\t// Import any files in the drop\n\tvar numFiles = 0;\n\tif(dataTransfer.files) {\n\t\tnumFiles = this.wiki.readFiles(dataTransfer.files,function(tiddlerFieldsArray) {\n\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify(tiddlerFieldsArray)});\n\t\t});\n\t}\n\t// Try to import the various data types we understand\n\tif(numFiles === 0) {\n\t\t$tw.utils.importDataTransfer(dataTransfer,this.wiki.generateNewTitle(\"Untitled\"),function(fieldsArray) {\n\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify(fieldsArray)});\n\t\t});\n\t}\n\t// Tell the browser that we handled the drop\n\tevent.preventDefault();\n\t// Stop the drop ripple up to any parent handlers\n\tevent.stopPropagation();\n};\n\nDropZoneWidget.prototype.handlePasteEvent = function(event) {\n\t// Let the browser handle it if we're in a textarea or input box\n\tif([\"TEXTAREA\",\"INPUT\"].indexOf(event.target.tagName) == -1) {\n\t\tvar self = this,\n\t\t\titems = event.clipboardData.items;\n\t\t// Enumerate the clipboard items\n\t\tfor(var t = 0; t<items.length; t++) {\n\t\t\tvar item = items[t];\n\t\t\tif(item.kind === \"file\") {\n\t\t\t\t// Import any files\n\t\t\t\tthis.wiki.readFile(item.getAsFile(),function(tiddlerFieldsArray) {\n\t\t\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify(tiddlerFieldsArray)});\n\t\t\t\t});\n\t\t\t} else if(item.kind === \"string\") {\n\t\t\t\t// Create tiddlers from string items\n\t\t\t\tvar type = item.type;\n\t\t\t\titem.getAsString(function(str) {\n\t\t\t\t\tvar tiddlerFields = {\n\t\t\t\t\t\ttitle: self.wiki.generateNewTitle(\"Untitled\"),\n\t\t\t\t\t\ttext: str,\n\t\t\t\t\t\ttype: type\n\t\t\t\t\t};\n\t\t\t\t\tif($tw.log.IMPORT) {\n\t\t\t\t\t\tconsole.log(\"Importing string '\" + str + \"', type: '\" + type + \"'\");\n\t\t\t\t\t}\n\t\t\t\t\tself.dispatchEvent({type: \"tm-import-tiddlers\", param: JSON.stringify([tiddlerFields])});\n\t\t\t\t});\n\t\t\t}\n\t\t}\n\t\t// Tell the browser that we've handled the paste\n\t\tevent.stopPropagation();\n\t\tevent.preventDefault();\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nDropZoneWidget.prototype.execute = function() {\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nDropZoneWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.dropzone = DropZoneWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/dropzone.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit-binary.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/edit-binary.js\ntype: application/javascript\nmodule-type: widget\n\nEdit-binary widget; placeholder for editing binary tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar BINARY_WARNING_MESSAGE = \"$:/core/ui/BinaryWarning\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EditBinaryWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEditBinaryWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEditBinaryWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nEditBinaryWidget.prototype.execute = function() {\n\t// Construct the child widgets\n\tthis.makeChildWidgets([{\n\t\ttype: \"transclude\",\n\t\tattributes: {\n\t\t\ttiddler: {type: \"string\", value: BINARY_WARNING_MESSAGE}\n\t\t}\n\t}]);\n};\n\n/*\nRefresh by refreshing our child widget\n*/\nEditBinaryWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports[\"edit-binary\"] = EditBinaryWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/edit-binary.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit-bitmap.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/edit-bitmap.js\ntype: application/javascript\nmodule-type: widget\n\nEdit-bitmap widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n// Default image sizes\nvar DEFAULT_IMAGE_WIDTH = 600,\n\tDEFAULT_IMAGE_HEIGHT = 370;\n\n// Configuration tiddlers\nvar LINE_WIDTH_TITLE = \"$:/config/BitmapEditor/LineWidth\",\n\tLINE_COLOUR_TITLE = \"$:/config/BitmapEditor/Colour\",\n\tLINE_OPACITY_TITLE = \"$:/config/BitmapEditor/Opacity\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EditBitmapWidget = function(parseTreeNode,options) {\n\t// Initialise the editor operations if they've not been done already\n\tif(!this.editorOperations) {\n\t\tEditBitmapWidget.prototype.editorOperations = {};\n\t\t$tw.modules.applyMethods(\"bitmapeditoroperation\",this.editorOperations);\n\t}\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEditBitmapWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEditBitmapWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Create the wrapper for the toolbar and render its content\n\tthis.toolbarNode = this.document.createElement(\"div\");\n\tthis.toolbarNode.className = \"tc-editor-toolbar\";\n\tparent.insertBefore(this.toolbarNode,nextSibling);\n\tthis.domNodes.push(this.toolbarNode);\n\t// Create the on-screen canvas\n\tthis.canvasDomNode = $tw.utils.domMaker(\"canvas\",{\n\t\tdocument: this.document,\n\t\t\"class\":\"tc-edit-bitmapeditor\",\n\t\teventListeners: [{\n\t\t\tname: \"touchstart\", handlerObject: this, handlerMethod: \"handleTouchStartEvent\"\n\t\t},{\n\t\t\tname: \"touchmove\", handlerObject: this, handlerMethod: \"handleTouchMoveEvent\"\n\t\t},{\n\t\t\tname: \"touchend\", handlerObject: this, handlerMethod: \"handleTouchEndEvent\"\n\t\t},{\n\t\t\tname: \"mousedown\", handlerObject: this, handlerMethod: \"handleMouseDownEvent\"\n\t\t},{\n\t\t\tname: \"mousemove\", handlerObject: this, handlerMethod: \"handleMouseMoveEvent\"\n\t\t},{\n\t\t\tname: \"mouseup\", handlerObject: this, handlerMethod: \"handleMouseUpEvent\"\n\t\t}]\n\t});\n\t// Set the width and height variables\n\tthis.setVariable(\"tv-bitmap-editor-width\",this.canvasDomNode.width + \"px\");\n\tthis.setVariable(\"tv-bitmap-editor-height\",this.canvasDomNode.height + \"px\");\n\t// Render toolbar child widgets\n\tthis.renderChildren(this.toolbarNode,null);\n\t// // Insert the elements into the DOM\n\tparent.insertBefore(this.canvasDomNode,nextSibling);\n\tthis.domNodes.push(this.canvasDomNode);\n\t// Load the image into the canvas\n\tif($tw.browser) {\n\t\tthis.loadCanvas();\n\t}\n\t// Add widget message listeners\n\tthis.addEventListeners([\n\t\t{type: \"tm-edit-bitmap-operation\", handler: \"handleEditBitmapOperationMessage\"}\n\t]);\n};\n\n/*\nHandle an edit bitmap operation message from the toolbar\n*/\nEditBitmapWidget.prototype.handleEditBitmapOperationMessage = function(event) {\n\t// Invoke the handler\n\tvar handler = this.editorOperations[event.param];\n\tif(handler) {\n\t\thandler.call(this,event);\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nEditBitmapWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.editTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nJust refresh the toolbar\n*/\nEditBitmapWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nSet the bitmap size variables and refresh the toolbar\n*/\nEditBitmapWidget.prototype.refreshToolbar = function() {\n\t// Set the width and height variables\n\tthis.setVariable(\"tv-bitmap-editor-width\",this.canvasDomNode.width + \"px\");\n\tthis.setVariable(\"tv-bitmap-editor-height\",this.canvasDomNode.height + \"px\");\n\t// Refresh each of our child widgets\n\t$tw.utils.each(this.children,function(childWidget) {\n\t\tchildWidget.refreshSelf();\n\t});\n};\n\nEditBitmapWidget.prototype.loadCanvas = function() {\n\tvar tiddler = this.wiki.getTiddler(this.editTitle),\n\t\tcurrImage = new Image();\n\t// Set up event handlers for loading the image\n\tvar self = this;\n\tcurrImage.onload = function() {\n\t\t// Copy the image to the on-screen canvas\n\t\tself.initCanvas(self.canvasDomNode,currImage.width,currImage.height,currImage);\n\t\t// And also copy the current bitmap to the off-screen canvas\n\t\tself.currCanvas = self.document.createElement(\"canvas\");\n\t\tself.initCanvas(self.currCanvas,currImage.width,currImage.height,currImage);\n\t\t// Set the width and height input boxes\n\t\tself.refreshToolbar();\n\t};\n\tcurrImage.onerror = function() {\n\t\t// Set the on-screen canvas size and clear it\n\t\tself.initCanvas(self.canvasDomNode,DEFAULT_IMAGE_WIDTH,DEFAULT_IMAGE_HEIGHT);\n\t\t// Set the off-screen canvas size and clear it\n\t\tself.currCanvas = self.document.createElement(\"canvas\");\n\t\tself.initCanvas(self.currCanvas,DEFAULT_IMAGE_WIDTH,DEFAULT_IMAGE_HEIGHT);\n\t\t// Set the width and height input boxes\n\t\tself.refreshToolbar();\n\t};\n\t// Get the current bitmap into an image object\n\tcurrImage.src = \"data:\" + tiddler.fields.type + \";base64,\" + tiddler.fields.text;\n};\n\nEditBitmapWidget.prototype.initCanvas = function(canvas,width,height,image) {\n\tcanvas.width = width;\n\tcanvas.height = height;\n\tvar ctx = canvas.getContext(\"2d\");\n\tif(image) {\n\t\tctx.drawImage(image,0,0);\n\t} else {\n\t\tctx.fillStyle = \"#fff\";\n\t\tctx.fillRect(0,0,canvas.width,canvas.height);\n\t}\n};\n\n/*\n** Change the size of the canvas, preserving the current image\n*/\nEditBitmapWidget.prototype.changeCanvasSize = function(newWidth,newHeight) {\n\t// Create and size a new canvas\n\tvar newCanvas = this.document.createElement(\"canvas\");\n\tthis.initCanvas(newCanvas,newWidth,newHeight);\n\t// Copy the old image\n\tvar ctx = newCanvas.getContext(\"2d\");\n\tctx.drawImage(this.currCanvas,0,0);\n\t// Set the new canvas as the current one\n\tthis.currCanvas = newCanvas;\n\t// Set the size of the onscreen canvas\n\tthis.canvasDomNode.width = newWidth;\n\tthis.canvasDomNode.height = newHeight;\n\t// Paint the onscreen canvas with the offscreen canvas\n\tctx = this.canvasDomNode.getContext(\"2d\");\n\tctx.drawImage(this.currCanvas,0,0);\n};\n\nEditBitmapWidget.prototype.handleTouchStartEvent = function(event) {\n\tthis.brushDown = true;\n\tthis.strokeStart(event.touches[0].clientX,event.touches[0].clientY);\n\tevent.preventDefault();\n\tevent.stopPropagation();\n\treturn false;\n};\n\nEditBitmapWidget.prototype.handleTouchMoveEvent = function(event) {\n\tif(this.brushDown) {\n\t\tthis.strokeMove(event.touches[0].clientX,event.touches[0].clientY);\n\t}\n\tevent.preventDefault();\n\tevent.stopPropagation();\n\treturn false;\n};\n\nEditBitmapWidget.prototype.handleTouchEndEvent = function(event) {\n\tif(this.brushDown) {\n\t\tthis.brushDown = false;\n\t\tthis.strokeEnd();\n\t}\n\tevent.preventDefault();\n\tevent.stopPropagation();\n\treturn false;\n};\n\nEditBitmapWidget.prototype.handleMouseDownEvent = function(event) {\n\tthis.strokeStart(event.clientX,event.clientY);\n\tthis.brushDown = true;\n\tevent.preventDefault();\n\tevent.stopPropagation();\n\treturn false;\n};\n\nEditBitmapWidget.prototype.handleMouseMoveEvent = function(event) {\n\tif(this.brushDown) {\n\t\tthis.strokeMove(event.clientX,event.clientY);\n\t\tevent.preventDefault();\n\t\tevent.stopPropagation();\n\t\treturn false;\n\t}\n\treturn true;\n};\n\nEditBitmapWidget.prototype.handleMouseUpEvent = function(event) {\n\tif(this.brushDown) {\n\t\tthis.brushDown = false;\n\t\tthis.strokeEnd();\n\t\tevent.preventDefault();\n\t\tevent.stopPropagation();\n\t\treturn false;\n\t}\n\treturn true;\n};\n\nEditBitmapWidget.prototype.adjustCoordinates = function(x,y) {\n\tvar canvasRect = this.canvasDomNode.getBoundingClientRect(),\n\t\tscale = this.canvasDomNode.width/canvasRect.width;\n\treturn {x: (x - canvasRect.left) * scale, y: (y - canvasRect.top) * scale};\n};\n\nEditBitmapWidget.prototype.strokeStart = function(x,y) {\n\t// Start off a new stroke\n\tthis.stroke = [this.adjustCoordinates(x,y)];\n};\n\nEditBitmapWidget.prototype.strokeMove = function(x,y) {\n\tvar ctx = this.canvasDomNode.getContext(\"2d\"),\n\t\tt;\n\t// Add the new position to the end of the stroke\n\tthis.stroke.push(this.adjustCoordinates(x,y));\n\t// Redraw the previous image\n\tctx.drawImage(this.currCanvas,0,0);\n\t// Render the stroke\n\tctx.globalAlpha = parseFloat(this.wiki.getTiddlerText(LINE_OPACITY_TITLE,\"1.0\"));\n\tctx.strokeStyle = this.wiki.getTiddlerText(LINE_COLOUR_TITLE,\"#ff0\");\n\tctx.lineWidth = parseFloat(this.wiki.getTiddlerText(LINE_WIDTH_TITLE,\"3\"));\n\tctx.lineCap = \"round\";\n\tctx.lineJoin = \"round\";\n\tctx.beginPath();\n\tctx.moveTo(this.stroke[0].x,this.stroke[0].y);\n\tfor(t=1; t<this.stroke.length-1; t++) {\n\t\tvar s1 = this.stroke[t],\n\t\t\ts2 = this.stroke[t-1],\n\t\t\ttx = (s1.x + s2.x)/2,\n\t\t\tty = (s1.y + s2.y)/2;\n\t\tctx.quadraticCurveTo(s2.x,s2.y,tx,ty);\n\t}\n\tctx.stroke();\n};\n\nEditBitmapWidget.prototype.strokeEnd = function() {\n\t// Copy the bitmap to the off-screen canvas\n\tvar ctx = this.currCanvas.getContext(\"2d\");\n\tctx.drawImage(this.canvasDomNode,0,0);\n\t// Save the image into the tiddler\n\tthis.saveChanges();\n};\n\nEditBitmapWidget.prototype.saveChanges = function() {\n\tvar tiddler = this.wiki.getTiddler(this.editTitle);\n\tif(tiddler) {\n\t\t// data URIs look like \"data:<type>;base64,<text>\"\n\t\tvar dataURL = this.canvasDomNode.toDataURL(tiddler.fields.type),\n\t\t\tposColon = dataURL.indexOf(\":\"),\n\t\t\tposSemiColon = dataURL.indexOf(\";\"),\n\t\t\tposComma = dataURL.indexOf(\",\"),\n\t\t\ttype = dataURL.substring(posColon+1,posSemiColon),\n\t\t\ttext = dataURL.substring(posComma+1);\n\t\tvar update = {type: type, text: text};\n\t\tthis.wiki.addTiddler(new $tw.Tiddler(this.wiki.getModificationFields(),tiddler,update,this.wiki.getCreationFields()));\n\t}\n};\n\nexports[\"edit-bitmap\"] = EditBitmapWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/edit-bitmap.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit-shortcut.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/edit-shortcut.js\ntype: application/javascript\nmodule-type: widget\n\nWidget to display an editable keyboard shortcut\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EditShortcutWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEditShortcutWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEditShortcutWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.inputNode = this.document.createElement(\"input\");\n\t// Assign classes\n\tif(this.shortcutClass) {\n\t\tthis.inputNode.className = this.shortcutClass;\t\t\n\t}\n\t// Assign other attributes\n\tif(this.shortcutStyle) {\n\t\tthis.inputNode.setAttribute(\"style\",this.shortcutStyle);\n\t}\n\tif(this.shortcutTooltip) {\n\t\tthis.inputNode.setAttribute(\"title\",this.shortcutTooltip);\n\t}\n\tif(this.shortcutPlaceholder) {\n\t\tthis.inputNode.setAttribute(\"placeholder\",this.shortcutPlaceholder);\n\t}\n\tif(this.shortcutAriaLabel) {\n\t\tthis.inputNode.setAttribute(\"aria-label\",this.shortcutAriaLabel);\n\t}\n\t// Assign the current shortcut\n\tthis.updateInputNode();\n\t// Add event handlers\n\t$tw.utils.addEventListeners(this.inputNode,[\n\t\t{name: \"keydown\", handlerObject: this, handlerMethod: \"handleKeydownEvent\"}\n\t]);\n\t// Link into the DOM\n\tparent.insertBefore(this.inputNode,nextSibling);\n\tthis.domNodes.push(this.inputNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nEditShortcutWidget.prototype.execute = function() {\n\tthis.shortcutTiddler = this.getAttribute(\"tiddler\");\n\tthis.shortcutField = this.getAttribute(\"field\");\n\tthis.shortcutIndex = this.getAttribute(\"index\");\n\tthis.shortcutPlaceholder = this.getAttribute(\"placeholder\");\n\tthis.shortcutDefault = this.getAttribute(\"default\",\"\");\n\tthis.shortcutClass = this.getAttribute(\"class\");\n\tthis.shortcutStyle = this.getAttribute(\"style\");\n\tthis.shortcutTooltip = this.getAttribute(\"tooltip\");\n\tthis.shortcutAriaLabel = this.getAttribute(\"aria-label\");\n};\n\n/*\nUpdate the value of the input node\n*/\nEditShortcutWidget.prototype.updateInputNode = function() {\n\tif(this.shortcutField) {\n\t\tvar tiddler = this.wiki.getTiddler(this.shortcutTiddler);\n\t\tif(tiddler && $tw.utils.hop(tiddler.fields,this.shortcutField)) {\n\t\t\tthis.inputNode.value = tiddler.getFieldString(this.shortcutField);\n\t\t} else {\n\t\t\tthis.inputNode.value = this.shortcutDefault;\n\t\t}\n\t} else if(this.shortcutIndex) {\n\t\tthis.inputNode.value = this.wiki.extractTiddlerDataItem(this.shortcutTiddler,this.shortcutIndex,this.shortcutDefault);\n\t} else {\n\t\tthis.inputNode.value = this.wiki.getTiddlerText(this.shortcutTiddler,this.shortcutDefault);\n\t}\n};\n\n/*\nHandle a dom \"keydown\" event\n*/\nEditShortcutWidget.prototype.handleKeydownEvent = function(event) {\n\t// Ignore shift, ctrl, meta, alt\n\tif(event.keyCode && $tw.keyboardManager.getModifierKeys().indexOf(event.keyCode) === -1) {\n\t\t// Get the shortcut text representation\n\t\tvar value = $tw.keyboardManager.getPrintableShortcuts([{\n\t\t\tctrlKey: event.ctrlKey,\n\t\t\tshiftKey: event.shiftKey,\n\t\t\taltKey: event.altKey,\n\t\t\tmetaKey: event.metaKey,\n\t\t\tkeyCode: event.keyCode\n\t\t}]);\n\t\tif(value.length > 0) {\n\t\t\tthis.wiki.setText(this.shortcutTiddler,this.shortcutField,this.shortcutIndex,value[0]);\n\t\t}\n\t\t// Ignore the keydown if it was already handled\n\t\tevent.preventDefault();\n\t\tevent.stopPropagation();\n\t\treturn true;\t\t\n\t} else {\n\t\treturn false;\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget needed re-rendering\n*/\nEditShortcutWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes.placeholder || changedAttributes[\"default\"] || changedAttributes[\"class\"] || changedAttributes.style || changedAttributes.tooltip || changedAttributes[\"aria-label\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else if(changedTiddlers[this.shortcutTiddler]) {\n\t\tthis.updateInputNode();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports[\"edit-shortcut\"] = EditShortcutWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/edit-shortcut.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit-text.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/edit-text.js\ntype: application/javascript\nmodule-type: widget\n\nEdit-text widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar editTextWidgetFactory = require(\"$:/core/modules/editor/factory.js\").editTextWidgetFactory,\n\tFramedEngine = require(\"$:/core/modules/editor/engines/framed.js\").FramedEngine,\n\tSimpleEngine = require(\"$:/core/modules/editor/engines/simple.js\").SimpleEngine;\n\nexports[\"edit-text\"] = editTextWidgetFactory(FramedEngine,SimpleEngine);\n\n})();\n",
"title": "$:/core/modules/widgets/edit-text.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/edit.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/edit.js\ntype: application/javascript\nmodule-type: widget\n\nEdit widget is a meta-widget chooses the appropriate actual editting widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EditWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEditWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEditWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n// Mappings from content type to editor type are stored in tiddlers with this prefix\nvar EDITOR_MAPPING_PREFIX = \"$:/config/EditorTypeMappings/\";\n\n/*\nCompute the internal state of the widget\n*/\nEditWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.editTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.editField = this.getAttribute(\"field\",\"text\");\n\tthis.editIndex = this.getAttribute(\"index\");\n\tthis.editClass = this.getAttribute(\"class\");\n\tthis.editPlaceholder = this.getAttribute(\"placeholder\");\n\t// Choose the appropriate edit widget\n\tthis.editorType = this.getEditorType();\n\t// Make the child widgets\n\tthis.makeChildWidgets([{\n\t\ttype: \"edit-\" + this.editorType,\n\t\tattributes: {\n\t\t\ttiddler: {type: \"string\", value: this.editTitle},\n\t\t\tfield: {type: \"string\", value: this.editField},\n\t\t\tindex: {type: \"string\", value: this.editIndex},\n\t\t\t\"class\": {type: \"string\", value: this.editClass},\n\t\t\t\"placeholder\": {type: \"string\", value: this.editPlaceholder}\n\t\t},\n\t\tchildren: this.parseTreeNode.children\n\t}]);\n};\n\nEditWidget.prototype.getEditorType = function() {\n\t// Get the content type of the thing we're editing\n\tvar type;\n\tif(this.editField === \"text\") {\n\t\tvar tiddler = this.wiki.getTiddler(this.editTitle);\n\t\tif(tiddler) {\n\t\t\ttype = tiddler.fields.type;\n\t\t}\n\t}\n\ttype = type || \"text/vnd.tiddlywiki\";\n\tvar editorType = this.wiki.getTiddlerText(EDITOR_MAPPING_PREFIX + type);\n\tif(!editorType) {\n\t\tvar typeInfo = $tw.config.contentTypeInfo[type];\n\t\tif(typeInfo && typeInfo.encoding === \"base64\") {\n\t\t\teditorType = \"binary\";\n\t\t} else {\n\t\t\teditorType = \"text\";\n\t\t}\n\t}\n\treturn editorType;\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nEditWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\t// Refresh if an attribute has changed, or the type associated with the target tiddler has changed\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || (changedTiddlers[this.editTitle] && this.getEditorType() !== this.editorType)) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nexports.edit = EditWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/edit.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/element.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/element.js\ntype: application/javascript\nmodule-type: widget\n\nElement widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ElementWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nElementWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nElementWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Neuter blacklisted elements\n\tvar tag = this.parseTreeNode.tag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"safe-\" + tag;\n\t}\n\tvar domNode = this.document.createElementNS(this.namespace,tag);\n\tthis.assignAttributes(domNode,{excludeEventAttributes: true});\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nElementWidget.prototype.execute = function() {\n\t// Select the namespace for the tag\n\tvar tagNamespaces = {\n\t\t\tsvg: \"http://www.w3.org/2000/svg\",\n\t\t\tmath: \"http://www.w3.org/1998/Math/MathML\",\n\t\t\tbody: \"http://www.w3.org/1999/xhtml\"\n\t\t};\n\tthis.namespace = tagNamespaces[this.parseTreeNode.tag];\n\tif(this.namespace) {\n\t\tthis.setVariable(\"namespace\",this.namespace);\n\t} else {\n\t\tthis.namespace = this.getVariable(\"namespace\",{defaultValue: \"http://www.w3.org/1999/xhtml\"});\n\t}\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nElementWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes(),\n\t\thasChangedAttributes = $tw.utils.count(changedAttributes) > 0;\n\tif(hasChangedAttributes) {\n\t\t// Update our attributes\n\t\tthis.assignAttributes(this.domNodes[0],{excludeEventAttributes: true});\n\t}\n\treturn this.refreshChildren(changedTiddlers) || hasChangedAttributes;\n};\n\nexports.element = ElementWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/element.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/encrypt.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/encrypt.js\ntype: application/javascript\nmodule-type: widget\n\nEncrypt widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EncryptWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEncryptWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEncryptWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar textNode = this.document.createTextNode(this.encryptedText);\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nEncryptWidget.prototype.execute = function() {\n\t// Get parameters from our attributes\n\tthis.filter = this.getAttribute(\"filter\",\"[!is[system]]\");\n\t// Encrypt the filtered tiddlers\n\tvar tiddlers = this.wiki.filterTiddlers(this.filter),\n\t\tjson = {},\n\t\tself = this;\n\t$tw.utils.each(tiddlers,function(title) {\n\t\tvar tiddler = self.wiki.getTiddler(title),\n\t\t\tjsonTiddler = {};\n\t\tfor(var f in tiddler.fields) {\n\t\t\tjsonTiddler[f] = tiddler.getFieldString(f);\n\t\t}\n\t\tjson[title] = jsonTiddler;\n\t});\n\tthis.encryptedText = $tw.utils.htmlEncode($tw.crypto.encrypt(JSON.stringify(json)));\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nEncryptWidget.prototype.refresh = function(changedTiddlers) {\n\t// We don't need to worry about refreshing because the encrypt widget isn't for interactive use\n\treturn false;\n};\n\nexports.encrypt = EncryptWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/encrypt.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/entity.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/entity.js\ntype: application/javascript\nmodule-type: widget\n\nHTML entity widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar EntityWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nEntityWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nEntityWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.execute();\n\tvar entityString = this.getAttribute(\"entity\",this.parseTreeNode.entity || \"\"),\n\t\ttextNode = this.document.createTextNode($tw.utils.entityDecode(entityString));\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nEntityWidget.prototype.execute = function() {\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nEntityWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.entity) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports.entity = EntityWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/entity.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/fieldmangler.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/fieldmangler.js\ntype: application/javascript\nmodule-type: widget\n\nField mangler widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar FieldManglerWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n\tthis.addEventListeners([\n\t\t{type: \"tm-remove-field\", handler: \"handleRemoveFieldEvent\"},\n\t\t{type: \"tm-add-field\", handler: \"handleAddFieldEvent\"},\n\t\t{type: \"tm-remove-tag\", handler: \"handleRemoveTagEvent\"},\n\t\t{type: \"tm-add-tag\", handler: \"handleAddTagEvent\"}\n\t]);\n};\n\n/*\nInherit from the base widget class\n*/\nFieldManglerWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nFieldManglerWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nFieldManglerWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.mangleTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nFieldManglerWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nFieldManglerWidget.prototype.handleRemoveFieldEvent = function(event) {\n\tvar tiddler = this.wiki.getTiddler(this.mangleTitle),\n\t\tdeletion = {};\n\tdeletion[event.param] = undefined;\n\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,deletion));\n\treturn true;\n};\n\nFieldManglerWidget.prototype.handleAddFieldEvent = function(event) {\n\tvar tiddler = this.wiki.getTiddler(this.mangleTitle),\n\t\taddition = this.wiki.getModificationFields(),\n\t\thadInvalidFieldName = false,\n\t\taddField = function(name,value) {\n\t\t\tvar trimmedName = name.toLowerCase().trim();\n\t\t\tif(!$tw.utils.isValidFieldName(trimmedName)) {\n\t\t\t\tif(!hadInvalidFieldName) {\n\t\t\t\t\talert($tw.language.getString(\n\t\t\t\t\t\t\"InvalidFieldName\",\n\t\t\t\t\t\t{variables:\n\t\t\t\t\t\t\t{fieldName: trimmedName}\n\t\t\t\t\t\t}\n\t\t\t\t\t));\n\t\t\t\t\thadInvalidFieldName = true;\n\t\t\t\t\treturn;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\tif(!value && tiddler) {\n\t\t\t\t\tvalue = tiddler.fields[trimmedName];\n\t\t\t\t}\n\t\t\t\taddition[trimmedName] = value || \"\";\n\t\t\t}\n\t\t\treturn;\n\t\t};\n\taddition.title = this.mangleTitle;\n\tif(typeof event.param === \"string\") {\n\t\taddField(event.param,\"\");\n\t}\n\tif(typeof event.paramObject === \"object\") {\n\t\tfor(var name in event.paramObject) {\n\t\t\taddField(name,event.paramObject[name]);\n\t\t}\n\t}\n\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,addition));\n\treturn true;\n};\n\nFieldManglerWidget.prototype.handleRemoveTagEvent = function(event) {\n\tvar tiddler = this.wiki.getTiddler(this.mangleTitle);\n\tif(tiddler && tiddler.fields.tags) {\n\t\tvar p = tiddler.fields.tags.indexOf(event.param);\n\t\tif(p !== -1) {\n\t\t\tvar modification = this.wiki.getModificationFields();\n\t\t\tmodification.tags = (tiddler.fields.tags || []).slice(0);\n\t\t\tmodification.tags.splice(p,1);\n\t\t\tif(modification.tags.length === 0) {\n\t\t\t\tmodification.tags = undefined;\n\t\t\t}\n\t\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,modification));\n\t\t}\n\t}\n\treturn true;\n};\n\nFieldManglerWidget.prototype.handleAddTagEvent = function(event) {\n\tvar tiddler = this.wiki.getTiddler(this.mangleTitle);\n\tif(tiddler && typeof event.param === \"string\") {\n\t\tvar tag = event.param.trim();\n\t\tif(tag !== \"\") {\n\t\t\tvar modification = this.wiki.getModificationFields();\n\t\t\tmodification.tags = (tiddler.fields.tags || []).slice(0);\n\t\t\t$tw.utils.pushTop(modification.tags,tag);\n\t\t\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,modification));\t\t\t\n\t\t}\n\t} else if(typeof event.param === \"string\" && event.param.trim() !== \"\" && this.mangleTitle.trim() !== \"\") {\n\t\tvar tag = [];\n\t\ttag.push(event.param.trim());\n\t\tthis.wiki.addTiddler({title: this.mangleTitle, tags: tag});\t\t\n\t}\n\treturn true;\n};\n\nexports.fieldmangler = FieldManglerWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/fieldmangler.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/fields.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/fields.js\ntype: application/javascript\nmodule-type: widget\n\nFields widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar FieldsWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nFieldsWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nFieldsWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar textNode = this.document.createTextNode(this.text);\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nFieldsWidget.prototype.execute = function() {\n\t// Get parameters from our attributes\n\tthis.tiddlerTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.template = this.getAttribute(\"template\");\n\tthis.exclude = this.getAttribute(\"exclude\");\n\tthis.stripTitlePrefix = this.getAttribute(\"stripTitlePrefix\",\"no\") === \"yes\";\n\t// Get the value to display\n\tvar tiddler = this.wiki.getTiddler(this.tiddlerTitle);\n\t// Get the exclusion list\n\tvar exclude;\n\tif(this.exclude) {\n\t\texclude = this.exclude.split(\" \");\n\t} else {\n\t\texclude = [\"text\"]; \n\t}\n\t// Compose the template\n\tvar text = [];\n\tif(this.template && tiddler) {\n\t\tvar fields = [];\n\t\tfor(var fieldName in tiddler.fields) {\n\t\t\tif(exclude.indexOf(fieldName) === -1) {\n\t\t\t\tfields.push(fieldName);\n\t\t\t}\n\t\t}\n\t\tfields.sort();\n\t\tfor(var f=0; f<fields.length; f++) {\n\t\t\tfieldName = fields[f];\n\t\t\tif(exclude.indexOf(fieldName) === -1) {\n\t\t\t\tvar row = this.template,\n\t\t\t\t\tvalue = tiddler.getFieldString(fieldName);\n\t\t\t\tif(this.stripTitlePrefix && fieldName === \"title\") {\n\t\t\t\t\tvar reStrip = /^\\{[^\\}]+\\}(.+)/mg,\n\t\t\t\t\t\treMatch = reStrip.exec(value);\n\t\t\t\t\tif(reMatch) {\n\t\t\t\t\t\tvalue = reMatch[1];\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\trow = $tw.utils.replaceString(row,\"$name$\",fieldName);\n\t\t\t\trow = $tw.utils.replaceString(row,\"$value$\",value);\n\t\t\t\trow = $tw.utils.replaceString(row,\"$encoded_value$\",$tw.utils.htmlEncode(value));\n\t\t\t\ttext.push(row);\n\t\t\t}\n\t\t}\n\t}\n\tthis.text = text.join(\"\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nFieldsWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.template || changedAttributes.exclude || changedAttributes.stripTitlePrefix || changedTiddlers[this.tiddlerTitle]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports.fields = FieldsWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/fields.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/image.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/image.js\ntype: application/javascript\nmodule-type: widget\n\nThe image widget displays an image referenced with an external URI or with a local tiddler title.\n\n```\n<$image src=\"TiddlerTitle\" width=\"320\" height=\"400\" class=\"classnames\">\n```\n\nThe image source can be the title of an existing tiddler or the URL of an external image.\n\nExternal images always generate an HTML `<img>` tag.\n\nTiddlers that have a _canonical_uri field generate an HTML `<img>` tag with the src attribute containing the URI.\n\nTiddlers that contain image data generate an HTML `<img>` tag with the src attribute containing a base64 representation of the image.\n\nTiddlers that contain wikitext could be rendered to a DIV of the usual size of a tiddler, and then transformed to the size requested.\n\nThe width and height attributes are interpreted as a number of pixels, and do not need to include the \"px\" suffix.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ImageWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nImageWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nImageWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create element\n\t// Determine what type of image it is\n\tvar tag = \"img\", src = \"\",\n\t\ttiddler = this.wiki.getTiddler(this.imageSource);\n\tif(!tiddler) {\n\t\t// The source isn't the title of a tiddler, so we'll assume it's a URL\n\t\tsrc = this.getVariable(\"tv-get-export-image-link\",{params: [{name: \"src\",value: this.imageSource}],defaultValue: this.imageSource});\n\t} else {\n\t\t// Check if it is an image tiddler\n\t\tif(this.wiki.isImageTiddler(this.imageSource)) {\n\t\t\tvar type = tiddler.fields.type,\n\t\t\t\ttext = tiddler.fields.text,\n\t\t\t\t_canonical_uri = tiddler.fields._canonical_uri;\n\t\t\t// If the tiddler has body text then it doesn't need to be lazily loaded\n\t\t\tif(text) {\n\t\t\t\t// Render the appropriate element for the image type\n\t\t\t\tswitch(type) {\n\t\t\t\t\tcase \"application/pdf\":\n\t\t\t\t\t\ttag = \"embed\";\n\t\t\t\t\t\tsrc = \"data:application/pdf;base64,\" + text;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"image/svg+xml\":\n\t\t\t\t\t\tsrc = \"data:image/svg+xml,\" + encodeURIComponent(text);\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tdefault:\n\t\t\t\t\t\tsrc = \"data:\" + type + \";base64,\" + text;\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t} else if(_canonical_uri) {\n\t\t\t\tswitch(type) {\n\t\t\t\t\tcase \"application/pdf\":\n\t\t\t\t\t\ttag = \"embed\";\n\t\t\t\t\t\tsrc = _canonical_uri;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tcase \"image/svg+xml\":\n\t\t\t\t\t\tsrc = _canonical_uri;\n\t\t\t\t\t\tbreak;\n\t\t\t\t\tdefault:\n\t\t\t\t\t\tsrc = _canonical_uri;\n\t\t\t\t\t\tbreak;\n\t\t\t\t}\t\n\t\t\t} else {\n\t\t\t\t// Just trigger loading of the tiddler\n\t\t\t\tthis.wiki.getTiddlerText(this.imageSource);\n\t\t\t}\n\t\t}\n\t}\n\t// Create the element and assign the attributes\n\tvar domNode = this.document.createElement(tag);\n\tdomNode.setAttribute(\"src\",src);\n\tif(this.imageClass) {\n\t\tdomNode.setAttribute(\"class\",this.imageClass);\t\t\n\t}\n\tif(this.imageWidth) {\n\t\tdomNode.setAttribute(\"width\",this.imageWidth);\n\t}\n\tif(this.imageHeight) {\n\t\tdomNode.setAttribute(\"height\",this.imageHeight);\n\t}\n\tif(this.imageTooltip) {\n\t\tdomNode.setAttribute(\"title\",this.imageTooltip);\t\t\n\t}\n\tif(this.imageAlt) {\n\t\tdomNode.setAttribute(\"alt\",this.imageAlt);\t\t\n\t}\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.domNodes.push(domNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nImageWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.imageSource = this.getAttribute(\"source\");\n\tthis.imageWidth = this.getAttribute(\"width\");\n\tthis.imageHeight = this.getAttribute(\"height\");\n\tthis.imageClass = this.getAttribute(\"class\");\n\tthis.imageTooltip = this.getAttribute(\"tooltip\");\n\tthis.imageAlt = this.getAttribute(\"alt\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nImageWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.source || changedAttributes.width || changedAttributes.height || changedAttributes[\"class\"] || changedAttributes.tooltip || changedTiddlers[this.imageSource]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\t\n\t}\n};\n\nexports.image = ImageWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/image.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/importvariables.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/importvariables.js\ntype: application/javascript\nmodule-type: widget\n\nImport variable definitions from other tiddlers\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ImportVariablesWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nImportVariablesWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nImportVariablesWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nImportVariablesWidget.prototype.execute = function(tiddlerList) {\n\tvar self = this;\n\t// Get our parameters\n\tthis.filter = this.getAttribute(\"filter\");\n\t// Compute the filter\n\tthis.tiddlerList = tiddlerList || this.wiki.filterTiddlers(this.filter,this);\n\t// Accumulate the <$set> widgets from each tiddler\n\tvar widgetStackStart,widgetStackEnd;\n\tfunction addWidgetNode(widgetNode) {\n\t\tif(widgetNode) {\n\t\t\tif(!widgetStackStart && !widgetStackEnd) {\n\t\t\t\twidgetStackStart = widgetNode;\n\t\t\t\twidgetStackEnd = widgetNode;\n\t\t\t} else {\n\t\t\t\twidgetStackEnd.children = [widgetNode];\n\t\t\t\twidgetStackEnd = widgetNode;\n\t\t\t}\n\t\t}\n\t}\n\t$tw.utils.each(this.tiddlerList,function(title) {\n\t\tvar parser = self.wiki.parseTiddler(title);\n\t\tif(parser) {\n\t\t\tvar parseTreeNode = parser.tree[0];\n\t\t\twhile(parseTreeNode && parseTreeNode.type === \"set\") {\n\t\t\t\taddWidgetNode({\n\t\t\t\t\ttype: \"set\",\n\t\t\t\t\tattributes: parseTreeNode.attributes,\n\t\t\t\t\tparams: parseTreeNode.params\n\t\t\t\t});\n\t\t\t\tparseTreeNode = parseTreeNode.children[0];\n\t\t\t}\n\t\t} \n\t});\n\t// Add our own children to the end of the pile\n\tvar parseTreeNodes;\n\tif(widgetStackStart && widgetStackEnd) {\n\t\tparseTreeNodes = [widgetStackStart];\n\t\twidgetStackEnd.children = this.parseTreeNode.children;\n\t} else {\n\t\tparseTreeNodes = this.parseTreeNode.children;\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets(parseTreeNodes);\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nImportVariablesWidget.prototype.refresh = function(changedTiddlers) {\n\t// Recompute our attributes and the filter list\n\tvar changedAttributes = this.computeAttributes(),\n\t\ttiddlerList = this.wiki.filterTiddlers(this.getAttribute(\"filter\"),this);\n\t// Refresh if the filter has changed, or the list of tiddlers has changed, or any of the tiddlers in the list has changed\n\tfunction haveListedTiddlersChanged() {\n\t\tvar changed = false;\n\t\ttiddlerList.forEach(function(title) {\n\t\t\tif(changedTiddlers[title]) {\n\t\t\t\tchanged = true;\n\t\t\t}\n\t\t});\n\t\treturn changed;\n\t}\n\tif(changedAttributes.filter || !$tw.utils.isArrayEqual(this.tiddlerList,tiddlerList) || haveListedTiddlersChanged()) {\n\t\t// Compute the filter\n\t\tthis.removeChildDomNodes();\n\t\tthis.execute(tiddlerList);\n\t\tthis.renderChildren(this.parentDomNode,this.findNextSiblingDomNode());\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nexports.importvariables = ImportVariablesWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/importvariables.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/keyboard.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/keyboard.js\ntype: application/javascript\nmodule-type: widget\n\nKeyboard shortcut widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar KeyboardWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nKeyboardWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nKeyboardWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar tag = this.parseTreeNode.isBlock ? \"div\" : \"span\";\n\tif(this.tag && $tw.config.htmlUnsafeElements.indexOf(this.tag) === -1) {\n\t\ttag = this.tag;\n\t}\n\t// Create element\n\tvar domNode = this.document.createElement(tag);\n\t// Assign classes\n\tvar classes = (this[\"class\"] || \"\").split(\" \");\n\tclasses.push(\"tc-keyboard\");\n\tdomNode.className = classes.join(\" \");\n\t// Add a keyboard event handler\n\tdomNode.addEventListener(\"keydown\",function (event) {\n\t\tif($tw.keyboardManager.checkKeyDescriptors(event,self.keyInfoArray)) {\n\t\t\tself.invokeActions(self,event);\n\t\t\tif(self.actions) {\n\t\t\t\tself.invokeActionString(self.actions,self,event);\n\t\t\t}\n\t\t\tself.dispatchMessage(event);\n\t\t\tevent.preventDefault();\n\t\t\tevent.stopPropagation();\n\t\t\treturn true;\n\t\t}\n\t\treturn false;\n\t},false);\n\t// Insert element\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\nKeyboardWidget.prototype.dispatchMessage = function(event) {\n\tthis.dispatchEvent({type: this.message, param: this.param, tiddlerTitle: this.getVariable(\"currentTiddler\")});\n};\n\n/*\nCompute the internal state of the widget\n*/\nKeyboardWidget.prototype.execute = function() {\n\t// Get attributes\n\tthis.actions = this.getAttribute(\"actions\");\n\tthis.message = this.getAttribute(\"message\");\n\tthis.param = this.getAttribute(\"param\");\n\tthis.key = this.getAttribute(\"key\");\n\tthis.tag = this.getAttribute(\"tag\");\n\tthis.keyInfoArray = $tw.keyboardManager.parseKeyDescriptors(this.key);\n\tthis[\"class\"] = this.getAttribute(\"class\");\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nKeyboardWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.message || changedAttributes.param || changedAttributes.key || changedAttributes[\"class\"] || changedAttributes.tag) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.keyboard = KeyboardWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/keyboard.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/link.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/link.js\ntype: application/javascript\nmodule-type: widget\n\nLink widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\nvar MISSING_LINK_CONFIG_TITLE = \"$:/config/MissingLinks\";\n\nvar LinkWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nLinkWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nLinkWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Get the value of the tv-wikilinks configuration macro\n\tvar wikiLinksMacro = this.getVariable(\"tv-wikilinks\"),\n\t\tuseWikiLinks = wikiLinksMacro ? (wikiLinksMacro.trim() !== \"no\") : true,\n\t\tmissingLinksEnabled = !(this.hideMissingLinks && this.isMissing && !this.isShadow);\n\t// Render the link if required\n\tif(useWikiLinks && missingLinksEnabled) {\n\t\tthis.renderLink(parent,nextSibling);\n\t} else {\n\t\t// Just insert the link text\n\t\tvar domNode = this.document.createElement(\"span\");\n\t\tparent.insertBefore(domNode,nextSibling);\n\t\tthis.renderChildren(domNode,null);\n\t\tthis.domNodes.push(domNode);\n\t}\n};\n\n/*\nRender this widget into the DOM\n*/\nLinkWidget.prototype.renderLink = function(parent,nextSibling) {\n\tvar self = this;\n\t// Sanitise the specified tag\n\tvar tag = this.linkTag;\n\tif($tw.config.htmlUnsafeElements.indexOf(tag) !== -1) {\n\t\ttag = \"a\";\n\t}\n\t// Create our element\n\tvar domNode = this.document.createElement(tag);\n\t// Assign classes\n\tvar classes = [];\n\tif(this.linkClasses) {\n\t\tclasses.push(this.linkClasses);\n\t}\n\tclasses.push(\"tc-tiddlylink\");\n\tif(this.isShadow) {\n\t\tclasses.push(\"tc-tiddlylink-shadow\");\n\t}\n\tif(this.isMissing && !this.isShadow) {\n\t\tclasses.push(\"tc-tiddlylink-missing\");\n\t} else {\n\t\tif(!this.isMissing) {\n\t\t\tclasses.push(\"tc-tiddlylink-resolves\");\n\t\t}\n\t}\n\tdomNode.setAttribute(\"class\",classes.join(\" \"));\n\t// Set an href\n\tvar wikiLinkTemplateMacro = this.getVariable(\"tv-wikilink-template\"),\n\t\twikiLinkTemplate = wikiLinkTemplateMacro ? wikiLinkTemplateMacro.trim() : \"#$uri_encoded$\",\n\t\twikiLinkText = $tw.utils.replaceString(wikiLinkTemplate,\"$uri_encoded$\",encodeURIComponent(this.to));\n\twikiLinkText = $tw.utils.replaceString(wikiLinkText,\"$uri_doubleencoded$\",encodeURIComponent(encodeURIComponent(this.to)));\n\twikiLinkText = this.getVariable(\"tv-get-export-link\",{params: [{name: \"to\",value: this.to}],defaultValue: wikiLinkText});\n\tif(tag === \"a\") {\n\t\tdomNode.setAttribute(\"href\",wikiLinkText);\n\t}\n\tif(this.tabIndex) {\n\t\tdomNode.setAttribute(\"tabindex\",this.tabIndex);\n\t}\n\t// Set the tooltip\n\t// HACK: Performance issues with re-parsing the tooltip prevent us defaulting the tooltip to \"<$transclude field='tooltip'><$transclude field='title'/></$transclude>\"\n\tvar tooltipWikiText = this.tooltip || this.getVariable(\"tv-wikilink-tooltip\");\n\tif(tooltipWikiText) {\n\t\tvar tooltipText = this.wiki.renderText(\"text/plain\",\"text/vnd.tiddlywiki\",tooltipWikiText,{\n\t\t\t\tparseAsInline: true,\n\t\t\t\tvariables: {\n\t\t\t\t\tcurrentTiddler: this.to\n\t\t\t\t},\n\t\t\t\tparentWidget: this\n\t\t\t});\n\t\tdomNode.setAttribute(\"title\",tooltipText);\n\t}\n\tif(this[\"aria-label\"]) {\n\t\tdomNode.setAttribute(\"aria-label\",this[\"aria-label\"]);\n\t}\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(domNode,[\n\t\t{name: \"click\", handlerObject: this, handlerMethod: \"handleClickEvent\"},\n\t]);\n\t// Make the link draggable if required\n\tif(this.draggable === \"yes\") {\n\t\t$tw.utils.makeDraggable({\n\t\t\tdomNode: domNode,\n\t\t\tdragTiddlerFn: function() {return self.to;},\n\t\t\twidget: this\n\t\t});\n\t}\n\t// Insert the link into the DOM and render any children\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\nLinkWidget.prototype.handleClickEvent = function(event) {\n\t// Send the click on its way as a navigate event\n\tvar bounds = this.domNodes[0].getBoundingClientRect();\n\tthis.dispatchEvent({\n\t\ttype: \"tm-navigate\",\n\t\tnavigateTo: this.to,\n\t\tnavigateFromTitle: this.getVariable(\"storyTiddler\"),\n\t\tnavigateFromNode: this,\n\t\tnavigateFromClientRect: { top: bounds.top, left: bounds.left, width: bounds.width, right: bounds.right, bottom: bounds.bottom, height: bounds.height\n\t\t},\n\t\tnavigateSuppressNavigation: event.metaKey || event.ctrlKey || (event.button === 1)\n\t});\n\tif(this.domNodes[0].hasAttribute(\"href\")) {\n\t\tevent.preventDefault();\n\t}\n\tevent.stopPropagation();\n\treturn false;\n};\n\n/*\nCompute the internal state of the widget\n*/\nLinkWidget.prototype.execute = function() {\n\t// Pick up our attributes\n\tthis.to = this.getAttribute(\"to\",this.getVariable(\"currentTiddler\"));\n\tthis.tooltip = this.getAttribute(\"tooltip\");\n\tthis[\"aria-label\"] = this.getAttribute(\"aria-label\");\n\tthis.linkClasses = this.getAttribute(\"class\");\n\tthis.tabIndex = this.getAttribute(\"tabindex\");\n\tthis.draggable = this.getAttribute(\"draggable\",\"yes\");\n\tthis.linkTag = this.getAttribute(\"tag\",\"a\");\n\t// Determine the link characteristics\n\tthis.isMissing = !this.wiki.tiddlerExists(this.to);\n\tthis.isShadow = this.wiki.isShadowTiddler(this.to);\n\tthis.hideMissingLinks = ($tw.wiki.getTiddlerText(MISSING_LINK_CONFIG_TITLE,\"yes\") === \"no\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nLinkWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.to || changedTiddlers[this.to] || changedAttributes[\"aria-label\"] || changedAttributes.tooltip || changedTiddlers[MISSING_LINK_CONFIG_TITLE]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.link = LinkWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/link.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/linkcatcher.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/linkcatcher.js\ntype: application/javascript\nmodule-type: widget\n\nLinkcatcher widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar LinkCatcherWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n\tthis.addEventListeners([\n\t\t{type: \"tm-navigate\", handler: \"handleNavigateEvent\"}\n\t]);\n};\n\n/*\nInherit from the base widget class\n*/\nLinkCatcherWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nLinkCatcherWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nLinkCatcherWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.catchTo = this.getAttribute(\"to\");\n\tthis.catchMessage = this.getAttribute(\"message\");\n\tthis.catchSet = this.getAttribute(\"set\");\n\tthis.catchSetTo = this.getAttribute(\"setTo\");\n\tthis.catchActions = this.getAttribute(\"actions\");\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nLinkCatcherWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.to || changedAttributes.message || changedAttributes.set || changedAttributes.setTo) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\n/*\nHandle a tm-navigate event\n*/\nLinkCatcherWidget.prototype.handleNavigateEvent = function(event) {\n\tif(this.catchTo) {\n\t\tthis.wiki.setTextReference(this.catchTo,event.navigateTo,this.getVariable(\"currentTiddler\"));\n\t}\n\tif(this.catchMessage && this.parentWidget) {\n\t\tthis.parentWidget.dispatchEvent({\n\t\t\ttype: this.catchMessage,\n\t\t\tparam: event.navigateTo,\n\t\t\tnavigateTo: event.navigateTo\n\t\t});\n\t}\n\tif(this.catchSet) {\n\t\tvar tiddler = this.wiki.getTiddler(this.catchSet);\n\t\tthis.wiki.addTiddler(new $tw.Tiddler(tiddler,{title: this.catchSet, text: this.catchSetTo}));\n\t}\n\tif(this.catchActions) {\n\t\tthis.invokeActionString(this.catchActions,this);\n\t}\n\treturn false;\n};\n\nexports.linkcatcher = LinkCatcherWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/linkcatcher.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/list.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/list.js\ntype: application/javascript\nmodule-type: widget\n\nList and list item widgets\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\n/*\nThe list widget creates list element sub-widgets that reach back into the list widget for their configuration\n*/\n\nvar ListWidget = function(parseTreeNode,options) {\n\t// Initialise the storyviews if they've not been done already\n\tif(!this.storyViews) {\n\t\tListWidget.prototype.storyViews = {};\n\t\t$tw.modules.applyMethods(\"storyview\",this.storyViews);\n\t}\n\t// Main initialisation inherited from widget.js\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nListWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nListWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n\t// Construct the storyview\n\tvar StoryView = this.storyViews[this.storyViewName];\n\tif(this.storyViewName && !StoryView) {\n\t\tStoryView = this.storyViews[\"classic\"];\n\t}\n\tif(StoryView && !this.document.isTiddlyWikiFakeDom) {\n\t\tthis.storyview = new StoryView(this);\n\t} else {\n\t\tthis.storyview = null;\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nListWidget.prototype.execute = function() {\n\t// Get our attributes\n\tthis.template = this.getAttribute(\"template\");\n\tthis.editTemplate = this.getAttribute(\"editTemplate\");\n\tthis.variableName = this.getAttribute(\"variable\",\"currentTiddler\");\n\tthis.storyViewName = this.getAttribute(\"storyview\");\n\tthis.historyTitle = this.getAttribute(\"history\");\n\t// Compose the list elements\n\tthis.list = this.getTiddlerList();\n\tvar members = [],\n\t\tself = this;\n\t// Check for an empty list\n\tif(this.list.length === 0) {\n\t\tmembers = this.getEmptyMessage();\n\t} else {\n\t\t$tw.utils.each(this.list,function(title,index) {\n\t\t\tmembers.push(self.makeItemTemplate(title));\n\t\t});\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets(members);\n\t// Clear the last history\n\tthis.history = [];\n};\n\nListWidget.prototype.getTiddlerList = function() {\n\tvar defaultFilter = \"[!is[system]sort[title]]\";\n\treturn this.wiki.filterTiddlers(this.getAttribute(\"filter\",defaultFilter),this);\n};\n\nListWidget.prototype.getEmptyMessage = function() {\n\tvar emptyMessage = this.getAttribute(\"emptyMessage\",\"\"),\n\t\tparser = this.wiki.parseText(\"text/vnd.tiddlywiki\",emptyMessage,{parseAsInline: true});\n\tif(parser) {\n\t\treturn parser.tree;\n\t} else {\n\t\treturn [];\n\t}\n};\n\n/*\nCompose the template for a list item\n*/\nListWidget.prototype.makeItemTemplate = function(title) {\n\t// Check if the tiddler is a draft\n\tvar tiddler = this.wiki.getTiddler(title),\n\t\tisDraft = tiddler && tiddler.hasField(\"draft.of\"),\n\t\ttemplate = this.template,\n\t\ttemplateTree;\n\tif(isDraft && this.editTemplate) {\n\t\ttemplate = this.editTemplate;\n\t}\n\t// Compose the transclusion of the template\n\tif(template) {\n\t\ttemplateTree = [{type: \"transclude\", attributes: {tiddler: {type: \"string\", value: template}}}];\n\t} else {\n\t\tif(this.parseTreeNode.children && this.parseTreeNode.children.length > 0) {\n\t\t\ttemplateTree = this.parseTreeNode.children;\n\t\t} else {\n\t\t\t// Default template is a link to the title\n\t\t\ttemplateTree = [{type: \"element\", tag: this.parseTreeNode.isBlock ? \"div\" : \"span\", children: [{type: \"link\", attributes: {to: {type: \"string\", value: title}}, children: [\n\t\t\t\t\t{type: \"text\", text: title}\n\t\t\t]}]}];\n\t\t}\n\t}\n\t// Return the list item\n\treturn {type: \"listitem\", itemTitle: title, variableName: this.variableName, children: templateTree};\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nListWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes(),\n\t\tresult;\n\t// Call the storyview\n\tif(this.storyview && this.storyview.refreshStart) {\n\t\tthis.storyview.refreshStart(changedTiddlers,changedAttributes);\n\t}\n\t// Completely refresh if any of our attributes have changed\n\tif(changedAttributes.filter || changedAttributes.template || changedAttributes.editTemplate || changedAttributes.emptyMessage || changedAttributes.storyview || changedAttributes.history) {\n\t\tthis.refreshSelf();\n\t\tresult = true;\n\t} else {\n\t\t// Handle any changes to the list\n\t\tresult = this.handleListChanges(changedTiddlers);\n\t\t// Handle any changes to the history stack\n\t\tif(this.historyTitle && changedTiddlers[this.historyTitle]) {\n\t\t\tthis.handleHistoryChanges();\n\t\t}\n\t}\n\t// Call the storyview\n\tif(this.storyview && this.storyview.refreshEnd) {\n\t\tthis.storyview.refreshEnd(changedTiddlers,changedAttributes);\n\t}\n\treturn result;\n};\n\n/*\nHandle any changes to the history list\n*/\nListWidget.prototype.handleHistoryChanges = function() {\n\t// Get the history data\n\tvar newHistory = this.wiki.getTiddlerDataCached(this.historyTitle,[]);\n\t// Ignore any entries of the history that match the previous history\n\tvar entry = 0;\n\twhile(entry < newHistory.length && entry < this.history.length && newHistory[entry].title === this.history[entry].title) {\n\t\tentry++;\n\t}\n\t// Navigate forwards to each of the new tiddlers\n\twhile(entry < newHistory.length) {\n\t\tif(this.storyview && this.storyview.navigateTo) {\n\t\t\tthis.storyview.navigateTo(newHistory[entry]);\n\t\t}\n\t\tentry++;\n\t}\n\t// Update the history\n\tthis.history = newHistory;\n};\n\n/*\nProcess any changes to the list\n*/\nListWidget.prototype.handleListChanges = function(changedTiddlers) {\n\t// Get the new list\n\tvar prevList = this.list;\n\tthis.list = this.getTiddlerList();\n\t// Check for an empty list\n\tif(this.list.length === 0) {\n\t\t// Check if it was empty before\n\t\tif(prevList.length === 0) {\n\t\t\t// If so, just refresh the empty message\n\t\t\treturn this.refreshChildren(changedTiddlers);\n\t\t} else {\n\t\t\t// Replace the previous content with the empty message\n\t\t\tfor(t=this.children.length-1; t>=0; t--) {\n\t\t\t\tthis.removeListItem(t);\n\t\t\t}\n\t\t\tvar nextSibling = this.findNextSiblingDomNode();\n\t\t\tthis.makeChildWidgets(this.getEmptyMessage());\n\t\t\tthis.renderChildren(this.parentDomNode,nextSibling);\n\t\t\treturn true;\n\t\t}\n\t} else {\n\t\t// If the list was empty then we need to remove the empty message\n\t\tif(prevList.length === 0) {\n\t\t\tthis.removeChildDomNodes();\n\t\t\tthis.children = [];\n\t\t}\n\t\t// Cycle through the list, inserting and removing list items as needed\n\t\tvar hasRefreshed = false;\n\t\tfor(var t=0; t<this.list.length; t++) {\n\t\t\tvar index = this.findListItem(t,this.list[t]);\n\t\t\tif(index === undefined) {\n\t\t\t\t// The list item must be inserted\n\t\t\t\tthis.insertListItem(t,this.list[t]);\n\t\t\t\thasRefreshed = true;\n\t\t\t} else {\n\t\t\t\t// There are intervening list items that must be removed\n\t\t\t\tfor(var n=index-1; n>=t; n--) {\n\t\t\t\t\tthis.removeListItem(n);\n\t\t\t\t\thasRefreshed = true;\n\t\t\t\t}\n\t\t\t\t// Refresh the item we're reusing\n\t\t\t\tvar refreshed = this.children[t].refresh(changedTiddlers);\n\t\t\t\thasRefreshed = hasRefreshed || refreshed;\n\t\t\t}\n\t\t}\n\t\t// Remove any left over items\n\t\tfor(t=this.children.length-1; t>=this.list.length; t--) {\n\t\t\tthis.removeListItem(t);\n\t\t\thasRefreshed = true;\n\t\t}\n\t\treturn hasRefreshed;\n\t}\n};\n\n/*\nFind the list item with a given title, starting from a specified position\n*/\nListWidget.prototype.findListItem = function(startIndex,title) {\n\twhile(startIndex < this.children.length) {\n\t\tif(this.children[startIndex].parseTreeNode.itemTitle === title) {\n\t\t\treturn startIndex;\n\t\t}\n\t\tstartIndex++;\n\t}\n\treturn undefined;\n};\n\n/*\nInsert a new list item at the specified index\n*/\nListWidget.prototype.insertListItem = function(index,title) {\n\t// Create, insert and render the new child widgets\n\tvar widget = this.makeChildWidget(this.makeItemTemplate(title));\n\twidget.parentDomNode = this.parentDomNode; // Hack to enable findNextSiblingDomNode() to work\n\tthis.children.splice(index,0,widget);\n\tvar nextSibling = widget.findNextSiblingDomNode();\n\twidget.render(this.parentDomNode,nextSibling);\n\t// Animate the insertion if required\n\tif(this.storyview && this.storyview.insert) {\n\t\tthis.storyview.insert(widget);\n\t}\n\treturn true;\n};\n\n/*\nRemove the specified list item\n*/\nListWidget.prototype.removeListItem = function(index) {\n\tvar widget = this.children[index];\n\t// Animate the removal if required\n\tif(this.storyview && this.storyview.remove) {\n\t\tthis.storyview.remove(widget);\n\t} else {\n\t\twidget.removeChildDomNodes();\n\t}\n\t// Remove the child widget\n\tthis.children.splice(index,1);\n};\n\nexports.list = ListWidget;\n\nvar ListItemWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nListItemWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nListItemWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nListItemWidget.prototype.execute = function() {\n\t// Set the current list item title\n\tthis.setVariable(this.parseTreeNode.variableName,this.parseTreeNode.itemTitle);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nListItemWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.listitem = ListItemWidget;\n\n})();",
"title": "$:/core/modules/widgets/list.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/macrocall.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/macrocall.js\ntype: application/javascript\nmodule-type: widget\n\nMacrocall widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar MacroCallWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nMacroCallWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nMacroCallWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nMacroCallWidget.prototype.execute = function() {\n\t// Get the parse type if specified\n\tthis.parseType = this.getAttribute(\"$type\",\"text/vnd.tiddlywiki\");\n\tthis.renderOutput = this.getAttribute(\"$output\",\"text/html\");\n\t// Merge together the parameters specified in the parse tree with the specified attributes\n\tvar params = this.parseTreeNode.params ? this.parseTreeNode.params.slice(0) : [];\n\t$tw.utils.each(this.attributes,function(attribute,name) {\n\t\tif(name.charAt(0) !== \"$\") {\n\t\t\tparams.push({name: name, value: attribute});\t\t\t\n\t\t}\n\t});\n\t// Get the macro value\n\tvar text = this.getVariable(this.parseTreeNode.name || this.getAttribute(\"$name\"),{params: params}),\n\t\tparseTreeNodes;\n\t// Are we rendering to HTML?\n\tif(this.renderOutput === \"text/html\") {\n\t\t// If so we'll return the parsed macro\n\t\tvar parser = this.wiki.parseText(this.parseType,text,\n\t\t\t\t\t\t\t{parseAsInline: !this.parseTreeNode.isBlock});\n\t\tparseTreeNodes = parser ? parser.tree : [];\n\t} else {\n\t\t// Otherwise, we'll render the text\n\t\tvar plainText = this.wiki.renderText(\"text/plain\",this.parseType,text,{parentWidget: this});\n\t\tparseTreeNodes = [{type: \"text\", text: plainText}];\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets(parseTreeNodes);\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nMacroCallWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif($tw.utils.count(changedAttributes) > 0) {\n\t\t// Rerender ourselves\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.macrocall = MacroCallWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/macrocall.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/navigator.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/navigator.js\ntype: application/javascript\nmodule-type: widget\n\nNavigator widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar IMPORT_TITLE = \"$:/Import\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar NavigatorWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n\tthis.addEventListeners([\n\t\t{type: \"tm-navigate\", handler: \"handleNavigateEvent\"},\n\t\t{type: \"tm-edit-tiddler\", handler: \"handleEditTiddlerEvent\"},\n\t\t{type: \"tm-delete-tiddler\", handler: \"handleDeleteTiddlerEvent\"},\n\t\t{type: \"tm-save-tiddler\", handler: \"handleSaveTiddlerEvent\"},\n\t\t{type: \"tm-cancel-tiddler\", handler: \"handleCancelTiddlerEvent\"},\n\t\t{type: \"tm-close-tiddler\", handler: \"handleCloseTiddlerEvent\"},\n\t\t{type: \"tm-close-all-tiddlers\", handler: \"handleCloseAllTiddlersEvent\"},\n\t\t{type: \"tm-close-other-tiddlers\", handler: \"handleCloseOtherTiddlersEvent\"},\n\t\t{type: \"tm-new-tiddler\", handler: \"handleNewTiddlerEvent\"},\n\t\t{type: \"tm-import-tiddlers\", handler: \"handleImportTiddlersEvent\"},\n\t\t{type: \"tm-perform-import\", handler: \"handlePerformImportEvent\"},\n\t\t{type: \"tm-fold-tiddler\", handler: \"handleFoldTiddlerEvent\"},\n\t\t{type: \"tm-fold-other-tiddlers\", handler: \"handleFoldOtherTiddlersEvent\"},\n\t\t{type: \"tm-fold-all-tiddlers\", handler: \"handleFoldAllTiddlersEvent\"},\n\t\t{type: \"tm-unfold-all-tiddlers\", handler: \"handleUnfoldAllTiddlersEvent\"},\n\t\t{type: \"tm-rename-tiddler\", handler: \"handleRenameTiddlerEvent\"}\n\t]);\n};\n\n/*\nInherit from the base widget class\n*/\nNavigatorWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nNavigatorWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nNavigatorWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.storyTitle = this.getAttribute(\"story\");\n\tthis.historyTitle = this.getAttribute(\"history\");\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nNavigatorWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.story || changedAttributes.history) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nNavigatorWidget.prototype.getStoryList = function() {\n\treturn this.storyTitle ? this.wiki.getTiddlerList(this.storyTitle) : null;\n};\n\nNavigatorWidget.prototype.saveStoryList = function(storyList) {\n\tvar storyTiddler = this.wiki.getTiddler(this.storyTitle);\n\tthis.wiki.addTiddler(new $tw.Tiddler(\n\t\t{title: this.storyTitle},\n\t\tstoryTiddler,\n\t\t{list: storyList}\n\t));\n};\n\nNavigatorWidget.prototype.removeTitleFromStory = function(storyList,title) {\n\tvar p = storyList.indexOf(title);\n\twhile(p !== -1) {\n\t\tstoryList.splice(p,1);\n\t\tp = storyList.indexOf(title);\n\t}\n};\n\nNavigatorWidget.prototype.replaceFirstTitleInStory = function(storyList,oldTitle,newTitle) {\n\tvar pos = storyList.indexOf(oldTitle);\n\tif(pos !== -1) {\n\t\tstoryList[pos] = newTitle;\n\t\tdo {\n\t\t\tpos = storyList.indexOf(oldTitle,pos + 1);\n\t\t\tif(pos !== -1) {\n\t\t\t\tstoryList.splice(pos,1);\n\t\t\t}\n\t\t} while(pos !== -1);\n\t} else {\n\t\tstoryList.splice(0,0,newTitle);\n\t}\n};\n\nNavigatorWidget.prototype.addToStory = function(title,fromTitle) {\n\tvar storyList = this.getStoryList();\n\t// Quit if we cannot get hold of the story list\n\tif(!storyList) {\n\t\treturn;\n\t}\n\t// See if the tiddler is already there\n\tvar slot = storyList.indexOf(title);\n\t// Quit if it already exists in the story river\n\tif(slot >= 0) {\n\t\treturn;\n\t}\n\t// First we try to find the position of the story element we navigated from\n\tvar fromIndex = storyList.indexOf(fromTitle);\n\tif(fromIndex >= 0) {\n\t\t// The tiddler is added from inside the river\n\t\t// Determine where to insert the tiddler; Fallback is \"below\"\n\t\tswitch(this.getAttribute(\"openLinkFromInsideRiver\",\"below\")) {\n\t\t\tcase \"top\":\n\t\t\t\tslot = 0;\n\t\t\t\tbreak;\n\t\t\tcase \"bottom\":\n\t\t\t\tslot = storyList.length;\n\t\t\t\tbreak;\n\t\t\tcase \"above\":\n\t\t\t\tslot = fromIndex;\n\t\t\t\tbreak;\n\t\t\tcase \"below\": // Intentional fall-through\n\t\t\tdefault:\n\t\t\t\tslot = fromIndex + 1;\n\t\t\t\tbreak;\n\t\t}\n\t} else {\n\t\t// The tiddler is opened from outside the river. Determine where to insert the tiddler; default is \"top\"\n\t\tif(this.getAttribute(\"openLinkFromOutsideRiver\",\"top\") === \"bottom\") {\n\t\t\t// Insert at bottom\n\t\t\tslot = storyList.length;\n\t\t} else {\n\t\t\t// Insert at top\n\t\t\tslot = 0;\n\t\t}\n\t}\n\t// Add the tiddler\n\tstoryList.splice(slot,0,title);\n\t// Save the story\n\tthis.saveStoryList(storyList);\n};\n\n/*\nAdd a new record to the top of the history stack\ntitle: a title string or an array of title strings\nfromPageRect: page coordinates of the origin of the navigation\n*/\nNavigatorWidget.prototype.addToHistory = function(title,fromPageRect) {\n\tthis.wiki.addToHistory(title,fromPageRect,this.historyTitle);\n};\n\n/*\nHandle a tm-navigate event\n*/\nNavigatorWidget.prototype.handleNavigateEvent = function(event) {\n\tevent = $tw.hooks.invokeHook(\"th-navigating\",event);\n\tif(event.navigateTo) {\n\t\tthis.addToStory(event.navigateTo,event.navigateFromTitle);\n\t\tif(!event.navigateSuppressNavigation) {\n\t\t\tthis.addToHistory(event.navigateTo,event.navigateFromClientRect);\n\t\t}\n\t}\n\treturn false;\n};\n\n// Close a specified tiddler\nNavigatorWidget.prototype.handleCloseTiddlerEvent = function(event) {\n\tvar title = event.param || event.tiddlerTitle,\n\t\tstoryList = this.getStoryList();\n\t// Look for tiddlers with this title to close\n\tthis.removeTitleFromStory(storyList,title);\n\tthis.saveStoryList(storyList);\n\treturn false;\n};\n\n// Close all tiddlers\nNavigatorWidget.prototype.handleCloseAllTiddlersEvent = function(event) {\n\tthis.saveStoryList([]);\n\treturn false;\n};\n\n// Close other tiddlers\nNavigatorWidget.prototype.handleCloseOtherTiddlersEvent = function(event) {\n\tvar title = event.param || event.tiddlerTitle;\n\tthis.saveStoryList([title]);\n\treturn false;\n};\n\n// Place a tiddler in edit mode\nNavigatorWidget.prototype.handleEditTiddlerEvent = function(event) {\n\tvar self = this;\n\tfunction isUnmodifiedShadow(title) {\n\t\treturn self.wiki.isShadowTiddler(title) && !self.wiki.tiddlerExists(title);\n\t}\n\tfunction confirmEditShadow(title) {\n\t\treturn confirm($tw.language.getString(\n\t\t\t\"ConfirmEditShadowTiddler\",\n\t\t\t{variables:\n\t\t\t\t{title: title}\n\t\t\t}\n\t\t));\n\t}\n\tvar title = event.param || event.tiddlerTitle;\n\tif(isUnmodifiedShadow(title) && !confirmEditShadow(title)) {\n\t\treturn false;\n\t}\n\t// Replace the specified tiddler with a draft in edit mode\n\tvar draftTiddler = this.makeDraftTiddler(title);\n\t// Update the story and history if required\n\tif(!event.paramObject || event.paramObject.suppressNavigation !== \"yes\") {\n\t\tvar draftTitle = draftTiddler.fields.title,\n\t\t\tstoryList = this.getStoryList();\n\t\tthis.removeTitleFromStory(storyList,draftTitle);\n\t\tthis.replaceFirstTitleInStory(storyList,title,draftTitle);\n\t\tthis.addToHistory(draftTitle,event.navigateFromClientRect);\n\t\tthis.saveStoryList(storyList);\n\t\treturn false;\n\t}\n};\n\n// Delete a tiddler\nNavigatorWidget.prototype.handleDeleteTiddlerEvent = function(event) {\n\t// Get the tiddler we're deleting\n\tvar title = event.param || event.tiddlerTitle,\n\t\ttiddler = this.wiki.getTiddler(title),\n\t\tstoryList = this.getStoryList(),\n\t\toriginalTitle = tiddler ? tiddler.fields[\"draft.of\"] : \"\",\n\t\toriginalTiddler = originalTitle ? this.wiki.getTiddler(originalTitle) : undefined,\n\t\tconfirmationTitle;\n\tif(!tiddler) {\n\t\treturn false;\n\t}\n\t// Check if the tiddler we're deleting is in draft mode\n\tif(originalTitle) {\n\t\t// If so, we'll prompt for confirmation referencing the original tiddler\n\t\tconfirmationTitle = originalTitle;\n\t} else {\n\t\t// If not a draft, then prompt for confirmation referencing the specified tiddler\n\t\tconfirmationTitle = title;\n\t}\n\t// Seek confirmation\n\tif((this.wiki.getTiddler(originalTitle) || (tiddler.fields.text || \"\") !== \"\") && !confirm($tw.language.getString(\n\t\t\t\t\"ConfirmDeleteTiddler\",\n\t\t\t\t{variables:\n\t\t\t\t\t{title: confirmationTitle}\n\t\t\t\t}\n\t\t\t))) {\n\t\treturn false;\n\t}\n\t// Delete the original tiddler\n\tif(originalTitle) {\n\t\tif(originalTiddler) {\n\t\t\t$tw.hooks.invokeHook(\"th-deleting-tiddler\",originalTiddler);\n\t\t}\n\t\tthis.wiki.deleteTiddler(originalTitle);\n\t\tthis.removeTitleFromStory(storyList,originalTitle);\n\t}\n\t// Invoke the hook function and delete this tiddler\n\t$tw.hooks.invokeHook(\"th-deleting-tiddler\",tiddler);\n\tthis.wiki.deleteTiddler(title);\n\t// Remove the closed tiddler from the story\n\tthis.removeTitleFromStory(storyList,title);\n\tthis.saveStoryList(storyList);\n\t// Trigger an autosave\n\t$tw.rootWidget.dispatchEvent({type: \"tm-auto-save-wiki\"});\n\treturn false;\n};\n\n/*\nCreate/reuse the draft tiddler for a given title\n*/\nNavigatorWidget.prototype.makeDraftTiddler = function(targetTitle) {\n\t// See if there is already a draft tiddler for this tiddler\n\tvar draftTitle = this.wiki.findDraft(targetTitle);\n\tif(draftTitle) {\n\t\treturn this.wiki.getTiddler(draftTitle);\n\t}\n\t// Get the current value of the tiddler we're editing\n\tvar tiddler = this.wiki.getTiddler(targetTitle);\n\t// Save the initial value of the draft tiddler\n\tdraftTitle = this.generateDraftTitle(targetTitle);\n\tvar draftTiddler = new $tw.Tiddler(\n\t\t\ttiddler,\n\t\t\t{\n\t\t\t\ttitle: draftTitle,\n\t\t\t\t\"draft.title\": targetTitle,\n\t\t\t\t\"draft.of\": targetTitle\n\t\t\t},\n\t\t\tthis.wiki.getModificationFields()\n\t\t);\n\tthis.wiki.addTiddler(draftTiddler);\n\treturn draftTiddler;\n};\n\n/*\nGenerate a title for the draft of a given tiddler\n*/\nNavigatorWidget.prototype.generateDraftTitle = function(title) {\n\tvar c = 0,\n\t\tdraftTitle;\n\tdo {\n\t\tdraftTitle = \"Draft \" + (c ? (c + 1) + \" \" : \"\") + \"of '\" + title + \"'\";\n\t\tc++;\n\t} while(this.wiki.tiddlerExists(draftTitle));\n\treturn draftTitle;\n};\n\n// Take a tiddler out of edit mode, saving the changes\nNavigatorWidget.prototype.handleSaveTiddlerEvent = function(event) {\n\tvar title = event.param || event.tiddlerTitle,\n\t\ttiddler = this.wiki.getTiddler(title),\n\t\tstoryList = this.getStoryList();\n\t// Replace the original tiddler with the draft\n\tif(tiddler) {\n\t\tvar draftTitle = (tiddler.fields[\"draft.title\"] || \"\").trim(),\n\t\t\tdraftOf = (tiddler.fields[\"draft.of\"] || \"\").trim();\n\t\tif(draftTitle) {\n\t\t\tvar isRename = draftOf !== draftTitle,\n\t\t\t\tisConfirmed = true;\n\t\t\tif(isRename && this.wiki.tiddlerExists(draftTitle)) {\n\t\t\t\tisConfirmed = confirm($tw.language.getString(\n\t\t\t\t\t\"ConfirmOverwriteTiddler\",\n\t\t\t\t\t{variables:\n\t\t\t\t\t\t{title: draftTitle}\n\t\t\t\t\t}\n\t\t\t\t));\n\t\t\t}\n\t\t\tif(isConfirmed) {\n\t\t\t\t// Create the new tiddler and pass it through the th-saving-tiddler hook\n\t\t\t\tvar newTiddler = new $tw.Tiddler(this.wiki.getCreationFields(),tiddler,{\n\t\t\t\t\ttitle: draftTitle,\n\t\t\t\t\t\"draft.title\": undefined,\n\t\t\t\t\t\"draft.of\": undefined\n\t\t\t\t},this.wiki.getModificationFields());\n\t\t\t\tnewTiddler = $tw.hooks.invokeHook(\"th-saving-tiddler\",newTiddler);\n\t\t\t\tthis.wiki.addTiddler(newTiddler);\n\t\t\t\t// If enabled, relink references to renamed tiddler\n\t\t\t\tvar shouldRelink = this.getAttribute(\"relinkOnRename\",\"no\").toLowerCase().trim() === \"yes\";\n\t\t\t\tif(isRename && shouldRelink && this.wiki.tiddlerExists(draftOf)) {\nconsole.log(\"Relinking '\" + draftOf + \"' to '\" + draftTitle + \"'\");\n\t\t\t\t\tthis.wiki.relinkTiddler(draftOf,draftTitle);\n\t\t\t\t}\n\t\t\t\t// Remove the draft tiddler\n\t\t\t\tthis.wiki.deleteTiddler(title);\n\t\t\t\t// Remove the original tiddler if we're renaming it\n\t\t\t\tif(isRename) {\n\t\t\t\t\tthis.wiki.deleteTiddler(draftOf);\n\t\t\t\t}\n\t\t\t\t// #2381 always remove new title & old\n\t\t\t\tthis.removeTitleFromStory(storyList,draftTitle);\n\t\t\t\tthis.removeTitleFromStory(storyList,draftOf);\n\t\t\t\tif(!event.paramObject || event.paramObject.suppressNavigation !== \"yes\") {\n\t\t\t\t\t// Replace the draft in the story with the original\n\t\t\t\t\tthis.replaceFirstTitleInStory(storyList,title,draftTitle);\n\t\t\t\t\tthis.addToHistory(draftTitle,event.navigateFromClientRect);\n\t\t\t\t\tif(draftTitle !== this.storyTitle) {\n\t\t\t\t\t\tthis.saveStoryList(storyList);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// Trigger an autosave\n\t\t\t\t$tw.rootWidget.dispatchEvent({type: \"tm-auto-save-wiki\"});\n\t\t\t}\n\t\t}\n\t}\n\treturn false;\n};\n\n// Take a tiddler out of edit mode without saving the changes\nNavigatorWidget.prototype.handleCancelTiddlerEvent = function(event) {\n\t// Flip the specified tiddler from draft back to the original\n\tvar draftTitle = event.param || event.tiddlerTitle,\n\t\tdraftTiddler = this.wiki.getTiddler(draftTitle),\n\t\toriginalTitle = draftTiddler && draftTiddler.fields[\"draft.of\"];\n\tif(draftTiddler && originalTitle) {\n\t\t// Ask for confirmation if the tiddler text has changed\n\t\tvar isConfirmed = true,\n\t\t\toriginalTiddler = this.wiki.getTiddler(originalTitle),\n\t\t\tstoryList = this.getStoryList();\n\t\tif(this.wiki.isDraftModified(draftTitle)) {\n\t\t\tisConfirmed = confirm($tw.language.getString(\n\t\t\t\t\"ConfirmCancelTiddler\",\n\t\t\t\t{variables:\n\t\t\t\t\t{title: draftTitle}\n\t\t\t\t}\n\t\t\t));\n\t\t}\n\t\t// Remove the draft tiddler\n\t\tif(isConfirmed) {\n\t\t\tthis.wiki.deleteTiddler(draftTitle);\n\t\t\tif(!event.paramObject || event.paramObject.suppressNavigation !== \"yes\") {\n\t\t\t\tif(originalTiddler) {\n\t\t\t\t\tthis.replaceFirstTitleInStory(storyList,draftTitle,originalTitle);\n\t\t\t\t\tthis.addToHistory(originalTitle,event.navigateFromClientRect);\n\t\t\t\t} else {\n\t\t\t\t\tthis.removeTitleFromStory(storyList,draftTitle);\n\t\t\t\t}\n\t\t\t\tthis.saveStoryList(storyList);\n\t\t\t}\n\t\t}\n\t}\n\treturn false;\n};\n\n// Create a new draft tiddler\n// event.param can either be the title of a template tiddler, or a hashmap of fields.\n//\n// The title of the newly created tiddler follows these rules:\n// * If a hashmap was used and a title field was specified, use that title\n// * If a hashmap was used without a title field, use a default title, if necessary making it unique with a numeric suffix\n// * If a template tiddler was used, use the title of the template, if necessary making it unique with a numeric suffix\n//\n// If a draft of the target tiddler already exists then it is reused\nNavigatorWidget.prototype.handleNewTiddlerEvent = function(event) {\n\t// Get the story details\n\tvar storyList = this.getStoryList(),\n\t\ttemplateTiddler, additionalFields, title, draftTitle, existingTiddler;\n\t// Get the template tiddler (if any)\n\tif(typeof event.param === \"string\") {\n\t\t// Get the template tiddler\n\t\ttemplateTiddler = this.wiki.getTiddler(event.param);\n\t\t// Generate a new title\n\t\ttitle = this.wiki.generateNewTitle(event.param || $tw.language.getString(\"DefaultNewTiddlerTitle\"));\n\t}\n\t// Get the specified additional fields\n\tif(typeof event.paramObject === \"object\") {\n\t\tadditionalFields = event.paramObject;\n\t}\n\tif(typeof event.param === \"object\") { // Backwards compatibility with 5.1.3\n\t\tadditionalFields = event.param;\n\t}\n\tif(additionalFields && additionalFields.title) {\n\t\ttitle = additionalFields.title;\n\t}\n\t// Generate a title if we don't have one\n\ttitle = title || this.wiki.generateNewTitle($tw.language.getString(\"DefaultNewTiddlerTitle\"));\n\t// Find any existing draft for this tiddler\n\tdraftTitle = this.wiki.findDraft(title);\n\t// Pull in any existing tiddler\n\tif(draftTitle) {\n\t\texistingTiddler = this.wiki.getTiddler(draftTitle);\n\t} else {\n\t\tdraftTitle = this.generateDraftTitle(title);\n\t\texistingTiddler = this.wiki.getTiddler(title);\n\t}\n\t// Merge the tags\n\tvar mergedTags = [];\n\tif(existingTiddler && existingTiddler.fields.tags) {\n\t\t$tw.utils.pushTop(mergedTags,existingTiddler.fields.tags);\n\t}\n\tif(additionalFields && additionalFields.tags) {\n\t\t// Merge tags\n\t\tmergedTags = $tw.utils.pushTop(mergedTags,$tw.utils.parseStringArray(additionalFields.tags));\n\t}\n\tif(templateTiddler && templateTiddler.fields.tags) {\n\t\t// Merge tags\n\t\tmergedTags = $tw.utils.pushTop(mergedTags,templateTiddler.fields.tags);\n\t}\n\t// Save the draft tiddler\n\tvar draftTiddler = new $tw.Tiddler({\n\t\t\ttext: \"\",\n\t\t\t\"draft.title\": title\n\t\t},\n\t\ttemplateTiddler,\n\t\texistingTiddler,\n\t\tadditionalFields,\n\t\tthis.wiki.getCreationFields(),\n\t\t{\n\t\t\ttitle: draftTitle,\n\t\t\t\"draft.of\": title,\n\t\t\ttags: mergedTags\n\t\t},this.wiki.getModificationFields());\n\tthis.wiki.addTiddler(draftTiddler);\n\t// Update the story to insert the new draft at the top and remove any existing tiddler\n\tif(storyList.indexOf(draftTitle) === -1) {\n\t\tvar slot = storyList.indexOf(event.navigateFromTitle);\n\t\tstoryList.splice(slot + 1,0,draftTitle);\n\t}\n\tif(storyList.indexOf(title) !== -1) {\n\t\tstoryList.splice(storyList.indexOf(title),1);\t\t\n\t}\n\tthis.saveStoryList(storyList);\n\t// Add a new record to the top of the history stack\n\tthis.addToHistory(draftTitle);\n\treturn false;\n};\n\n// Import JSON tiddlers into a pending import tiddler\nNavigatorWidget.prototype.handleImportTiddlersEvent = function(event) {\n\t// Get the tiddlers\n\tvar tiddlers = [];\n\ttry {\n\t\ttiddlers = JSON.parse(event.param);\t\n\t} catch(e) {\n\t}\n\t// Get the current $:/Import tiddler\n\tvar importTiddler = this.wiki.getTiddler(IMPORT_TITLE),\n\t\timportData = this.wiki.getTiddlerData(IMPORT_TITLE,{}),\n\t\tnewFields = new Object({\n\t\t\ttitle: IMPORT_TITLE,\n\t\t\ttype: \"application/json\",\n\t\t\t\"plugin-type\": \"import\",\n\t\t\t\"status\": \"pending\"\n\t\t}),\n\t\tincomingTiddlers = [];\n\t// Process each tiddler\n\timportData.tiddlers = importData.tiddlers || {};\n\t$tw.utils.each(tiddlers,function(tiddlerFields) {\n\t\tvar title = tiddlerFields.title;\n\t\tif(title) {\n\t\t\tincomingTiddlers.push(title);\n\t\t\timportData.tiddlers[title] = tiddlerFields;\n\t\t}\n\t});\n\t// Give the active upgrader modules a chance to process the incoming tiddlers\n\tvar messages = this.wiki.invokeUpgraders(incomingTiddlers,importData.tiddlers);\n\t$tw.utils.each(messages,function(message,title) {\n\t\tnewFields[\"message-\" + title] = message;\n\t});\n\t// Deselect any suppressed tiddlers\n\t$tw.utils.each(importData.tiddlers,function(tiddler,title) {\n\t\tif($tw.utils.count(tiddler) === 0) {\n\t\t\tnewFields[\"selection-\" + title] = \"unchecked\";\n\t\t}\n\t});\n\t// Save the $:/Import tiddler\n\tnewFields.text = JSON.stringify(importData,null,$tw.config.preferences.jsonSpaces);\n\tthis.wiki.addTiddler(new $tw.Tiddler(importTiddler,newFields));\n\t// Update the story and history details\n\tif(this.getVariable(\"tv-auto-open-on-import\") !== \"no\") {\n\t\tvar storyList = this.getStoryList(),\n\t\t\thistory = [];\n\t\t// Add it to the story\n\t\tif(storyList.indexOf(IMPORT_TITLE) === -1) {\n\t\t\tstoryList.unshift(IMPORT_TITLE);\n\t\t}\n\t\t// And to history\n\t\thistory.push(IMPORT_TITLE);\n\t\t// Save the updated story and history\n\t\tthis.saveStoryList(storyList);\n\t\tthis.addToHistory(history);\n\t}\n\treturn false;\n};\n\n// \nNavigatorWidget.prototype.handlePerformImportEvent = function(event) {\n\tvar self = this,\n\t\timportTiddler = this.wiki.getTiddler(event.param),\n\t\timportData = this.wiki.getTiddlerDataCached(event.param,{tiddlers: {}}),\n\t\timportReport = [];\n\t// Add the tiddlers to the store\n\timportReport.push($tw.language.getString(\"Import/Imported/Hint\") + \"\\n\");\n\t$tw.utils.each(importData.tiddlers,function(tiddlerFields) {\n\t\tvar title = tiddlerFields.title;\n\t\tif(title && importTiddler && importTiddler.fields[\"selection-\" + title] !== \"unchecked\") {\n\t\t\tvar tiddler = new $tw.Tiddler(tiddlerFields);\n\t\t\ttiddler = $tw.hooks.invokeHook(\"th-importing-tiddler\",tiddler);\n\t\t\tself.wiki.addTiddler(tiddler);\n\t\t\timportReport.push(\"# [[\" + tiddlerFields.title + \"]]\");\n\t\t}\n\t});\n\t// Replace the $:/Import tiddler with an import report\n\tthis.wiki.addTiddler(new $tw.Tiddler({\n\t\ttitle: event.param,\n\t\ttext: importReport.join(\"\\n\"),\n\t\t\"status\": \"complete\"\n\t}));\n\t// Navigate to the $:/Import tiddler\n\tthis.addToHistory([event.param]);\n\t// Trigger an autosave\n\t$tw.rootWidget.dispatchEvent({type: \"tm-auto-save-wiki\"});\n};\n\nNavigatorWidget.prototype.handleFoldTiddlerEvent = function(event) {\n\tvar paramObject = event.paramObject || {};\n\tif(paramObject.foldedState) {\n\t\tvar foldedState = this.wiki.getTiddlerText(paramObject.foldedState,\"show\") === \"show\" ? \"hide\" : \"show\";\n\t\tthis.wiki.setText(paramObject.foldedState,\"text\",null,foldedState);\n\t}\n};\n\nNavigatorWidget.prototype.handleFoldOtherTiddlersEvent = function(event) {\n\tvar self = this,\n\t\tparamObject = event.paramObject || {},\n\t\tprefix = paramObject.foldedStatePrefix;\n\t$tw.utils.each(this.getStoryList(),function(title) {\n\t\tself.wiki.setText(prefix + title,\"text\",null,event.param === title ? \"show\" : \"hide\");\n\t});\n};\n\nNavigatorWidget.prototype.handleFoldAllTiddlersEvent = function(event) {\n\tvar self = this,\n\t\tparamObject = event.paramObject || {},\n\t\tprefix = paramObject.foldedStatePrefix;\n\t$tw.utils.each(this.getStoryList(),function(title) {\n\t\tself.wiki.setText(prefix + title,\"text\",null,\"hide\");\n\t});\n};\n\nNavigatorWidget.prototype.handleUnfoldAllTiddlersEvent = function(event) {\n\tvar self = this,\n\t\tparamObject = event.paramObject || {},\n\t\tprefix = paramObject.foldedStatePrefix;\n\t$tw.utils.each(this.getStoryList(),function(title) {\n\t\tself.wiki.setText(prefix + title,\"text\",null,\"show\");\n\t});\n};\n\nNavigatorWidget.prototype.handleRenameTiddlerEvent = function(event) {\n\tvar paramObject = event.paramObject || {},\n\t\tfrom = paramObject.from || event.tiddlerTitle,\n\t\tto = paramObject.to;\n\t$tw.wiki.renameTiddler(from,to);\n};\n\nexports.navigator = NavigatorWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/navigator.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/password.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/password.js\ntype: application/javascript\nmodule-type: widget\n\nPassword widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar PasswordWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nPasswordWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nPasswordWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Get the current password\n\tvar password = $tw.browser ? $tw.utils.getPassword(this.passwordName) || \"\" : \"\";\n\t// Create our element\n\tvar domNode = this.document.createElement(\"input\");\n\tdomNode.setAttribute(\"type\",\"password\");\n\tdomNode.setAttribute(\"value\",password);\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(domNode,[\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleChangeEvent\"}\n\t]);\n\t// Insert the label into the DOM and render any children\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tthis.domNodes.push(domNode);\n};\n\nPasswordWidget.prototype.handleChangeEvent = function(event) {\n\tvar password = this.domNodes[0].value;\n\treturn $tw.utils.savePassword(this.passwordName,password);\n};\n\n/*\nCompute the internal state of the widget\n*/\nPasswordWidget.prototype.execute = function() {\n\t// Get the parameters from the attributes\n\tthis.passwordName = this.getAttribute(\"name\",\"\");\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nPasswordWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.name) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.password = PasswordWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/password.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/radio.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/radio.js\ntype: application/javascript\nmodule-type: widget\n\nSet a field or index at a given tiddler via radio buttons\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar RadioWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nRadioWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nRadioWidget.prototype.render = function(parent,nextSibling) {\n\t// Save the parent dom node\n\tthis.parentDomNode = parent;\n\t// Compute our attributes\n\tthis.computeAttributes();\n\t// Execute our logic\n\tthis.execute();\n\t// Create our elements\n\tthis.labelDomNode = this.document.createElement(\"label\");\n\tthis.labelDomNode.setAttribute(\"class\",this.radioClass);\n\tthis.inputDomNode = this.document.createElement(\"input\");\n\tthis.inputDomNode.setAttribute(\"type\",\"radio\");\n\tif(this.getValue() == this.radioValue) {\n\t\tthis.inputDomNode.setAttribute(\"checked\",\"true\");\n\t}\n\tthis.labelDomNode.appendChild(this.inputDomNode);\n\tthis.spanDomNode = this.document.createElement(\"span\");\n\tthis.labelDomNode.appendChild(this.spanDomNode);\n\t// Add a click event handler\n\t$tw.utils.addEventListeners(this.inputDomNode,[\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleChangeEvent\"}\n\t]);\n\t// Insert the label into the DOM and render any children\n\tparent.insertBefore(this.labelDomNode,nextSibling);\n\tthis.renderChildren(this.spanDomNode,null);\n\tthis.domNodes.push(this.labelDomNode);\n};\n\nRadioWidget.prototype.getValue = function() {\n\tvar value,\n\t\ttiddler = this.wiki.getTiddler(this.radioTitle);\n\tif (this.radioIndex) {\n\t\tvalue = this.wiki.extractTiddlerDataItem(this.radioTitle,this.radioIndex);\n\t} else {\n\t\tvalue = tiddler && tiddler.getFieldString(this.radioField);\n\t}\n\treturn value;\n};\n\nRadioWidget.prototype.setValue = function() {\n\tif(this.radioIndex) {\n\t\tthis.wiki.setText(this.radioTitle,\"\",this.radioIndex,this.radioValue);\n\t} else {\n\t\tvar tiddler = this.wiki.getTiddler(this.radioTitle),\n\t\t\taddition = {};\n\t\taddition[this.radioField] = this.radioValue;\n\t\tthis.wiki.addTiddler(new $tw.Tiddler(this.wiki.getCreationFields(),{title: this.radioTitle},tiddler,addition,this.wiki.getModificationFields()));\n\t}\n};\n\nRadioWidget.prototype.handleChangeEvent = function(event) {\n\tif(this.inputDomNode.checked) {\n\t\tthis.setValue();\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nRadioWidget.prototype.execute = function() {\n\t// Get the parameters from the attributes\n\tthis.radioTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.radioField = this.getAttribute(\"field\",\"text\");\n\tthis.radioIndex = this.getAttribute(\"index\");\n\tthis.radioValue = this.getAttribute(\"value\");\n\tthis.radioClass = this.getAttribute(\"class\",\"\");\n\tif(this.radioClass !== \"\") {\n\t\tthis.radioClass += \" \";\n\t}\n\tthis.radioClass += \"tc-radio\";\n\t// Make the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nRadioWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes.value || changedAttributes[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\tvar refreshed = false;\n\t\tif(changedTiddlers[this.radioTitle]) {\n\t\t\tthis.inputDomNode.checked = this.getValue() === this.radioValue;\n\t\t\trefreshed = true;\n\t\t}\n\t\treturn this.refreshChildren(changedTiddlers) || refreshed;\n\t}\n};\n\nexports.radio = RadioWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/radio.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/raw.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/raw.js\ntype: application/javascript\nmodule-type: widget\n\nRaw widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar RawWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nRawWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nRawWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.execute();\n\tvar div = this.document.createElement(\"div\");\n\tdiv.innerHTML=this.parseTreeNode.html;\n\tparent.insertBefore(div,nextSibling);\n\tthis.domNodes.push(div);\t\n};\n\n/*\nCompute the internal state of the widget\n*/\nRawWidget.prototype.execute = function() {\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nRawWidget.prototype.refresh = function(changedTiddlers) {\n\treturn false;\n};\n\nexports.raw = RawWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/raw.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/reveal.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/reveal.js\ntype: application/javascript\nmodule-type: widget\n\nReveal widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar RevealWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nRevealWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nRevealWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar tag = this.parseTreeNode.isBlock ? \"div\" : \"span\";\n\tif(this.revealTag && $tw.config.htmlUnsafeElements.indexOf(this.revealTag) === -1) {\n\t\ttag = this.revealTag;\n\t}\n\tvar domNode = this.document.createElement(tag);\n\tvar classes = this[\"class\"].split(\" \") || [];\n\tclasses.push(\"tc-reveal\");\n\tdomNode.className = classes.join(\" \");\n\tif(this.style) {\n\t\tdomNode.setAttribute(\"style\",this.style);\n\t}\n\tparent.insertBefore(domNode,nextSibling);\n\tthis.renderChildren(domNode,null);\n\tif(!domNode.isTiddlyWikiFakeDom && this.type === \"popup\" && this.isOpen) {\n\t\tthis.positionPopup(domNode);\n\t\t$tw.utils.addClass(domNode,\"tc-popup\"); // Make sure that clicks don't dismiss popups within the revealed content\n\t}\n\tif(!this.isOpen) {\n\t\tdomNode.setAttribute(\"hidden\",\"true\");\n\t}\n\tthis.domNodes.push(domNode);\n};\n\nRevealWidget.prototype.positionPopup = function(domNode) {\n\tdomNode.style.position = \"absolute\";\n\tdomNode.style.zIndex = \"1000\";\n\tswitch(this.position) {\n\t\tcase \"left\":\n\t\t\tdomNode.style.left = (this.popup.left - domNode.offsetWidth) + \"px\";\n\t\t\tdomNode.style.top = this.popup.top + \"px\";\n\t\t\tbreak;\n\t\tcase \"above\":\n\t\t\tdomNode.style.left = this.popup.left + \"px\";\n\t\t\tdomNode.style.top = (this.popup.top - domNode.offsetHeight) + \"px\";\n\t\t\tbreak;\n\t\tcase \"aboveright\":\n\t\t\tdomNode.style.left = (this.popup.left + this.popup.width) + \"px\";\n\t\t\tdomNode.style.top = (this.popup.top + this.popup.height - domNode.offsetHeight) + \"px\";\n\t\t\tbreak;\n\t\tcase \"right\":\n\t\t\tdomNode.style.left = (this.popup.left + this.popup.width) + \"px\";\n\t\t\tdomNode.style.top = this.popup.top + \"px\";\n\t\t\tbreak;\n\t\tcase \"belowleft\":\n\t\t\tdomNode.style.left = (this.popup.left + this.popup.width - domNode.offsetWidth) + \"px\";\n\t\t\tdomNode.style.top = (this.popup.top + this.popup.height) + \"px\";\n\t\t\tbreak;\n\t\tdefault: // Below\n\t\t\tdomNode.style.left = this.popup.left + \"px\";\n\t\t\tdomNode.style.top = (this.popup.top + this.popup.height) + \"px\";\n\t\t\tbreak;\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nRevealWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.state = this.getAttribute(\"state\");\n\tthis.revealTag = this.getAttribute(\"tag\");\n\tthis.type = this.getAttribute(\"type\");\n\tthis.text = this.getAttribute(\"text\");\n\tthis.position = this.getAttribute(\"position\");\n\tthis[\"class\"] = this.getAttribute(\"class\",\"\");\n\tthis.style = this.getAttribute(\"style\",\"\");\n\tthis[\"default\"] = this.getAttribute(\"default\",\"\");\n\tthis.animate = this.getAttribute(\"animate\",\"no\");\n\tthis.retain = this.getAttribute(\"retain\",\"no\");\n\tthis.openAnimation = this.animate === \"no\" ? undefined : \"open\";\n\tthis.closeAnimation = this.animate === \"no\" ? undefined : \"close\";\n\t// Compute the title of the state tiddler and read it\n\tthis.stateTitle = this.state;\n\tthis.readState();\n\t// Construct the child widgets\n\tvar childNodes = this.isOpen ? this.parseTreeNode.children : [];\n\tthis.hasChildNodes = this.isOpen;\n\tthis.makeChildWidgets(childNodes);\n};\n\n/*\nRead the state tiddler\n*/\nRevealWidget.prototype.readState = function() {\n\t// Read the information from the state tiddler\n\tvar state = this.stateTitle ? this.wiki.getTextReference(this.stateTitle,this[\"default\"],this.getVariable(\"currentTiddler\")) : this[\"default\"];\n\tswitch(this.type) {\n\t\tcase \"popup\":\n\t\t\tthis.readPopupState(state);\n\t\t\tbreak;\n\t\tcase \"match\":\n\t\t\tthis.readMatchState(state);\n\t\t\tbreak;\n\t\tcase \"nomatch\":\n\t\t\tthis.readMatchState(state);\n\t\t\tthis.isOpen = !this.isOpen;\n\t\t\tbreak;\n\t}\n};\n\nRevealWidget.prototype.readMatchState = function(state) {\n\tthis.isOpen = state === this.text;\n};\n\nRevealWidget.prototype.readPopupState = function(state) {\n\tvar popupLocationRegExp = /^\\((-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+),(-?[0-9\\.E]+)\\)$/,\n\t\tmatch = popupLocationRegExp.exec(state);\n\t// Check if the state matches the location regexp\n\tif(match) {\n\t\t// If so, we're open\n\t\tthis.isOpen = true;\n\t\t// Get the location\n\t\tthis.popup = {\n\t\t\tleft: parseFloat(match[1]),\n\t\t\ttop: parseFloat(match[2]),\n\t\t\twidth: parseFloat(match[3]),\n\t\t\theight: parseFloat(match[4])\n\t\t};\n\t} else {\n\t\t// If not, we're closed\n\t\tthis.isOpen = false;\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nRevealWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.state || changedAttributes.type || changedAttributes.text || changedAttributes.position || changedAttributes[\"default\"] || changedAttributes.animate) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\tvar refreshed = false,\n\t\t\tcurrentlyOpen = this.isOpen;\n\t\tthis.readState();\n\t\tif(this.isOpen !== currentlyOpen) {\n\t\t\tif(this.retain === \"yes\") {\n\t\t\t\tthis.updateState();\n\t\t\t} else {\n\t\t\t\tthis.refreshSelf();\n\t\t\t\trefreshed = true;\n\t\t\t}\n\t\t}\n\t\treturn this.refreshChildren(changedTiddlers) || refreshed;\n\t}\n};\n\n/*\nCalled by refresh() to dynamically show or hide the content\n*/\nRevealWidget.prototype.updateState = function() {\n\t// Read the current state\n\tthis.readState();\n\t// Construct the child nodes if needed\n\tvar domNode = this.domNodes[0];\n\tif(this.isOpen && !this.hasChildNodes) {\n\t\tthis.hasChildNodes = true;\n\t\tthis.makeChildWidgets(this.parseTreeNode.children);\n\t\tthis.renderChildren(domNode,null);\n\t}\n\t// Animate our DOM node\n\tif(!domNode.isTiddlyWikiFakeDom && this.type === \"popup\" && this.isOpen) {\n\t\tthis.positionPopup(domNode);\n\t\t$tw.utils.addClass(domNode,\"tc-popup\"); // Make sure that clicks don't dismiss popups within the revealed content\n\n\t}\n\tif(this.isOpen) {\n\t\tdomNode.removeAttribute(\"hidden\");\n $tw.anim.perform(this.openAnimation,domNode);\n\t} else {\n\t\t$tw.anim.perform(this.closeAnimation,domNode,{callback: function() {\n\t\t\tdomNode.setAttribute(\"hidden\",\"true\");\n }});\n\t}\n};\n\nexports.reveal = RevealWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/reveal.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/scrollable.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/scrollable.js\ntype: application/javascript\nmodule-type: widget\n\nScrollable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ScrollableWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n\tthis.scaleFactor = 1;\n\tthis.addEventListeners([\n\t\t{type: \"tm-scroll\", handler: \"handleScrollEvent\"}\n\t]);\n\tif($tw.browser) {\n\t\tthis.requestAnimationFrame = window.requestAnimationFrame ||\n\t\t\twindow.webkitRequestAnimationFrame ||\n\t\t\twindow.mozRequestAnimationFrame ||\n\t\t\tfunction(callback) {\n\t\t\t\treturn window.setTimeout(callback, 1000/60);\n\t\t\t};\n\t\tthis.cancelAnimationFrame = window.cancelAnimationFrame ||\n\t\t\twindow.webkitCancelAnimationFrame ||\n\t\t\twindow.webkitCancelRequestAnimationFrame ||\n\t\t\twindow.mozCancelAnimationFrame ||\n\t\t\twindow.mozCancelRequestAnimationFrame ||\n\t\t\tfunction(id) {\n\t\t\t\twindow.clearTimeout(id);\n\t\t\t};\n\t}\n};\n\n/*\nInherit from the base widget class\n*/\nScrollableWidget.prototype = new Widget();\n\nScrollableWidget.prototype.cancelScroll = function() {\n\tif(this.idRequestFrame) {\n\t\tthis.cancelAnimationFrame.call(window,this.idRequestFrame);\n\t\tthis.idRequestFrame = null;\n\t}\n};\n\n/*\nHandle a scroll event\n*/\nScrollableWidget.prototype.handleScrollEvent = function(event) {\n\t// Pass the scroll event through if our offsetsize is larger than our scrollsize\n\tif(this.outerDomNode.scrollWidth <= this.outerDomNode.offsetWidth && this.outerDomNode.scrollHeight <= this.outerDomNode.offsetHeight && this.fallthrough === \"yes\") {\n\t\treturn true;\n\t}\n\tthis.scrollIntoView(event.target);\n\treturn false; // Handled event\n};\n\n/*\nScroll an element into view\n*/\nScrollableWidget.prototype.scrollIntoView = function(element) {\n\tvar duration = $tw.utils.getAnimationDuration();\n\tthis.cancelScroll();\n\tthis.startTime = Date.now();\n\tvar scrollPosition = {\n\t\tx: this.outerDomNode.scrollLeft,\n\t\ty: this.outerDomNode.scrollTop\n\t};\n\t// Get the client bounds of the element and adjust by the scroll position\n\tvar scrollableBounds = this.outerDomNode.getBoundingClientRect(),\n\t\tclientTargetBounds = element.getBoundingClientRect(),\n\t\tbounds = {\n\t\t\tleft: clientTargetBounds.left + scrollPosition.x - scrollableBounds.left,\n\t\t\ttop: clientTargetBounds.top + scrollPosition.y - scrollableBounds.top,\n\t\t\twidth: clientTargetBounds.width,\n\t\t\theight: clientTargetBounds.height\n\t\t};\n\t// We'll consider the horizontal and vertical scroll directions separately via this function\n\tvar getEndPos = function(targetPos,targetSize,currentPos,currentSize) {\n\t\t\t// If the target is already visible then stay where we are\n\t\t\tif(targetPos >= currentPos && (targetPos + targetSize) <= (currentPos + currentSize)) {\n\t\t\t\treturn currentPos;\n\t\t\t// If the target is above/left of the current view, then scroll to its top/left\n\t\t\t} else if(targetPos <= currentPos) {\n\t\t\t\treturn targetPos;\n\t\t\t// If the target is smaller than the window and the scroll position is too far up, then scroll till the target is at the bottom of the window\n\t\t\t} else if(targetSize < currentSize && currentPos < (targetPos + targetSize - currentSize)) {\n\t\t\t\treturn targetPos + targetSize - currentSize;\n\t\t\t// If the target is big, then just scroll to the top\n\t\t\t} else if(currentPos < targetPos) {\n\t\t\t\treturn targetPos;\n\t\t\t// Otherwise, stay where we are\n\t\t\t} else {\n\t\t\t\treturn currentPos;\n\t\t\t}\n\t\t},\n\t\tendX = getEndPos(bounds.left,bounds.width,scrollPosition.x,this.outerDomNode.offsetWidth),\n\t\tendY = getEndPos(bounds.top,bounds.height,scrollPosition.y,this.outerDomNode.offsetHeight);\n\t// Only scroll if necessary\n\tif(endX !== scrollPosition.x || endY !== scrollPosition.y) {\n\t\tvar self = this,\n\t\t\tdrawFrame;\n\t\tdrawFrame = function () {\n\t\t\tvar t;\n\t\t\tif(duration <= 0) {\n\t\t\t\tt = 1;\n\t\t\t} else {\n\t\t\t\tt = ((Date.now()) - self.startTime) / duration;\t\n\t\t\t}\n\t\t\tif(t >= 1) {\n\t\t\t\tself.cancelScroll();\n\t\t\t\tt = 1;\n\t\t\t}\n\t\t\tt = $tw.utils.slowInSlowOut(t);\n\t\t\tself.outerDomNode.scrollLeft = scrollPosition.x + (endX - scrollPosition.x) * t;\n\t\t\tself.outerDomNode.scrollTop = scrollPosition.y + (endY - scrollPosition.y) * t;\n\t\t\tif(t < 1) {\n\t\t\t\tself.idRequestFrame = self.requestAnimationFrame.call(window,drawFrame);\n\t\t\t}\n\t\t};\n\t\tdrawFrame();\n\t}\n};\n\n/*\nRender this widget into the DOM\n*/\nScrollableWidget.prototype.render = function(parent,nextSibling) {\n\tvar self = this;\n\t// Remember parent\n\tthis.parentDomNode = parent;\n\t// Compute attributes and execute state\n\tthis.computeAttributes();\n\tthis.execute();\n\t// Create elements\n\tthis.outerDomNode = this.document.createElement(\"div\");\n\t$tw.utils.setStyle(this.outerDomNode,[\n\t\t{overflowY: \"auto\"},\n\t\t{overflowX: \"auto\"},\n\t\t{webkitOverflowScrolling: \"touch\"}\n\t]);\n\tthis.innerDomNode = this.document.createElement(\"div\");\n\tthis.outerDomNode.appendChild(this.innerDomNode);\n\t// Assign classes\n\tthis.outerDomNode.className = this[\"class\"] || \"\";\n\t// Insert element\n\tparent.insertBefore(this.outerDomNode,nextSibling);\n\tthis.renderChildren(this.innerDomNode,null);\n\tthis.domNodes.push(this.outerDomNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nScrollableWidget.prototype.execute = function() {\n\t// Get attributes\n\tthis.fallthrough = this.getAttribute(\"fallthrough\",\"yes\");\n\tthis[\"class\"] = this.getAttribute(\"class\");\n\t// Make child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nScrollableWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes[\"class\"]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports.scrollable = ScrollableWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/scrollable.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/select.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/select.js\ntype: application/javascript\nmodule-type: widget\n\nSelect widget:\n\n```\n<$select tiddler=\"MyTiddler\" field=\"text\">\n<$list filter=\"[tag[chapter]]\">\n<option value=<<currentTiddler>>>\n<$view field=\"description\"/>\n</option>\n</$list>\n</$select>\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar SelectWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nSelectWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nSelectWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n\tthis.setSelectValue();\n\t$tw.utils.addEventListeners(this.getSelectDomNode(),[\n\t\t{name: \"change\", handlerObject: this, handlerMethod: \"handleChangeEvent\"}\n\t]);\n};\n\n/*\nHandle a change event\n*/\nSelectWidget.prototype.handleChangeEvent = function(event) {\n\t// Get the new value and assign it to the tiddler\n\tif(this.selectMultiple == false) {\n\t\tvar value = this.getSelectDomNode().value;\n\t} else {\n\t\tvar value = this.getSelectValues()\n\t\t\t\tvalue = $tw.utils.stringifyList(value);\n\t}\n\tthis.wiki.setText(this.selectTitle,this.selectField,this.selectIndex,value);\n\t// Trigger actions\n\tif(this.selectActions) {\n\t\tthis.invokeActionString(this.selectActions,this,event);\n\t}\n};\n\n/*\nIf necessary, set the value of the select element to the current value\n*/\nSelectWidget.prototype.setSelectValue = function() {\n\tvar value = this.selectDefault;\n\t// Get the value\n\tif(this.selectIndex) {\n\t\tvalue = this.wiki.extractTiddlerDataItem(this.selectTitle,this.selectIndex);\n\t} else {\n\t\tvar tiddler = this.wiki.getTiddler(this.selectTitle);\n\t\tif(tiddler) {\n\t\t\tif(this.selectField === \"text\") {\n\t\t\t\t// Calling getTiddlerText() triggers lazy loading of skinny tiddlers\n\t\t\t\tvalue = this.wiki.getTiddlerText(this.selectTitle);\n\t\t\t} else {\n\t\t\t\tif($tw.utils.hop(tiddler.fields,this.selectField)) {\n\t\t\t\t\tvalue = tiddler.getFieldString(this.selectField);\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tif(this.selectField === \"title\") {\n\t\t\t\tvalue = this.selectTitle;\n\t\t\t}\n\t\t}\n\t}\n\t// Assign it to the select element if it's different than the current value\n\tif (this.selectMultiple) {\n\t\tvalue = value === undefined ? \"\" : value;\n\t\tvar select = this.getSelectDomNode();\n\t\tvar values = Array.isArray(value) ? value : $tw.utils.parseStringArray(value);\n\t\tfor(var i=0; i < select.children.length; i++){\n\t\t\tif(values.indexOf(select.children[i].value) != -1) {\n\t\t\t\tselect.children[i].selected = true;\n\t\t\t}\n\t\t}\n\t\t\n\t} else {\n\t\tvar domNode = this.getSelectDomNode();\n\t\tif(domNode.value !== value) {\n\t\t\tdomNode.value = value;\n\t\t}\n\t}\n};\n\n/*\nGet the DOM node of the select element\n*/\nSelectWidget.prototype.getSelectDomNode = function() {\n\treturn this.children[0].domNodes[0];\n};\n\n// Return an array of the selected opion values\n// select is an HTML select element\nSelectWidget.prototype.getSelectValues = function() {\n\tvar select, result, options, opt;\n\tselect = this.getSelectDomNode();\n\tresult = [];\n\toptions = select && select.options;\n\tfor (var i=0; i<options.length; i++) {\n\t\topt = options[i];\n\t\tif (opt.selected) {\n\t\t\tresult.push(opt.value || opt.text);\n\t\t}\n\t}\n\treturn result;\n}\n\n/*\nCompute the internal state of the widget\n*/\nSelectWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.selectActions = this.getAttribute(\"actions\");\n\tthis.selectTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.selectField = this.getAttribute(\"field\",\"text\");\n\tthis.selectIndex = this.getAttribute(\"index\");\n\tthis.selectClass = this.getAttribute(\"class\");\n\tthis.selectDefault = this.getAttribute(\"default\");\n\tthis.selectMultiple = this.getAttribute(\"multiple\", false);\n\tthis.selectSize = this.getAttribute(\"size\");\n\t// Make the child widgets\n\tvar selectNode = {\n\t\ttype: \"element\",\n\t\ttag: \"select\",\n\t\tchildren: this.parseTreeNode.children\n\t};\n\tif(this.selectClass) {\n\t\t$tw.utils.addAttributeToParseTreeNode(selectNode,\"class\",this.selectClass);\n\t}\n\tif(this.selectMultiple) {\n\t\t$tw.utils.addAttributeToParseTreeNode(selectNode,\"multiple\",\"multiple\");\n\t}\n\tif(this.selectSize) {\n\t\t$tw.utils.addAttributeToParseTreeNode(selectNode,\"size\",this.selectSize);\n\t}\n\tthis.makeChildWidgets([selectNode]);\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nSelectWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\t// If we're using a different tiddler/field/index then completely refresh ourselves\n\tif(changedAttributes.selectTitle || changedAttributes.selectField || changedAttributes.selectIndex) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t// If the target tiddler value has changed, just update setting and refresh the children\n\t} else {\n\t\tvar childrenRefreshed = this.refreshChildren(changedTiddlers);\n\t\tif(changedTiddlers[this.selectTitle] || childrenRefreshed) {\n\t\t\tthis.setSelectValue();\n\t\t} \n\t\treturn childrenRefreshed;\n\t}\n};\n\nexports.select = SelectWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/select.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/set.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/set.js\ntype: application/javascript\nmodule-type: widget\n\nSet variable widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar SetWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nSetWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nSetWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nSetWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.setName = this.getAttribute(\"name\",\"currentTiddler\");\n\tthis.setFilter = this.getAttribute(\"filter\");\n\tthis.setSelect = this.getAttribute(\"select\");\n\tthis.setValue = this.getAttribute(\"value\");\n\tthis.setEmptyValue = this.getAttribute(\"emptyValue\");\n\t// Set context variable\n\tthis.setVariable(this.setName,this.getValue(),this.parseTreeNode.params);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nGet the value to be assigned\n*/\nSetWidget.prototype.getValue = function() {\n\tvar value = this.setValue;\n\tif(this.setFilter) {\n\t\tvar results = this.wiki.filterTiddlers(this.setFilter,this);\n\t\tif(!this.setValue) {\n\t\t\tvar select;\n\t\t\tif(this.setSelect) {\n\t\t\t\tselect = parseInt(this.setSelect,10);\n\t\t\t}\n\t\t\tif(select !== undefined) {\n\t\t\t\tvalue = results[select] || \"\";\n\t\t\t} else {\n\t\t\t\tvalue = $tw.utils.stringifyList(results);\t\t\t\n\t\t\t}\n\t\t}\n\t\tif(results.length === 0 && this.setEmptyValue !== undefined) {\n\t\t\tvalue = this.setEmptyValue;\n\t\t}\n\t} else if(!value && this.setEmptyValue) {\n\t\tvalue = this.setEmptyValue;\n\t}\n\treturn value;\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nSetWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.name || changedAttributes.filter || changedAttributes.select ||changedAttributes.value || changedAttributes.emptyValue ||\n\t (this.setFilter && this.getValue() != this.variables[this.setName].value)) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.setvariable = SetWidget;\nexports.set = SetWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/set.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/text.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/text.js\ntype: application/javascript\nmodule-type: widget\n\nText node widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar TextNodeWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nTextNodeWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nTextNodeWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tvar text = this.getAttribute(\"text\",this.parseTreeNode.text || \"\");\n\ttext = text.replace(/\\r/mg,\"\");\n\tvar textNode = this.document.createTextNode(text);\n\tparent.insertBefore(textNode,nextSibling);\n\tthis.domNodes.push(textNode);\n};\n\n/*\nCompute the internal state of the widget\n*/\nTextNodeWidget.prototype.execute = function() {\n\t// Nothing to do for a text node\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nTextNodeWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.text) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports.text = TextNodeWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/text.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/tiddler.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/tiddler.js\ntype: application/javascript\nmodule-type: widget\n\nTiddler widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar TiddlerWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nTiddlerWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nTiddlerWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nTiddlerWidget.prototype.execute = function() {\n\tthis.tiddlerState = this.computeTiddlerState();\n\tthis.setVariable(\"currentTiddler\",this.tiddlerState.currentTiddler);\n\tthis.setVariable(\"missingTiddlerClass\",this.tiddlerState.missingTiddlerClass);\n\tthis.setVariable(\"shadowTiddlerClass\",this.tiddlerState.shadowTiddlerClass);\n\tthis.setVariable(\"systemTiddlerClass\",this.tiddlerState.systemTiddlerClass);\n\tthis.setVariable(\"tiddlerTagClasses\",this.tiddlerState.tiddlerTagClasses);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nCompute the tiddler state flags\n*/\nTiddlerWidget.prototype.computeTiddlerState = function() {\n\t// Get our parameters\n\tthis.tiddlerTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\t// Compute the state\n\tvar state = {\n\t\tcurrentTiddler: this.tiddlerTitle || \"\",\n\t\tmissingTiddlerClass: (this.wiki.tiddlerExists(this.tiddlerTitle) || this.wiki.isShadowTiddler(this.tiddlerTitle)) ? \"tc-tiddler-exists\" : \"tc-tiddler-missing\",\n\t\tshadowTiddlerClass: this.wiki.isShadowTiddler(this.tiddlerTitle) ? \"tc-tiddler-shadow\" : \"\",\n\t\tsystemTiddlerClass: this.wiki.isSystemTiddler(this.tiddlerTitle) ? \"tc-tiddler-system\" : \"\",\n\t\ttiddlerTagClasses: this.getTagClasses()\n\t};\n\t// Compute a simple hash to make it easier to detect changes\n\tstate.hash = state.currentTiddler + state.missingTiddlerClass + state.shadowTiddlerClass + state.systemTiddlerClass + state.tiddlerTagClasses;\n\treturn state;\n};\n\n/*\nCreate a string of CSS classes derived from the tags of the current tiddler\n*/\nTiddlerWidget.prototype.getTagClasses = function() {\n\tvar tiddler = this.wiki.getTiddler(this.tiddlerTitle);\n\tif(tiddler) {\n\t\tvar tags = [];\n\t\t$tw.utils.each(tiddler.fields.tags,function(tag) {\n\t\t\ttags.push(\"tc-tagged-\" + encodeURIComponent(tag));\n\t\t});\n\t\treturn tags.join(\" \");\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nTiddlerWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes(),\n\t\tnewTiddlerState = this.computeTiddlerState();\n\tif(changedAttributes.tiddler || newTiddlerState.hash !== this.tiddlerState.hash) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nexports.tiddler = TiddlerWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/tiddler.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/transclude.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/transclude.js\ntype: application/javascript\nmodule-type: widget\n\nTransclude widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar TranscludeWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nTranscludeWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nTranscludeWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nTranscludeWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.transcludeTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.transcludeSubTiddler = this.getAttribute(\"subtiddler\");\n\tthis.transcludeField = this.getAttribute(\"field\");\n\tthis.transcludeIndex = this.getAttribute(\"index\");\n\tthis.transcludeMode = this.getAttribute(\"mode\");\n\t// Parse the text reference\n\tvar parseAsInline = !this.parseTreeNode.isBlock;\n\tif(this.transcludeMode === \"inline\") {\n\t\tparseAsInline = true;\n\t} else if(this.transcludeMode === \"block\") {\n\t\tparseAsInline = false;\n\t}\n\tvar parser = this.wiki.parseTextReference(\n\t\t\t\t\t\tthis.transcludeTitle,\n\t\t\t\t\t\tthis.transcludeField,\n\t\t\t\t\t\tthis.transcludeIndex,\n\t\t\t\t\t\t{\n\t\t\t\t\t\t\tparseAsInline: parseAsInline,\n\t\t\t\t\t\t\tsubTiddler: this.transcludeSubTiddler\n\t\t\t\t\t\t}),\n\t\tparseTreeNodes = parser ? parser.tree : this.parseTreeNode.children;\n\t// Set context variables for recursion detection\n\tvar recursionMarker = this.makeRecursionMarker();\n\tthis.setVariable(\"transclusion\",recursionMarker);\n\t// Check for recursion\n\tif(parser) {\n\t\tif(this.parentWidget && this.parentWidget.hasVariable(\"transclusion\",recursionMarker)) {\n\t\t\tparseTreeNodes = [{type: \"element\", tag: \"span\", attributes: {\n\t\t\t\t\"class\": {type: \"string\", value: \"tc-error\"}\n\t\t\t}, children: [\n\t\t\t\t{type: \"text\", text: $tw.language.getString(\"Error/RecursiveTransclusion\")}\n\t\t\t]}];\n\t\t}\n\t}\n\t// Construct the child widgets\n\tthis.makeChildWidgets(parseTreeNodes);\n};\n\n/*\nCompose a string comprising the title, field and/or index to identify this transclusion for recursion detection\n*/\nTranscludeWidget.prototype.makeRecursionMarker = function() {\n\tvar output = [];\n\toutput.push(\"{\");\n\toutput.push(this.getVariable(\"currentTiddler\",{defaultValue: \"\"}));\n\toutput.push(\"|\");\n\toutput.push(this.transcludeTitle || \"\");\n\toutput.push(\"|\");\n\toutput.push(this.transcludeField || \"\");\n\toutput.push(\"|\");\n\toutput.push(this.transcludeIndex || \"\");\n\toutput.push(\"|\");\n\toutput.push(this.transcludeSubTiddler || \"\");\n\toutput.push(\"}\");\n\treturn output.join(\"\");\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nTranscludeWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedTiddlers[this.transcludeTitle]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn this.refreshChildren(changedTiddlers);\t\t\n\t}\n};\n\nexports.transclude = TranscludeWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/transclude.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/vars.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/vars.js\ntype: application/javascript\nmodule-type: widget\n\nThis widget allows multiple variables to be set in one go:\n\n```\n\\define helloworld() Hello world!\n<$vars greeting=\"Hi\" me={{!!title}} sentence=<<helloworld>>>\n <<greeting>>! I am <<me>> and I say: <<sentence>>\n</$vars>\n```\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar VarsWidget = function(parseTreeNode,options) {\n\t// Call the constructor\n\tWidget.call(this);\n\t// Initialise\t\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nVarsWidget.prototype = Object.create(Widget.prototype);\n\n/*\nRender this widget into the DOM\n*/\nVarsWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nVarsWidget.prototype.execute = function() {\n\t// Parse variables\n\tvar self = this;\n\t$tw.utils.each(this.attributes,function(val,key) {\n\t\tif(key.charAt(0) !== \"$\") {\n\t\t\tself.setVariable(key,val);\n\t\t}\n\t});\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nRefresh the widget by ensuring our attributes are up to date\n*/\nVarsWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(Object.keys(changedAttributes).length) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t}\n\treturn this.refreshChildren(changedTiddlers);\n};\n\nexports[\"vars\"] = VarsWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/vars.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/view.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/view.js\ntype: application/javascript\nmodule-type: widget\n\nView widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar ViewWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nViewWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nViewWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tif(this.text) {\n\t\tvar textNode = this.document.createTextNode(this.text);\n\t\tparent.insertBefore(textNode,nextSibling);\n\t\tthis.domNodes.push(textNode);\n\t} else {\n\t\tthis.makeChildWidgets();\n\t\tthis.renderChildren(parent,nextSibling);\n\t}\n};\n\n/*\nCompute the internal state of the widget\n*/\nViewWidget.prototype.execute = function() {\n\t// Get parameters from our attributes\n\tthis.viewTitle = this.getAttribute(\"tiddler\",this.getVariable(\"currentTiddler\"));\n\tthis.viewSubtiddler = this.getAttribute(\"subtiddler\");\n\tthis.viewField = this.getAttribute(\"field\",\"text\");\n\tthis.viewIndex = this.getAttribute(\"index\");\n\tthis.viewFormat = this.getAttribute(\"format\",\"text\");\n\tthis.viewTemplate = this.getAttribute(\"template\",\"\");\n\tswitch(this.viewFormat) {\n\t\tcase \"htmlwikified\":\n\t\t\tthis.text = this.getValueAsHtmlWikified();\n\t\t\tbreak;\n\t\tcase \"plainwikified\":\n\t\t\tthis.text = this.getValueAsPlainWikified();\n\t\t\tbreak;\n\t\tcase \"htmlencodedplainwikified\":\n\t\t\tthis.text = this.getValueAsHtmlEncodedPlainWikified();\n\t\t\tbreak;\n\t\tcase \"htmlencoded\":\n\t\t\tthis.text = this.getValueAsHtmlEncoded();\n\t\t\tbreak;\n\t\tcase \"urlencoded\":\n\t\t\tthis.text = this.getValueAsUrlEncoded();\n\t\t\tbreak;\n\t\tcase \"doubleurlencoded\":\n\t\t\tthis.text = this.getValueAsDoubleUrlEncoded();\n\t\t\tbreak;\n\t\tcase \"date\":\n\t\t\tthis.text = this.getValueAsDate(this.viewTemplate);\n\t\t\tbreak;\n\t\tcase \"relativedate\":\n\t\t\tthis.text = this.getValueAsRelativeDate();\n\t\t\tbreak;\n\t\tcase \"stripcomments\":\n\t\t\tthis.text = this.getValueAsStrippedComments();\n\t\t\tbreak;\n\t\tcase \"jsencoded\":\n\t\t\tthis.text = this.getValueAsJsEncoded();\n\t\t\tbreak;\n\t\tdefault: // \"text\"\n\t\t\tthis.text = this.getValueAsText();\n\t\t\tbreak;\n\t}\n};\n\n/*\nThe various formatter functions are baked into this widget for the moment. Eventually they will be replaced by macro functions\n*/\n\n/*\nRetrieve the value of the widget. Options are:\nasString: Optionally return the value as a string\n*/\nViewWidget.prototype.getValue = function(options) {\n\toptions = options || {};\n\tvar value = options.asString ? \"\" : undefined;\n\tif(this.viewIndex) {\n\t\tvalue = this.wiki.extractTiddlerDataItem(this.viewTitle,this.viewIndex);\n\t} else {\n\t\tvar tiddler;\n\t\tif(this.viewSubtiddler) {\n\t\t\ttiddler = this.wiki.getSubTiddler(this.viewTitle,this.viewSubtiddler);\t\n\t\t} else {\n\t\t\ttiddler = this.wiki.getTiddler(this.viewTitle);\n\t\t}\n\t\tif(tiddler) {\n\t\t\tif(this.viewField === \"text\" && !this.viewSubtiddler) {\n\t\t\t\t// Calling getTiddlerText() triggers lazy loading of skinny tiddlers\n\t\t\t\tvalue = this.wiki.getTiddlerText(this.viewTitle);\n\t\t\t} else {\n\t\t\t\tif($tw.utils.hop(tiddler.fields,this.viewField)) {\n\t\t\t\t\tif(options.asString) {\n\t\t\t\t\t\tvalue = tiddler.getFieldString(this.viewField);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tvalue = tiddler.fields[this.viewField];\t\t\t\t\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t} else {\n\t\t\tif(this.viewField === \"title\") {\n\t\t\t\tvalue = this.viewTitle;\n\t\t\t}\n\t\t}\n\t}\n\treturn value;\n};\n\nViewWidget.prototype.getValueAsText = function() {\n\treturn this.getValue({asString: true});\n};\n\nViewWidget.prototype.getValueAsHtmlWikified = function() {\n\treturn this.wiki.renderText(\"text/html\",\"text/vnd.tiddlywiki\",this.getValueAsText(),{parentWidget: this});\n};\n\nViewWidget.prototype.getValueAsPlainWikified = function() {\n\treturn this.wiki.renderText(\"text/plain\",\"text/vnd.tiddlywiki\",this.getValueAsText(),{parentWidget: this});\n};\n\nViewWidget.prototype.getValueAsHtmlEncodedPlainWikified = function() {\n\treturn $tw.utils.htmlEncode(this.wiki.renderText(\"text/plain\",\"text/vnd.tiddlywiki\",this.getValueAsText(),{parentWidget: this}));\n};\n\nViewWidget.prototype.getValueAsHtmlEncoded = function() {\n\treturn $tw.utils.htmlEncode(this.getValueAsText());\n};\n\nViewWidget.prototype.getValueAsUrlEncoded = function() {\n\treturn encodeURIComponent(this.getValueAsText());\n};\n\nViewWidget.prototype.getValueAsDoubleUrlEncoded = function() {\n\treturn encodeURIComponent(encodeURIComponent(this.getValueAsText()));\n};\n\nViewWidget.prototype.getValueAsDate = function(format) {\n\tformat = format || \"YYYY MM DD 0hh:0mm\";\n\tvar value = $tw.utils.parseDate(this.getValue());\n\tif(value && $tw.utils.isDate(value) && value.toString() !== \"Invalid Date\") {\n\t\treturn $tw.utils.formatDateString(value,format);\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\nViewWidget.prototype.getValueAsRelativeDate = function(format) {\n\tvar value = $tw.utils.parseDate(this.getValue());\n\tif(value && $tw.utils.isDate(value) && value.toString() !== \"Invalid Date\") {\n\t\treturn $tw.utils.getRelativeDate((new Date()) - (new Date(value))).description;\n\t} else {\n\t\treturn \"\";\n\t}\n};\n\nViewWidget.prototype.getValueAsStrippedComments = function() {\n\tvar lines = this.getValueAsText().split(\"\\n\"),\n\t\tout = [];\n\tfor(var line=0; line<lines.length; line++) {\n\t\tvar text = lines[line];\n\t\tif(!/^\\s*\\/\\/#/.test(text)) {\n\t\t\tout.push(text);\n\t\t}\n\t}\n\treturn out.join(\"\\n\");\n};\n\nViewWidget.prototype.getValueAsJsEncoded = function() {\n\treturn $tw.utils.stringify(this.getValueAsText());\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nViewWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\tif(changedAttributes.tiddler || changedAttributes.field || changedAttributes.index || changedAttributes.template || changedAttributes.format || changedTiddlers[this.viewTitle]) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\treturn false;\t\n\t}\n};\n\nexports.view = ViewWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/view.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/widget.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/widget.js\ntype: application/javascript\nmodule-type: widget\n\nWidget base class\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nCreate a widget object for a parse tree node\n\tparseTreeNode: reference to the parse tree node to be rendered\n\toptions: see below\nOptions include:\n\twiki: mandatory reference to wiki associated with this render tree\n\tparentWidget: optional reference to a parent renderer node for the context chain\n\tdocument: optional document object to use instead of global document\n*/\nvar Widget = function(parseTreeNode,options) {\n\tif(arguments.length > 0) {\n\t\tthis.initialise(parseTreeNode,options);\n\t}\n};\n\n/*\nInitialise widget properties. These steps are pulled out of the constructor so that we can reuse them in subclasses\n*/\nWidget.prototype.initialise = function(parseTreeNode,options) {\n\toptions = options || {};\n\t// Save widget info\n\tthis.parseTreeNode = parseTreeNode;\n\tthis.wiki = options.wiki;\n\tthis.parentWidget = options.parentWidget;\n\tthis.variablesConstructor = function() {};\n\tthis.variablesConstructor.prototype = this.parentWidget ? this.parentWidget.variables : {};\n\tthis.variables = new this.variablesConstructor();\n\tthis.document = options.document;\n\tthis.attributes = {};\n\tthis.children = [];\n\tthis.domNodes = [];\n\tthis.eventListeners = {};\n\t// Hashmap of the widget classes\n\tif(!this.widgetClasses) {\n\t\tWidget.prototype.widgetClasses = $tw.modules.applyMethods(\"widget\");\n\t}\n};\n\n/*\nRender this widget into the DOM\n*/\nWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nWidget.prototype.execute = function() {\n\tthis.makeChildWidgets();\n};\n\n/*\nSet the value of a context variable\nname: name of the variable\nvalue: value of the variable\nparams: array of {name:, default:} for each parameter\n*/\nWidget.prototype.setVariable = function(name,value,params) {\n\tthis.variables[name] = {value: value, params: params};\n};\n\n/*\nGet the prevailing value of a context variable\nname: name of variable\noptions: see below\nOptions include\nparams: array of {name:, value:} for each parameter\ndefaultValue: default value if the variable is not defined\n*/\nWidget.prototype.getVariable = function(name,options) {\n\toptions = options || {};\n\tvar actualParams = options.params || [],\n\t\tparentWidget = this.parentWidget;\n\t// Check for the variable defined in the parent widget (or an ancestor in the prototype chain)\n\tif(parentWidget && name in parentWidget.variables) {\n\t\tvar variable = parentWidget.variables[name],\n\t\t\tvalue = variable.value;\n\t\t// Substitute any parameters specified in the definition\n\t\tvalue = this.substituteVariableParameters(value,variable.params,actualParams);\n\t\tvalue = this.substituteVariableReferences(value);\n\t\treturn value;\n\t}\n\t// If the variable doesn't exist in the parent widget then look for a macro module\n\treturn this.evaluateMacroModule(name,actualParams,options.defaultValue);\n};\n\nWidget.prototype.substituteVariableParameters = function(text,formalParams,actualParams) {\n\tif(formalParams) {\n\t\tvar nextAnonParameter = 0, // Next candidate anonymous parameter in macro call\n\t\t\tparamInfo, paramValue;\n\t\t// Step through each of the parameters in the macro definition\n\t\tfor(var p=0; p<formalParams.length; p++) {\n\t\t\t// Check if we've got a macro call parameter with the same name\n\t\t\tparamInfo = formalParams[p];\n\t\t\tparamValue = undefined;\n\t\t\tfor(var m=0; m<actualParams.length; m++) {\n\t\t\t\tif(actualParams[m].name === paramInfo.name) {\n\t\t\t\t\tparamValue = actualParams[m].value;\n\t\t\t\t}\n\t\t\t}\n\t\t\t// If not, use the next available anonymous macro call parameter\n\t\t\twhile(nextAnonParameter < actualParams.length && actualParams[nextAnonParameter].name) {\n\t\t\t\tnextAnonParameter++;\n\t\t\t}\n\t\t\tif(paramValue === undefined && nextAnonParameter < actualParams.length) {\n\t\t\t\tparamValue = actualParams[nextAnonParameter++].value;\n\t\t\t}\n\t\t\t// If we've still not got a value, use the default, if any\n\t\t\tparamValue = paramValue || paramInfo[\"default\"] || \"\";\n\t\t\t// Replace any instances of this parameter\n\t\t\ttext = $tw.utils.replaceString(text,new RegExp(\"\\\\$\" + $tw.utils.escapeRegExp(paramInfo.name) + \"\\\\$\",\"mg\"),paramValue);\n\t\t}\n\t}\n\treturn text;\n};\n\nWidget.prototype.substituteVariableReferences = function(text) {\n\tvar self = this;\n\treturn (text || \"\").replace(/\\$\\(([^\\)\\$]+)\\)\\$/g,function(match,p1,offset,string) {\n\t\treturn self.getVariable(p1,{defaultValue: \"\"});\n\t});\n};\n\nWidget.prototype.evaluateMacroModule = function(name,actualParams,defaultValue) {\n\tif($tw.utils.hop($tw.macros,name)) {\n\t\tvar macro = $tw.macros[name],\n\t\t\targs = [];\n\t\tif(macro.params.length > 0) {\n\t\t\tvar nextAnonParameter = 0, // Next candidate anonymous parameter in macro call\n\t\t\t\tparamInfo, paramValue;\n\t\t\t// Step through each of the parameters in the macro definition\n\t\t\tfor(var p=0; p<macro.params.length; p++) {\n\t\t\t\t// Check if we've got a macro call parameter with the same name\n\t\t\t\tparamInfo = macro.params[p];\n\t\t\t\tparamValue = undefined;\n\t\t\t\tfor(var m=0; m<actualParams.length; m++) {\n\t\t\t\t\tif(actualParams[m].name === paramInfo.name) {\n\t\t\t\t\t\tparamValue = actualParams[m].value;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\t// If not, use the next available anonymous macro call parameter\n\t\t\t\twhile(nextAnonParameter < actualParams.length && actualParams[nextAnonParameter].name) {\n\t\t\t\t\tnextAnonParameter++;\n\t\t\t\t}\n\t\t\t\tif(paramValue === undefined && nextAnonParameter < actualParams.length) {\n\t\t\t\t\tparamValue = actualParams[nextAnonParameter++].value;\n\t\t\t\t}\n\t\t\t\t// If we've still not got a value, use the default, if any\n\t\t\t\tparamValue = paramValue || paramInfo[\"default\"] || \"\";\n\t\t\t\t// Save the parameter\n\t\t\t\targs.push(paramValue);\n\t\t\t}\n\t\t}\n\t\telse for(var i=0; i<actualParams.length; ++i) {\n\t\t\targs.push(actualParams[i].value);\n\t\t}\n\t\treturn (macro.run.apply(this,args) || \"\").toString();\n\t} else {\n\t\treturn defaultValue;\n\t}\n};\n\n/*\nCheck whether a given context variable value exists in the parent chain\n*/\nWidget.prototype.hasVariable = function(name,value) {\n\tvar node = this;\n\twhile(node) {\n\t\tif($tw.utils.hop(node.variables,name) && node.variables[name].value === value) {\n\t\t\treturn true;\n\t\t}\n\t\tnode = node.parentWidget;\n\t}\n\treturn false;\n};\n\n/*\nConstruct a qualifying string based on a hash of concatenating the values of a given variable in the parent chain\n*/\nWidget.prototype.getStateQualifier = function(name) {\n\tthis.qualifiers = this.qualifiers || Object.create(null);\n\tname = name || \"transclusion\";\n\tif(this.qualifiers[name]) {\n\t\treturn this.qualifiers[name];\n\t} else {\n\t\tvar output = [],\n\t\t\tnode = this;\n\t\twhile(node && node.parentWidget) {\n\t\t\tif($tw.utils.hop(node.parentWidget.variables,name)) {\n\t\t\t\toutput.push(node.getVariable(name));\n\t\t\t}\n\t\t\tnode = node.parentWidget;\n\t\t}\n\t\tvar value = $tw.utils.hashString(output.join(\"\"));\n\t\tthis.qualifiers[name] = value;\n\t\treturn value;\n\t}\n};\n\n/*\nCompute the current values of the attributes of the widget. Returns a hashmap of the names of the attributes that have changed\n*/\nWidget.prototype.computeAttributes = function() {\n\tvar changedAttributes = {},\n\t\tself = this,\n\t\tvalue;\n\t$tw.utils.each(this.parseTreeNode.attributes,function(attribute,name) {\n\t\tif(attribute.type === \"filtered\") {\n\t\t\tvalue = self.wiki.filterTiddlers(attribute.filter,self)[0] || \"\";\n\t\t} else if(attribute.type === \"indirect\") {\n\t\t\tvalue = self.wiki.getTextReference(attribute.textReference,\"\",self.getVariable(\"currentTiddler\"));\n\t\t} else if(attribute.type === \"macro\") {\n\t\t\tvalue = self.getVariable(attribute.value.name,{params: attribute.value.params});\n\t\t} else { // String attribute\n\t\t\tvalue = attribute.value;\n\t\t}\n\t\t// Check whether the attribute has changed\n\t\tif(self.attributes[name] !== value) {\n\t\t\tself.attributes[name] = value;\n\t\t\tchangedAttributes[name] = true;\n\t\t}\n\t});\n\treturn changedAttributes;\n};\n\n/*\nCheck for the presence of an attribute\n*/\nWidget.prototype.hasAttribute = function(name) {\n\treturn $tw.utils.hop(this.attributes,name);\n};\n\n/*\nGet the value of an attribute\n*/\nWidget.prototype.getAttribute = function(name,defaultText) {\n\tif($tw.utils.hop(this.attributes,name)) {\n\t\treturn this.attributes[name];\n\t} else {\n\t\treturn defaultText;\n\t}\n};\n\n/*\nAssign the computed attributes of the widget to a domNode\noptions include:\nexcludeEventAttributes: ignores attributes whose name begins with \"on\"\n*/\nWidget.prototype.assignAttributes = function(domNode,options) {\n\toptions = options || {};\n\tvar self = this;\n\t$tw.utils.each(this.attributes,function(v,a) {\n\t\t// Check exclusions\n\t\tif(options.excludeEventAttributes && a.substr(0,2) === \"on\") {\n\t\t\tv = undefined;\n\t\t}\n\t\tif(v !== undefined) {\n\t\t\tvar b = a.split(\":\");\n\t\t\t// Setting certain attributes can cause a DOM error (eg xmlns on the svg element)\n\t\t\ttry {\n\t\t\t\tif (b.length == 2 && b[0] == \"xlink\"){\n\t\t\t\t\tdomNode.setAttributeNS(\"http://www.w3.org/1999/xlink\",b[1],v);\n\t\t\t\t} else {\n\t\t\t\t\tdomNode.setAttributeNS(null,a,v);\n\t\t\t\t}\n\t\t\t} catch(e) {\n\t\t\t}\n\t\t}\n\t});\n};\n\n/*\nMake child widgets correspondng to specified parseTreeNodes\n*/\nWidget.prototype.makeChildWidgets = function(parseTreeNodes) {\n\tthis.children = [];\n\tvar self = this;\n\t$tw.utils.each(parseTreeNodes || (this.parseTreeNode && this.parseTreeNode.children),function(childNode) {\n\t\tself.children.push(self.makeChildWidget(childNode));\n\t});\n};\n\n/*\nConstruct the widget object for a parse tree node\n*/\nWidget.prototype.makeChildWidget = function(parseTreeNode) {\n\tvar WidgetClass = this.widgetClasses[parseTreeNode.type];\n\tif(!WidgetClass) {\n\t\tWidgetClass = this.widgetClasses.text;\n\t\tparseTreeNode = {type: \"text\", text: \"Undefined widget '\" + parseTreeNode.type + \"'\"};\n\t}\n\treturn new WidgetClass(parseTreeNode,{\n\t\twiki: this.wiki,\n\t\tvariables: {},\n\t\tparentWidget: this,\n\t\tdocument: this.document\n\t});\n};\n\n/*\nGet the next sibling of this widget\n*/\nWidget.prototype.nextSibling = function() {\n\tif(this.parentWidget) {\n\t\tvar index = this.parentWidget.children.indexOf(this);\n\t\tif(index !== -1 && index < this.parentWidget.children.length-1) {\n\t\t\treturn this.parentWidget.children[index+1];\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nGet the previous sibling of this widget\n*/\nWidget.prototype.previousSibling = function() {\n\tif(this.parentWidget) {\n\t\tvar index = this.parentWidget.children.indexOf(this);\n\t\tif(index !== -1 && index > 0) {\n\t\t\treturn this.parentWidget.children[index-1];\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nRender the children of this widget into the DOM\n*/\nWidget.prototype.renderChildren = function(parent,nextSibling) {\n\t$tw.utils.each(this.children,function(childWidget) {\n\t\tchildWidget.render(parent,nextSibling);\n\t});\n};\n\n/*\nAdd a list of event listeners from an array [{type:,handler:},...]\n*/\nWidget.prototype.addEventListeners = function(listeners) {\n\tvar self = this;\n\t$tw.utils.each(listeners,function(listenerInfo) {\n\t\tself.addEventListener(listenerInfo.type,listenerInfo.handler);\n\t});\n};\n\n/*\nAdd an event listener\n*/\nWidget.prototype.addEventListener = function(type,handler) {\n\tvar self = this;\n\tif(typeof handler === \"string\") { // The handler is a method name on this widget\n\t\tthis.eventListeners[type] = function(event) {\n\t\t\treturn self[handler].call(self,event);\n\t\t};\n\t} else { // The handler is a function\n\t\tthis.eventListeners[type] = function(event) {\n\t\t\treturn handler.call(self,event);\n\t\t};\n\t}\n};\n\n/*\nDispatch an event to a widget. If the widget doesn't handle the event then it is also dispatched to the parent widget\n*/\nWidget.prototype.dispatchEvent = function(event) {\n\t// Dispatch the event if this widget handles it\n\tvar listener = this.eventListeners[event.type];\n\tif(listener) {\n\t\t// Don't propagate the event if the listener returned false\n\t\tif(!listener(event)) {\n\t\t\treturn false;\n\t\t}\n\t}\n\t// Dispatch the event to the parent widget\n\tif(this.parentWidget) {\n\t\treturn this.parentWidget.dispatchEvent(event);\n\t}\n\treturn true;\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nWidget.prototype.refresh = function(changedTiddlers) {\n\treturn this.refreshChildren(changedTiddlers);\n};\n\n/*\nRebuild a previously rendered widget\n*/\nWidget.prototype.refreshSelf = function() {\n\tvar nextSibling = this.findNextSiblingDomNode();\n\tthis.removeChildDomNodes();\n\tthis.render(this.parentDomNode,nextSibling);\n};\n\n/*\nRefresh all the children of a widget\n*/\nWidget.prototype.refreshChildren = function(changedTiddlers) {\n\tvar self = this,\n\t\trefreshed = false;\n\t$tw.utils.each(this.children,function(childWidget) {\n\t\trefreshed = childWidget.refresh(changedTiddlers) || refreshed;\n\t});\n\treturn refreshed;\n};\n\n/*\nFind the next sibling in the DOM to this widget. This is done by scanning the widget tree through all next siblings and their descendents that share the same parent DOM node\n*/\nWidget.prototype.findNextSiblingDomNode = function(startIndex) {\n\t// Refer to this widget by its index within its parents children\n\tvar parent = this.parentWidget,\n\t\tindex = startIndex !== undefined ? startIndex : parent.children.indexOf(this);\nif(index === -1) {\n\tthrow \"node not found in parents children\";\n}\n\t// Look for a DOM node in the later siblings\n\twhile(++index < parent.children.length) {\n\t\tvar domNode = parent.children[index].findFirstDomNode();\n\t\tif(domNode) {\n\t\t\treturn domNode;\n\t\t}\n\t}\n\t// Go back and look for later siblings of our parent if it has the same parent dom node\n\tvar grandParent = parent.parentWidget;\n\tif(grandParent && parent.parentDomNode === this.parentDomNode) {\n\t\tindex = grandParent.children.indexOf(parent);\n\t\tif(index !== -1) {\n\t\t\treturn parent.findNextSiblingDomNode(index);\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nFind the first DOM node generated by a widget or its children\n*/\nWidget.prototype.findFirstDomNode = function() {\n\t// Return the first dom node of this widget, if we've got one\n\tif(this.domNodes.length > 0) {\n\t\treturn this.domNodes[0];\n\t}\n\t// Otherwise, recursively call our children\n\tfor(var t=0; t<this.children.length; t++) {\n\t\tvar domNode = this.children[t].findFirstDomNode();\n\t\tif(domNode) {\n\t\t\treturn domNode;\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nRemove any DOM nodes created by this widget or its children\n*/\nWidget.prototype.removeChildDomNodes = function() {\n\t// If this widget has directly created DOM nodes, delete them and exit. This assumes that any child widgets are contained within the created DOM nodes, which would normally be the case\n\tif(this.domNodes.length > 0) {\n\t\t$tw.utils.each(this.domNodes,function(domNode) {\n\t\t\tdomNode.parentNode.removeChild(domNode);\n\t\t});\n\t\tthis.domNodes = [];\n\t} else {\n\t\t// Otherwise, ask the child widgets to delete their DOM nodes\n\t\t$tw.utils.each(this.children,function(childWidget) {\n\t\t\tchildWidget.removeChildDomNodes();\n\t\t});\n\t}\n};\n\n/*\nInvoke the action widgets that are descendents of the current widget.\n*/\nWidget.prototype.invokeActions = function(triggeringWidget,event) {\n\tvar handled = false;\n\t// For each child widget\n\tfor(var t=0; t<this.children.length; t++) {\n\t\tvar child = this.children[t];\n\t\t// Invoke the child if it is an action widget\n\t\tif(child.invokeAction) {\n\t\t\tchild.refreshSelf();\n\t\t\tif(child.invokeAction(triggeringWidget,event)) {\n\t\t\t\thandled = true;\n\t\t\t}\n\t\t}\n\t\t// Propagate through through the child if it permits it\n\t\tif(child.allowActionPropagation() && child.invokeActions(triggeringWidget,event)) {\n\t\t\thandled = true;\n\t\t}\n\t}\n\treturn handled;\n};\n\n/*\nInvoke the action widgets defined in a string\n*/\nWidget.prototype.invokeActionString = function(actions,triggeringWidget,event,variables) {\n\tactions = actions || \"\";\n\tvar parser = this.wiki.parseText(\"text/vnd.tiddlywiki\",actions,{\n\t\t\tparentWidget: this,\n\t\t\tdocument: this.document\n\t\t}),\n\t\twidgetNode = this.wiki.makeWidget(parser,{\n\t\t\tparentWidget: this,\n\t\t\tdocument: this.document,\n\t\t\tvariables: variables\n\t\t});\n\tvar container = this.document.createElement(\"div\");\n\twidgetNode.render(container,null);\n\treturn widgetNode.invokeActions(this,event);\n};\n\nWidget.prototype.allowActionPropagation = function() {\n\treturn true;\n};\n\nexports.widget = Widget;\n\n})();\n",
"title": "$:/core/modules/widgets/widget.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/widgets/wikify.js": {
"text": "/*\\\ntitle: $:/core/modules/widgets/wikify.js\ntype: application/javascript\nmodule-type: widget\n\nWidget to wikify text into a variable\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar Widget = require(\"$:/core/modules/widgets/widget.js\").widget;\n\nvar WikifyWidget = function(parseTreeNode,options) {\n\tthis.initialise(parseTreeNode,options);\n};\n\n/*\nInherit from the base widget class\n*/\nWikifyWidget.prototype = new Widget();\n\n/*\nRender this widget into the DOM\n*/\nWikifyWidget.prototype.render = function(parent,nextSibling) {\n\tthis.parentDomNode = parent;\n\tthis.computeAttributes();\n\tthis.execute();\n\tthis.renderChildren(parent,nextSibling);\n};\n\n/*\nCompute the internal state of the widget\n*/\nWikifyWidget.prototype.execute = function() {\n\t// Get our parameters\n\tthis.wikifyName = this.getAttribute(\"name\");\n\tthis.wikifyText = this.getAttribute(\"text\");\n\tthis.wikifyType = this.getAttribute(\"type\");\n\tthis.wikifyMode = this.getAttribute(\"mode\",\"block\");\n\tthis.wikifyOutput = this.getAttribute(\"output\",\"text\");\n\t// Create the parse tree\n\tthis.wikifyParser = this.wiki.parseText(this.wikifyType,this.wikifyText,{\n\t\t\tparseAsInline: this.wikifyMode === \"inline\"\n\t\t});\n\t// Create the widget tree \n\tthis.wikifyWidgetNode = this.wiki.makeWidget(this.wikifyParser,{\n\t\t\tdocument: $tw.fakeDocument,\n\t\t\tparentWidget: this\n\t\t});\n\t// Render the widget tree to the container\n\tthis.wikifyContainer = $tw.fakeDocument.createElement(\"div\");\n\tthis.wikifyWidgetNode.render(this.wikifyContainer,null);\n\tthis.wikifyResult = this.getResult();\n\t// Set context variable\n\tthis.setVariable(this.wikifyName,this.wikifyResult);\n\t// Construct the child widgets\n\tthis.makeChildWidgets();\n};\n\n/*\nReturn the result string\n*/\nWikifyWidget.prototype.getResult = function() {\n\tvar result;\n\tswitch(this.wikifyOutput) {\n\t\tcase \"text\":\n\t\t\tresult = this.wikifyContainer.textContent;\n\t\t\tbreak;\n\t\tcase \"formattedtext\":\n\t\t\tresult = this.wikifyContainer.formattedTextContent;\n\t\t\tbreak;\n\t\tcase \"html\":\n\t\t\tresult = this.wikifyContainer.innerHTML;\n\t\t\tbreak;\n\t\tcase \"parsetree\":\n\t\t\tresult = JSON.stringify(this.wikifyParser.tree,0,$tw.config.preferences.jsonSpaces);\n\t\t\tbreak;\n\t\tcase \"widgettree\":\n\t\t\tresult = JSON.stringify(this.getWidgetTree(),0,$tw.config.preferences.jsonSpaces);\n\t\t\tbreak;\n\t}\n\treturn result;\n};\n\n/*\nReturn a string of the widget tree\n*/\nWikifyWidget.prototype.getWidgetTree = function() {\n\tvar copyNode = function(widgetNode,resultNode) {\n\t\t\tvar type = widgetNode.parseTreeNode.type;\n\t\t\tresultNode.type = type;\n\t\t\tswitch(type) {\n\t\t\t\tcase \"element\":\n\t\t\t\t\tresultNode.tag = widgetNode.parseTreeNode.tag;\n\t\t\t\t\tbreak;\n\t\t\t\tcase \"text\":\n\t\t\t\t\tresultNode.text = widgetNode.parseTreeNode.text;\n\t\t\t\t\tbreak;\t\n\t\t\t}\n\t\t\tif(Object.keys(widgetNode.attributes || {}).length > 0) {\n\t\t\t\tresultNode.attributes = {};\n\t\t\t\t$tw.utils.each(widgetNode.attributes,function(attr,attrName) {\n\t\t\t\t\tresultNode.attributes[attrName] = widgetNode.getAttribute(attrName);\n\t\t\t\t});\n\t\t\t}\n\t\t\tif(Object.keys(widgetNode.children || {}).length > 0) {\n\t\t\t\tresultNode.children = [];\n\t\t\t\t$tw.utils.each(widgetNode.children,function(widgetChildNode) {\n\t\t\t\t\tvar node = {};\n\t\t\t\t\tresultNode.children.push(node);\n\t\t\t\t\tcopyNode(widgetChildNode,node);\n\t\t\t\t});\n\t\t\t}\n\t\t},\n\t\tresults = {};\n\tcopyNode(this.wikifyWidgetNode,results);\n\treturn results;\n};\n\n/*\nSelectively refreshes the widget if needed. Returns true if the widget or any of its children needed re-rendering\n*/\nWikifyWidget.prototype.refresh = function(changedTiddlers) {\n\tvar changedAttributes = this.computeAttributes();\n\t// Refresh ourselves entirely if any of our attributes have changed\n\tif(changedAttributes.name || changedAttributes.text || changedAttributes.type || changedAttributes.mode || changedAttributes.output) {\n\t\tthis.refreshSelf();\n\t\treturn true;\n\t} else {\n\t\t// Refresh the widget tree\n\t\tif(this.wikifyWidgetNode.refresh(changedTiddlers)) {\n\t\t\t// Check if there was any change\n\t\t\tvar result = this.getResult();\n\t\t\tif(result !== this.wikifyResult) {\n\t\t\t\t// If so, save the change\n\t\t\t\tthis.wikifyResult = result;\n\t\t\t\tthis.setVariable(this.wikifyName,this.wikifyResult);\n\t\t\t\t// Refresh each of our child widgets\n\t\t\t\t$tw.utils.each(this.children,function(childWidget) {\n\t\t\t\t\tchildWidget.refreshSelf();\n\t\t\t\t});\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\t\t// Just refresh the children\n\t\treturn this.refreshChildren(changedTiddlers);\n\t}\n};\n\nexports.wikify = WikifyWidget;\n\n})();\n",
"title": "$:/core/modules/widgets/wikify.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/core/modules/wiki-bulkops.js": {
"text": "/*\\\ntitle: $:/core/modules/wiki-bulkops.js\ntype: application/javascript\nmodule-type: wikimethod\n\nBulk tiddler operations such as rename.\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\n/*\nRename a tiddler, and relink any tags or lists that reference it.\n*/\nfunction renameTiddler(fromTitle,toTitle,options) {\n\tfromTitle = (fromTitle || \"\").trim();\n\ttoTitle = (toTitle || \"\").trim();\n\toptions = options || {};\n\tif(fromTitle && toTitle && fromTitle !== toTitle) {\n\t\t// Rename the tiddler itself\n\t\tvar oldTiddler = this.getTiddler(fromTitle),\n\t\t\tnewTiddler = new $tw.Tiddler(oldTiddler,{title: toTitle},this.getModificationFields());\n\t\tnewTiddler = $tw.hooks.invokeHook(\"th-renaming-tiddler\",newTiddler,oldTiddler);\n\t\tthis.addTiddler(newTiddler);\n\t\tthis.deleteTiddler(fromTitle);\n\t\t// Rename any tags or lists that reference it\n\t\tthis.relinkTiddler(fromTitle,toTitle,options)\n\t}\n}\n\n/*\nRelink any tags or lists that reference a given tiddler\n*/\nfunction relinkTiddler(fromTitle,toTitle,options) {\n\tvar self = this;\n\tfromTitle = (fromTitle || \"\").trim();\n\ttoTitle = (toTitle || \"\").trim();\n\toptions = options || {};\n\tif(fromTitle && toTitle && fromTitle !== toTitle) {\n\t\tthis.each(function(tiddler,title) {\n\t\t\tvar type = tiddler.fields.type || \"\";\n\t\t\t// Don't touch plugins or JavaScript modules\n\t\t\tif(!tiddler.fields[\"plugin-type\"] && type !== \"application/javascript\") {\n\t\t\t\tvar tags = (tiddler.fields.tags || []).slice(0),\n\t\t\t\t\tlist = (tiddler.fields.list || []).slice(0),\n\t\t\t\t\tisModified = false;\n\t\t\t\tif(!options.dontRenameInTags) {\n\t\t\t\t\t// Rename tags\n\t\t\t\t\t$tw.utils.each(tags,function (title,index) {\n\t\t\t\t\t\tif(title === fromTitle) {\nconsole.log(\"Renaming tag '\" + tags[index] + \"' to '\" + toTitle + \"' of tiddler '\" + tiddler.fields.title + \"'\");\n\t\t\t\t\t\t\ttags[index] = toTitle;\n\t\t\t\t\t\t\tisModified = true;\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t\tif(!options.dontRenameInLists) {\n\t\t\t\t\t// Rename lists\n\t\t\t\t\t$tw.utils.each(list,function (title,index) {\n\t\t\t\t\t\tif(title === fromTitle) {\nconsole.log(\"Renaming list item '\" + list[index] + \"' to '\" + toTitle + \"' of tiddler '\" + tiddler.fields.title + \"'\");\n\t\t\t\t\t\t\tlist[index] = toTitle;\n\t\t\t\t\t\t\tisModified = true;\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t\tif(isModified) {\n\t\t\t\t\tvar newTiddler = new $tw.Tiddler(tiddler,{tags: tags, list: list},self.getModificationFields())\n\t\t\t\t\tnewTiddler = $tw.hooks.invokeHook(\"th-relinking-tiddler\",newTiddler,tiddler);\n\t\t\t\t\tself.addTiddler(newTiddler);\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t}\n};\n\nexports.renameTiddler = renameTiddler;\nexports.relinkTiddler = relinkTiddler;\n\n})();\n",
"title": "$:/core/modules/wiki-bulkops.js",
"type": "application/javascript",
"module-type": "wikimethod"
},
"$:/core/modules/wiki.js": {
"text": "/*\\\ntitle: $:/core/modules/wiki.js\ntype: application/javascript\nmodule-type: wikimethod\n\nExtension methods for the $tw.Wiki object\n\nAdds the following properties to the wiki object:\n\n* `eventListeners` is a hashmap by type of arrays of listener functions\n* `changedTiddlers` is a hashmap describing changes to named tiddlers since wiki change events were last dispatched. Each entry is a hashmap containing two fields:\n\tmodified: true/false\n\tdeleted: true/false\n* `changeCount` is a hashmap by tiddler title containing a numerical index that starts at zero and is incremented each time a tiddler is created changed or deleted\n* `caches` is a hashmap by tiddler title containing a further hashmap of named cache objects. Caches are automatically cleared when a tiddler is modified or deleted\n* `globalCache` is a hashmap by cache name of cache objects that are cleared whenever any tiddler change occurs\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar widget = require(\"$:/core/modules/widgets/widget.js\");\n\nvar USER_NAME_TITLE = \"$:/status/UserName\",\n\tTIMESTAMP_DISABLE_TITLE = \"$:/config/TimestampDisable\";\n\n/*\nGet the value of a text reference. Text references can have any of these forms:\n\t<tiddlertitle>\n\t<tiddlertitle>!!<fieldname>\n\t!!<fieldname> - specifies a field of the current tiddlers\n\t<tiddlertitle>##<index>\n*/\nexports.getTextReference = function(textRef,defaultText,currTiddlerTitle) {\n\tvar tr = $tw.utils.parseTextReference(textRef),\n\t\ttitle = tr.title || currTiddlerTitle;\n\tif(tr.field) {\n\t\tvar tiddler = this.getTiddler(title);\n\t\tif(tr.field === \"title\") { // Special case so we can return the title of a non-existent tiddler\n\t\t\treturn title;\n\t\t} else if(tiddler && $tw.utils.hop(tiddler.fields,tr.field)) {\n\t\t\treturn tiddler.getFieldString(tr.field);\n\t\t} else {\n\t\t\treturn defaultText;\n\t\t}\n\t} else if(tr.index) {\n\t\treturn this.extractTiddlerDataItem(title,tr.index,defaultText);\n\t} else {\n\t\treturn this.getTiddlerText(title,defaultText);\n\t}\n};\n\nexports.setTextReference = function(textRef,value,currTiddlerTitle) {\n\tvar tr = $tw.utils.parseTextReference(textRef),\n\t\ttitle = tr.title || currTiddlerTitle;\n\tthis.setText(title,tr.field,tr.index,value);\n};\n\nexports.setText = function(title,field,index,value,options) {\n\toptions = options || {};\n\tvar creationFields = options.suppressTimestamp ? {} : this.getCreationFields(),\n\t\tmodificationFields = options.suppressTimestamp ? {} : this.getModificationFields();\n\t// Check if it is a reference to a tiddler field\n\tif(index) {\n\t\tvar data = this.getTiddlerData(title,Object.create(null));\n\t\tif(value !== undefined) {\n\t\t\tdata[index] = value;\n\t\t} else {\n\t\t\tdelete data[index];\n\t\t}\n\t\tthis.setTiddlerData(title,data,modificationFields);\n\t} else {\n\t\tvar tiddler = this.getTiddler(title),\n\t\t\tfields = {title: title};\n\t\tfields[field || \"text\"] = value;\n\t\tthis.addTiddler(new $tw.Tiddler(creationFields,tiddler,fields,modificationFields));\n\t}\n};\n\nexports.deleteTextReference = function(textRef,currTiddlerTitle) {\n\tvar tr = $tw.utils.parseTextReference(textRef),\n\t\ttitle,tiddler,fields;\n\t// Check if it is a reference to a tiddler\n\tif(tr.title && !tr.field) {\n\t\tthis.deleteTiddler(tr.title);\n\t// Else check for a field reference\n\t} else if(tr.field) {\n\t\ttitle = tr.title || currTiddlerTitle;\n\t\ttiddler = this.getTiddler(title);\n\t\tif(tiddler && $tw.utils.hop(tiddler.fields,tr.field)) {\n\t\t\tfields = Object.create(null);\n\t\t\tfields[tr.field] = undefined;\n\t\t\tthis.addTiddler(new $tw.Tiddler(tiddler,fields,this.getModificationFields()));\n\t\t}\n\t}\n};\n\nexports.addEventListener = function(type,listener) {\n\tthis.eventListeners = this.eventListeners || {};\n\tthis.eventListeners[type] = this.eventListeners[type] || [];\n\tthis.eventListeners[type].push(listener);\t\n};\n\nexports.removeEventListener = function(type,listener) {\n\tvar listeners = this.eventListeners[type];\n\tif(listeners) {\n\t\tvar p = listeners.indexOf(listener);\n\t\tif(p !== -1) {\n\t\t\tlisteners.splice(p,1);\n\t\t}\n\t}\n};\n\nexports.dispatchEvent = function(type /*, args */) {\n\tvar args = Array.prototype.slice.call(arguments,1),\n\t\tlisteners = this.eventListeners[type];\n\tif(listeners) {\n\t\tfor(var p=0; p<listeners.length; p++) {\n\t\t\tvar listener = listeners[p];\n\t\t\tlistener.apply(listener,args);\n\t\t}\n\t}\n};\n\n/*\nCauses a tiddler to be marked as changed, incrementing the change count, and triggers event handlers.\nThis method should be called after the changes it describes have been made to the wiki.tiddlers[] array.\n\ttitle: Title of tiddler\n\tisDeleted: defaults to false (meaning the tiddler has been created or modified),\n\t\ttrue if the tiddler has been deleted\n*/\nexports.enqueueTiddlerEvent = function(title,isDeleted) {\n\t// Record the touch in the list of changed tiddlers\n\tthis.changedTiddlers = this.changedTiddlers || Object.create(null);\n\tthis.changedTiddlers[title] = this.changedTiddlers[title] || Object.create(null);\n\tthis.changedTiddlers[title][isDeleted ? \"deleted\" : \"modified\"] = true;\n\t// Increment the change count\n\tthis.changeCount = this.changeCount || Object.create(null);\n\tif($tw.utils.hop(this.changeCount,title)) {\n\t\tthis.changeCount[title]++;\n\t} else {\n\t\tthis.changeCount[title] = 1;\n\t}\n\t// Trigger events\n\tthis.eventListeners = this.eventListeners || {};\n\tif(!this.eventsTriggered) {\n\t\tvar self = this;\n\t\t$tw.utils.nextTick(function() {\n\t\t\tvar changes = self.changedTiddlers;\n\t\t\tself.changedTiddlers = Object.create(null);\n\t\t\tself.eventsTriggered = false;\n\t\t\tif($tw.utils.count(changes) > 0) {\n\t\t\t\tself.dispatchEvent(\"change\",changes);\n\t\t\t}\n\t\t});\n\t\tthis.eventsTriggered = true;\n\t}\n};\n\nexports.getSizeOfTiddlerEventQueue = function() {\n\treturn $tw.utils.count(this.changedTiddlers);\n};\n\nexports.clearTiddlerEventQueue = function() {\n\tthis.changedTiddlers = Object.create(null);\n\tthis.changeCount = Object.create(null);\n};\n\nexports.getChangeCount = function(title) {\n\tthis.changeCount = this.changeCount || Object.create(null);\n\tif($tw.utils.hop(this.changeCount,title)) {\n\t\treturn this.changeCount[title];\n\t} else {\n\t\treturn 0;\n\t}\n};\n\n/*\nGenerate an unused title from the specified base\n*/\nexports.generateNewTitle = function(baseTitle,options) {\n\toptions = options || {};\n\tvar c = 0,\n\t\ttitle = baseTitle;\n\twhile(this.tiddlerExists(title) || this.isShadowTiddler(title) || this.findDraft(title)) {\n\t\ttitle = baseTitle + \n\t\t\t(options.prefix || \" \") + \n\t\t\t(++c);\n\t}\n\treturn title;\n};\n\nexports.isSystemTiddler = function(title) {\n\treturn title && title.indexOf(\"$:/\") === 0;\n};\n\nexports.isTemporaryTiddler = function(title) {\n\treturn title && title.indexOf(\"$:/temp/\") === 0;\n};\n\nexports.isImageTiddler = function(title) {\n\tvar tiddler = this.getTiddler(title);\n\tif(tiddler) {\t\t\n\t\tvar contentTypeInfo = $tw.config.contentTypeInfo[tiddler.fields.type || \"text/vnd.tiddlywiki\"];\n\t\treturn !!contentTypeInfo && contentTypeInfo.flags.indexOf(\"image\") !== -1;\n\t} else {\n\t\treturn null;\n\t}\n};\n\n/*\nLike addTiddler() except it will silently reject any plugin tiddlers that are older than the currently loaded version. Returns true if the tiddler was imported\n*/\nexports.importTiddler = function(tiddler) {\n\tvar existingTiddler = this.getTiddler(tiddler.fields.title);\n\t// Check if we're dealing with a plugin\n\tif(tiddler && tiddler.hasField(\"plugin-type\") && tiddler.hasField(\"version\") && existingTiddler && existingTiddler.hasField(\"plugin-type\") && existingTiddler.hasField(\"version\")) {\n\t\t// Reject the incoming plugin if it is older\n\t\tif(!$tw.utils.checkVersions(tiddler.fields.version,existingTiddler.fields.version)) {\n\t\t\treturn false;\n\t\t}\n\t}\n\t// Fall through to adding the tiddler\n\tthis.addTiddler(tiddler);\n\treturn true;\n};\n\n/*\nReturn a hashmap of the fields that should be set when a tiddler is created\n*/\nexports.getCreationFields = function() {\n\tif(this.getTiddlerText(TIMESTAMP_DISABLE_TITLE,\"\").toLowerCase() !== \"yes\") {\n\t\tvar fields = {\n\t\t\t\tcreated: new Date()\n\t\t\t},\n\t\t\tcreator = this.getTiddlerText(USER_NAME_TITLE);\n\t\tif(creator) {\n\t\t\tfields.creator = creator;\n\t\t}\n\t\treturn fields;\n\t} else {\n\t\treturn {};\n\t}\n};\n\n/*\nReturn a hashmap of the fields that should be set when a tiddler is modified\n*/\nexports.getModificationFields = function() {\n\tif(this.getTiddlerText(TIMESTAMP_DISABLE_TITLE,\"\").toLowerCase() !== \"yes\") {\n\t\tvar fields = Object.create(null),\n\t\t\tmodifier = this.getTiddlerText(USER_NAME_TITLE);\n\t\tfields.modified = new Date();\n\t\tif(modifier) {\n\t\t\tfields.modifier = modifier;\n\t\t}\n\t\treturn fields;\n\t} else {\n\t\treturn {};\n\t}\n};\n\n/*\nReturn a sorted array of tiddler titles. Options include:\nsortField: field to sort by\nexcludeTag: tag to exclude\nincludeSystem: whether to include system tiddlers (defaults to false)\n*/\nexports.getTiddlers = function(options) {\n\toptions = options || Object.create(null);\n\tvar self = this,\n\t\tsortField = options.sortField || \"title\",\n\t\ttiddlers = [], t, titles = [];\n\tthis.each(function(tiddler,title) {\n\t\tif(options.includeSystem || !self.isSystemTiddler(title)) {\n\t\t\tif(!options.excludeTag || !tiddler.hasTag(options.excludeTag)) {\n\t\t\t\ttiddlers.push(tiddler);\n\t\t\t}\n\t\t}\n\t});\n\ttiddlers.sort(function(a,b) {\n\t\tvar aa = a.fields[sortField].toLowerCase() || \"\",\n\t\t\tbb = b.fields[sortField].toLowerCase() || \"\";\n\t\tif(aa < bb) {\n\t\t\treturn -1;\n\t\t} else {\n\t\t\tif(aa > bb) {\n\t\t\t\treturn 1;\n\t\t\t} else {\n\t\t\t\treturn 0;\n\t\t\t}\n\t\t}\n\t});\n\tfor(t=0; t<tiddlers.length; t++) {\n\t\ttitles.push(tiddlers[t].fields.title);\n\t}\n\treturn titles;\n};\n\nexports.countTiddlers = function(excludeTag) {\n\tvar tiddlers = this.getTiddlers({excludeTag: excludeTag});\n\treturn $tw.utils.count(tiddlers);\n};\n\n/*\nReturns a function iterator(callback) that iterates through the specified titles, and invokes the callback with callback(tiddler,title)\n*/\nexports.makeTiddlerIterator = function(titles) {\n\tvar self = this;\n\tif(!$tw.utils.isArray(titles)) {\n\t\ttitles = Object.keys(titles);\n\t} else {\n\t\ttitles = titles.slice(0);\n\t}\n\treturn function(callback) {\n\t\ttitles.forEach(function(title) {\n\t\t\tcallback(self.getTiddler(title),title);\n\t\t});\n\t};\n};\n\n/*\nSort an array of tiddler titles by a specified field\n\ttitles: array of titles (sorted in place)\n\tsortField: name of field to sort by\n\tisDescending: true if the sort should be descending\n\tisCaseSensitive: true if the sort should consider upper and lower case letters to be different\n*/\nexports.sortTiddlers = function(titles,sortField,isDescending,isCaseSensitive,isNumeric) {\n\tvar self = this;\n\ttitles.sort(function(a,b) {\n\t\tvar x,y,\n\t\t\tcompareNumbers = function(x,y) {\n\t\t\t\tvar result = \n\t\t\t\t\tisNaN(x) && !isNaN(y) ? (isDescending ? -1 : 1) :\n\t\t\t\t\t!isNaN(x) && isNaN(y) ? (isDescending ? 1 : -1) :\n\t\t\t\t\t\t\t\t\t\t\t(isDescending ? y - x : x - y);\n\t\t\t\treturn result;\n\t\t\t};\n\t\tif(sortField !== \"title\") {\n\t\t\tvar tiddlerA = self.getTiddler(a),\n\t\t\t\ttiddlerB = self.getTiddler(b);\n\t\t\tif(tiddlerA) {\n\t\t\t\ta = tiddlerA.fields[sortField] || \"\";\n\t\t\t} else {\n\t\t\t\ta = \"\";\n\t\t\t}\n\t\t\tif(tiddlerB) {\n\t\t\t\tb = tiddlerB.fields[sortField] || \"\";\n\t\t\t} else {\n\t\t\t\tb = \"\";\n\t\t\t}\n\t\t}\n\t\tx = Number(a);\n\t\ty = Number(b);\n\t\tif(isNumeric && (!isNaN(x) || !isNaN(y))) {\n\t\t\treturn compareNumbers(x,y);\n\t\t} else if($tw.utils.isDate(a) && $tw.utils.isDate(b)) {\n\t\t\treturn isDescending ? b - a : a - b;\n\t\t} else {\n\t\t\ta = String(a);\n\t\t\tb = String(b);\n\t\t\tif(!isCaseSensitive) {\n\t\t\t\ta = a.toLowerCase();\n\t\t\t\tb = b.toLowerCase();\n\t\t\t}\n\t\t\treturn isDescending ? b.localeCompare(a) : a.localeCompare(b);\n\t\t}\n\t});\n};\n\n/*\nFor every tiddler invoke a callback(title,tiddler) with `this` set to the wiki object. Options include:\nsortField: field to sort by\nexcludeTag: tag to exclude\nincludeSystem: whether to include system tiddlers (defaults to false)\n*/\nexports.forEachTiddler = function(/* [options,]callback */) {\n\tvar arg = 0,\n\t\toptions = arguments.length >= 2 ? arguments[arg++] : {},\n\t\tcallback = arguments[arg++],\n\t\ttitles = this.getTiddlers(options),\n\t\tt, tiddler;\n\tfor(t=0; t<titles.length; t++) {\n\t\ttiddler = this.getTiddler(titles[t]);\n\t\tif(tiddler) {\n\t\t\tcallback.call(this,tiddler.fields.title,tiddler);\n\t\t}\n\t}\n};\n\n/*\nReturn an array of tiddler titles that are directly linked from the specified tiddler\n*/\nexports.getTiddlerLinks = function(title) {\n\tvar self = this;\n\t// We'll cache the links so they only get computed if the tiddler changes\n\treturn this.getCacheForTiddler(title,\"links\",function() {\n\t\t// Parse the tiddler\n\t\tvar parser = self.parseTiddler(title);\n\t\t// Count up the links\n\t\tvar links = [],\n\t\t\tcheckParseTree = function(parseTree) {\n\t\t\t\tfor(var t=0; t<parseTree.length; t++) {\n\t\t\t\t\tvar parseTreeNode = parseTree[t];\n\t\t\t\t\tif(parseTreeNode.type === \"link\" && parseTreeNode.attributes.to && parseTreeNode.attributes.to.type === \"string\") {\n\t\t\t\t\t\tvar value = parseTreeNode.attributes.to.value;\n\t\t\t\t\t\tif(links.indexOf(value) === -1) {\n\t\t\t\t\t\t\tlinks.push(value);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\tif(parseTreeNode.children) {\n\t\t\t\t\t\tcheckParseTree(parseTreeNode.children);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t};\n\t\tif(parser) {\n\t\t\tcheckParseTree(parser.tree);\n\t\t}\n\t\treturn links;\n\t});\n};\n\n/*\nReturn an array of tiddler titles that link to the specified tiddler\n*/\nexports.getTiddlerBacklinks = function(targetTitle) {\n\tvar self = this,\n\t\tbacklinks = [];\n\tthis.forEachTiddler(function(title,tiddler) {\n\t\tvar links = self.getTiddlerLinks(title);\n\t\tif(links.indexOf(targetTitle) !== -1) {\n\t\t\tbacklinks.push(title);\n\t\t}\n\t});\n\treturn backlinks;\n};\n\n/*\nReturn a hashmap of tiddler titles that are referenced but not defined. Each value is the number of times the missing tiddler is referenced\n*/\nexports.getMissingTitles = function() {\n\tvar self = this,\n\t\tmissing = [];\n// We should cache the missing tiddler list, even if we recreate it every time any tiddler is modified\n\tthis.forEachTiddler(function(title,tiddler) {\n\t\tvar links = self.getTiddlerLinks(title);\n\t\t$tw.utils.each(links,function(link) {\n\t\t\tif((!self.tiddlerExists(link) && !self.isShadowTiddler(link)) && missing.indexOf(link) === -1) {\n\t\t\t\tmissing.push(link);\n\t\t\t}\n\t\t});\n\t});\n\treturn missing;\n};\n\nexports.getOrphanTitles = function() {\n\tvar self = this,\n\t\torphans = this.getTiddlers();\n\tthis.forEachTiddler(function(title,tiddler) {\n\t\tvar links = self.getTiddlerLinks(title);\n\t\t$tw.utils.each(links,function(link) {\n\t\t\tvar p = orphans.indexOf(link);\n\t\t\tif(p !== -1) {\n\t\t\t\torphans.splice(p,1);\n\t\t\t}\n\t\t});\n\t});\n\treturn orphans; // Todo\n};\n\n/*\nRetrieves a list of the tiddler titles that are tagged with a given tag\n*/\nexports.getTiddlersWithTag = function(tag) {\n\tvar self = this;\n\treturn this.getGlobalCache(\"taglist-\" + tag,function() {\n\t\tvar tagmap = self.getTagMap();\n\t\treturn self.sortByList(tagmap[tag],tag);\n\t});\n};\n\n/*\nGet a hashmap by tag of arrays of tiddler titles\n*/\nexports.getTagMap = function() {\n\tvar self = this;\n\treturn this.getGlobalCache(\"tagmap\",function() {\n\t\tvar tags = Object.create(null),\n\t\t\tstoreTags = function(tagArray,title) {\n\t\t\t\tif(tagArray) {\n\t\t\t\t\tfor(var index=0; index<tagArray.length; index++) {\n\t\t\t\t\t\tvar tag = tagArray[index];\n\t\t\t\t\t\tif($tw.utils.hop(tags,tag)) {\n\t\t\t\t\t\t\ttags[tag].push(title);\n\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\ttags[tag] = [title];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t},\n\t\t\ttitle, tiddler;\n\t\t// Collect up all the tags\n\t\tself.eachShadow(function(tiddler,title) {\n\t\t\tif(!self.tiddlerExists(title)) {\n\t\t\t\ttiddler = self.getTiddler(title);\n\t\t\t\tstoreTags(tiddler.fields.tags,title);\n\t\t\t}\n\t\t});\n\t\tself.each(function(tiddler,title) {\n\t\t\tstoreTags(tiddler.fields.tags,title);\n\t\t});\n\t\treturn tags;\n\t});\n};\n\n/*\nLookup a given tiddler and return a list of all the tiddlers that include it in the specified list field\n*/\nexports.findListingsOfTiddler = function(targetTitle,fieldName) {\n\tfieldName = fieldName || \"list\";\n\tvar titles = [];\n\tthis.each(function(tiddler,title) {\n\t\tvar list = $tw.utils.parseStringArray(tiddler.fields[fieldName]);\n\t\tif(list && list.indexOf(targetTitle) !== -1) {\n\t\t\ttitles.push(title);\n\t\t}\n\t});\n\treturn titles;\n};\n\n/*\nSorts an array of tiddler titles according to an ordered list\n*/\nexports.sortByList = function(array,listTitle) {\n\tvar list = this.getTiddlerList(listTitle);\n\tif(!array || array.length === 0) {\n\t\treturn [];\n\t} else {\n\t\tvar titles = [], t, title;\n\t\t// First place any entries that are present in the list\n\t\tfor(t=0; t<list.length; t++) {\n\t\t\ttitle = list[t];\n\t\t\tif(array.indexOf(title) !== -1) {\n\t\t\t\ttitles.push(title);\n\t\t\t}\n\t\t}\n\t\t// Then place any remaining entries\n\t\tfor(t=0; t<array.length; t++) {\n\t\t\ttitle = array[t];\n\t\t\tif(list.indexOf(title) === -1) {\n\t\t\t\ttitles.push(title);\n\t\t\t}\n\t\t}\n\t\t// Finally obey the list-before and list-after fields of each tiddler in turn\n\t\tvar sortedTitles = titles.slice(0);\n\t\tfor(t=0; t<sortedTitles.length; t++) {\n\t\t\ttitle = sortedTitles[t];\n\t\t\tvar currPos = titles.indexOf(title),\n\t\t\t\tnewPos = -1,\n\t\t\t\ttiddler = this.getTiddler(title);\n\t\t\tif(tiddler) {\n\t\t\t\tvar beforeTitle = tiddler.fields[\"list-before\"],\n\t\t\t\t\tafterTitle = tiddler.fields[\"list-after\"];\n\t\t\t\tif(beforeTitle === \"\") {\n\t\t\t\t\tnewPos = 0;\n\t\t\t\t} else if(beforeTitle) {\n\t\t\t\t\tnewPos = titles.indexOf(beforeTitle);\n\t\t\t\t} else if(afterTitle) {\n\t\t\t\t\tnewPos = titles.indexOf(afterTitle);\n\t\t\t\t\tif(newPos >= 0) {\n\t\t\t\t\t\t++newPos;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tif(newPos === -1) {\n\t\t\t\t\tnewPos = currPos;\n\t\t\t\t}\n\t\t\t\tif(newPos !== currPos) {\n\t\t\t\t\ttitles.splice(currPos,1);\n\t\t\t\t\tif(newPos >= currPos) {\n\t\t\t\t\t\tnewPos--;\n\t\t\t\t\t}\n\t\t\t\t\ttitles.splice(newPos,0,title);\n\t\t\t\t}\n\t\t\t}\n\n\t\t}\n\t\treturn titles;\n\t}\n};\n\nexports.getSubTiddler = function(title,subTiddlerTitle) {\n\tvar bundleInfo = this.getPluginInfo(title) || this.getTiddlerDataCached(title);\n\tif(bundleInfo && bundleInfo.tiddlers) {\n\t\tvar subTiddler = bundleInfo.tiddlers[subTiddlerTitle];\n\t\tif(subTiddler) {\n\t\t\treturn new $tw.Tiddler(subTiddler);\n\t\t}\n\t}\n\treturn null;\n};\n\n/*\nRetrieve a tiddler as a JSON string of the fields\n*/\nexports.getTiddlerAsJson = function(title) {\n\tvar tiddler = this.getTiddler(title);\n\tif(tiddler) {\n\t\tvar fields = Object.create(null);\n\t\t$tw.utils.each(tiddler.fields,function(value,name) {\n\t\t\tfields[name] = tiddler.getFieldString(name);\n\t\t});\n\t\treturn JSON.stringify(fields);\n\t} else {\n\t\treturn JSON.stringify({title: title});\n\t}\n};\n\n/*\nGet the content of a tiddler as a JavaScript object. How this is done depends on the type of the tiddler:\n\napplication/json: the tiddler JSON is parsed into an object\napplication/x-tiddler-dictionary: the tiddler is parsed as sequence of name:value pairs\n\nOther types currently just return null.\n\ntitleOrTiddler: string tiddler title or a tiddler object\ndefaultData: default data to be returned if the tiddler is missing or doesn't contain data\n\nNote that the same value is returned for repeated calls for the same tiddler data. The value is frozen to prevent modification; otherwise modifications would be visible to all callers\n*/\nexports.getTiddlerDataCached = function(titleOrTiddler,defaultData) {\n\tvar self = this,\n\t\ttiddler = titleOrTiddler;\n\tif(!(tiddler instanceof $tw.Tiddler)) {\n\t\ttiddler = this.getTiddler(tiddler);\t\n\t}\n\tif(tiddler) {\n\t\treturn this.getCacheForTiddler(tiddler.fields.title,\"data\",function() {\n\t\t\t// Return the frozen value\n\t\t\tvar value = self.getTiddlerData(tiddler.fields.title,undefined);\n\t\t\t$tw.utils.deepFreeze(value);\n\t\t\treturn value;\n\t\t}) || defaultData;\n\t} else {\n\t\treturn defaultData;\n\t}\n};\n\n/*\nAlternative, uncached version of getTiddlerDataCached(). The return value can be mutated freely and reused\n*/\nexports.getTiddlerData = function(titleOrTiddler,defaultData) {\n\tvar tiddler = titleOrTiddler,\n\t\tdata;\n\tif(!(tiddler instanceof $tw.Tiddler)) {\n\t\ttiddler = this.getTiddler(tiddler);\t\n\t}\n\tif(tiddler && tiddler.fields.text) {\n\t\tswitch(tiddler.fields.type) {\n\t\t\tcase \"application/json\":\n\t\t\t\t// JSON tiddler\n\t\t\t\ttry {\n\t\t\t\t\tdata = JSON.parse(tiddler.fields.text);\n\t\t\t\t} catch(ex) {\n\t\t\t\t\treturn defaultData;\n\t\t\t\t}\n\t\t\t\treturn data;\n\t\t\tcase \"application/x-tiddler-dictionary\":\n\t\t\t\treturn $tw.utils.parseFields(tiddler.fields.text);\n\t\t}\n\t}\n\treturn defaultData;\n};\n\n/*\nExtract an indexed field from within a data tiddler\n*/\nexports.extractTiddlerDataItem = function(titleOrTiddler,index,defaultText) {\n\tvar data = this.getTiddlerDataCached(titleOrTiddler,Object.create(null)),\n\t\ttext;\n\tif(data && $tw.utils.hop(data,index)) {\n\t\ttext = data[index];\n\t}\n\tif(typeof text === \"string\" || typeof text === \"number\") {\n\t\treturn text.toString();\n\t} else {\n\t\treturn defaultText;\n\t}\n};\n\n/*\nSet a tiddlers content to a JavaScript object. Currently this is done by setting the tiddler's type to \"application/json\" and setting the text to the JSON text of the data.\ntitle: title of tiddler\ndata: object that can be serialised to JSON\nfields: optional hashmap of additional tiddler fields to be set\n*/\nexports.setTiddlerData = function(title,data,fields) {\n\tvar existingTiddler = this.getTiddler(title),\n\t\tnewFields = {\n\t\t\ttitle: title\n\t};\n\tif(existingTiddler && existingTiddler.fields.type === \"application/x-tiddler-dictionary\") {\n\t\tnewFields.text = $tw.utils.makeTiddlerDictionary(data);\n\t} else {\n\t\tnewFields.type = \"application/json\";\n\t\tnewFields.text = JSON.stringify(data,null,$tw.config.preferences.jsonSpaces);\n\t}\n\tthis.addTiddler(new $tw.Tiddler(this.getCreationFields(),existingTiddler,fields,newFields,this.getModificationFields()));\n};\n\n/*\nReturn the content of a tiddler as an array containing each line\n*/\nexports.getTiddlerList = function(title,field,index) {\n\tif(index) {\n\t\treturn $tw.utils.parseStringArray(this.extractTiddlerDataItem(title,index,\"\"));\n\t}\n\tfield = field || \"list\";\n\tvar tiddler = this.getTiddler(title);\n\tif(tiddler) {\n\t\treturn ($tw.utils.parseStringArray(tiddler.fields[field]) || []).slice(0);\n\t}\n\treturn [];\n};\n\n// Return a named global cache object. Global cache objects are cleared whenever a tiddler change occurs\nexports.getGlobalCache = function(cacheName,initializer) {\n\tthis.globalCache = this.globalCache || Object.create(null);\n\tif($tw.utils.hop(this.globalCache,cacheName)) {\n\t\treturn this.globalCache[cacheName];\n\t} else {\n\t\tthis.globalCache[cacheName] = initializer();\n\t\treturn this.globalCache[cacheName];\n\t}\n};\n\nexports.clearGlobalCache = function() {\n\tthis.globalCache = Object.create(null);\n};\n\n// Return the named cache object for a tiddler. If the cache doesn't exist then the initializer function is invoked to create it\nexports.getCacheForTiddler = function(title,cacheName,initializer) {\n\tthis.caches = this.caches || Object.create(null);\n\tvar caches = this.caches[title];\n\tif(caches && caches[cacheName]) {\n\t\treturn caches[cacheName];\n\t} else {\n\t\tif(!caches) {\n\t\t\tcaches = Object.create(null);\n\t\t\tthis.caches[title] = caches;\n\t\t}\n\t\tcaches[cacheName] = initializer();\n\t\treturn caches[cacheName];\n\t}\n};\n\n// Clear all caches associated with a particular tiddler, or, if the title is null, clear all the caches for all the tiddlers\nexports.clearCache = function(title) {\n\tif(title) {\n\t\tthis.caches = this.caches || Object.create(null);\n\t\tif($tw.utils.hop(this.caches,title)) {\n\t\t\tdelete this.caches[title];\n\t\t}\n\t} else {\n\t\tthis.caches = Object.create(null);\n\t}\n};\n\nexports.initParsers = function(moduleType) {\n\t// Install the parser modules\n\t$tw.Wiki.parsers = {};\n\tvar self = this;\n\t$tw.modules.forEachModuleOfType(\"parser\",function(title,module) {\n\t\tfor(var f in module) {\n\t\t\tif($tw.utils.hop(module,f)) {\n\t\t\t\t$tw.Wiki.parsers[f] = module[f]; // Store the parser class\n\t\t\t}\n\t\t}\n\t});\n};\n\n/*\nParse a block of text of a specified MIME type\n\ttype: content type of text to be parsed\n\ttext: text\n\toptions: see below\nOptions include:\n\tparseAsInline: if true, the text of the tiddler will be parsed as an inline run\n\t_canonical_uri: optional string of the canonical URI of this content\n*/\nexports.parseText = function(type,text,options) {\n\ttext = text || \"\";\n\toptions = options || {};\n\t// Select a parser\n\tvar Parser = $tw.Wiki.parsers[type];\n\tif(!Parser && $tw.utils.getFileExtensionInfo(type)) {\n\t\tParser = $tw.Wiki.parsers[$tw.utils.getFileExtensionInfo(type).type];\n\t}\n\tif(!Parser) {\n\t\tParser = $tw.Wiki.parsers[options.defaultType || \"text/vnd.tiddlywiki\"];\n\t}\n\tif(!Parser) {\n\t\treturn null;\n\t}\n\t// Return the parser instance\n\treturn new Parser(type,text,{\n\t\tparseAsInline: options.parseAsInline,\n\t\twiki: this,\n\t\t_canonical_uri: options._canonical_uri\n\t});\n};\n\n/*\nParse a tiddler according to its MIME type\n*/\nexports.parseTiddler = function(title,options) {\n\toptions = $tw.utils.extend({},options);\n\tvar cacheType = options.parseAsInline ? \"inlineParseTree\" : \"blockParseTree\",\n\t\ttiddler = this.getTiddler(title),\n\t\tself = this;\n\treturn tiddler ? this.getCacheForTiddler(title,cacheType,function() {\n\t\t\tif(tiddler.hasField(\"_canonical_uri\")) {\n\t\t\t\toptions._canonical_uri = tiddler.fields._canonical_uri;\n\t\t\t}\n\t\t\treturn self.parseText(tiddler.fields.type,tiddler.fields.text,options);\n\t\t}) : null;\n};\n\nexports.parseTextReference = function(title,field,index,options) {\n\tvar tiddler,text;\n\tif(options.subTiddler) {\n\t\ttiddler = this.getSubTiddler(title,options.subTiddler);\n\t} else {\n\t\ttiddler = this.getTiddler(title);\n\t\tif(field === \"text\" || (!field && !index)) {\n\t\t\tthis.getTiddlerText(title); // Force the tiddler to be lazily loaded\n\t\t\treturn this.parseTiddler(title,options);\n\t\t}\n\t}\n\tif(field === \"text\" || (!field && !index)) {\n\t\tif(tiddler && tiddler.fields) {\n\t\t\treturn this.parseText(tiddler.fields.type || \"text/vnd.tiddlywiki\",tiddler.fields.text,options);\t\t\t\n\t\t} else {\n\t\t\treturn null;\n\t\t}\n\t} else if(field) {\n\t\tif(field === \"title\") {\n\t\t\ttext = title;\n\t\t} else {\n\t\t\tif(!tiddler || !tiddler.hasField(field)) {\n\t\t\t\treturn null;\n\t\t\t}\n\t\t\ttext = tiddler.fields[field];\n\t\t}\n\t\treturn this.parseText(\"text/vnd.tiddlywiki\",text.toString(),options);\n\t} else if(index) {\n\t\tthis.getTiddlerText(title); // Force the tiddler to be lazily loaded\n\t\ttext = this.extractTiddlerDataItem(tiddler,index,undefined);\n\t\tif(text === undefined) {\n\t\t\treturn null;\n\t\t}\n\t\treturn this.parseText(\"text/vnd.tiddlywiki\",text,options);\n\t}\n};\n\n/*\nMake a widget tree for a parse tree\nparser: parser object\noptions: see below\nOptions include:\ndocument: optional document to use\nvariables: hashmap of variables to set\nparentWidget: optional parent widget for the root node\n*/\nexports.makeWidget = function(parser,options) {\n\toptions = options || {};\n\tvar widgetNode = {\n\t\t\ttype: \"widget\",\n\t\t\tchildren: []\n\t\t},\n\t\tcurrWidgetNode = widgetNode;\n\t// Create set variable widgets for each variable\n\t$tw.utils.each(options.variables,function(value,name) {\n\t\tvar setVariableWidget = {\n\t\t\ttype: \"set\",\n\t\t\tattributes: {\n\t\t\t\tname: {type: \"string\", value: name},\n\t\t\t\tvalue: {type: \"string\", value: value}\n\t\t\t},\n\t\t\tchildren: []\n\t\t};\n\t\tcurrWidgetNode.children = [setVariableWidget];\n\t\tcurrWidgetNode = setVariableWidget;\n\t});\n\t// Add in the supplied parse tree nodes\n\tcurrWidgetNode.children = parser ? parser.tree : [];\n\t// Create the widget\n\treturn new widget.widget(widgetNode,{\n\t\twiki: this,\n\t\tdocument: options.document || $tw.fakeDocument,\n\t\tparentWidget: options.parentWidget\n\t});\n};\n\n/*\nMake a widget tree for transclusion\ntitle: target tiddler title\noptions: as for wiki.makeWidget() plus:\noptions.field: optional field to transclude (defaults to \"text\")\noptions.mode: transclusion mode \"inline\" or \"block\"\noptions.children: optional array of children for the transclude widget\noptions.importVariables: optional importvariables filter string for macros to be included\noptions.importPageMacros: optional boolean; if true, equivalent to passing \"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\" to options.importVariables\n*/\nexports.makeTranscludeWidget = function(title,options) {\n\toptions = options || {};\n\tvar parseTreeDiv = {tree: [{\n\t\t\ttype: \"element\",\n\t\t\ttag: \"div\",\n\t\t\tchildren: []}]},\n\t\tparseTreeImportVariables = {\n\t\t\ttype: \"importvariables\",\n\t\t\tattributes: {\n\t\t\t\tfilter: {\n\t\t\t\t\tname: \"filter\",\n\t\t\t\t\ttype: \"string\"\n\t\t\t\t}\n\t\t\t},\n\t\t\tisBlock: false,\n\t\t\tchildren: []},\n\t\tparseTreeTransclude = {\n\t\t\ttype: \"transclude\",\n\t\t\tattributes: {\n\t\t\t\ttiddler: {\n\t\t\t\t\tname: \"tiddler\",\n\t\t\t\t\ttype: \"string\",\n\t\t\t\t\tvalue: title}},\n\t\t\tisBlock: !options.parseAsInline};\n\tif(options.importVariables || options.importPageMacros) {\n\t\tif(options.importVariables) {\n\t\t\tparseTreeImportVariables.attributes.filter.value = options.importVariables;\n\t\t} else if(options.importPageMacros) {\n\t\t\tparseTreeImportVariables.attributes.filter.value = \"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\";\n\t\t}\n\t\tparseTreeDiv.tree[0].children.push(parseTreeImportVariables);\n\t\tparseTreeImportVariables.children.push(parseTreeTransclude);\n\t} else {\n\t\tparseTreeDiv.tree[0].children.push(parseTreeTransclude);\n\t}\n\tif(options.field) {\n\t\tparseTreeTransclude.attributes.field = {type: \"string\", value: options.field};\n\t}\n\tif(options.mode) {\n\t\tparseTreeTransclude.attributes.mode = {type: \"string\", value: options.mode};\n\t}\n\tif(options.children) {\n\t\tparseTreeTransclude.children = options.children;\n\t}\n\treturn $tw.wiki.makeWidget(parseTreeDiv,options);\n};\n\n/*\nParse text in a specified format and render it into another format\n\toutputType: content type for the output\n\ttextType: content type of the input text\n\ttext: input text\n\toptions: see below\nOptions include:\nvariables: hashmap of variables to set\nparentWidget: optional parent widget for the root node\n*/\nexports.renderText = function(outputType,textType,text,options) {\n\toptions = options || {};\n\tvar parser = this.parseText(textType,text,options),\n\t\twidgetNode = this.makeWidget(parser,options);\n\tvar container = $tw.fakeDocument.createElement(\"div\");\n\twidgetNode.render(container,null);\n\treturn outputType === \"text/html\" ? container.innerHTML : container.textContent;\n};\n\n/*\nParse text from a tiddler and render it into another format\n\toutputType: content type for the output\n\ttitle: title of the tiddler to be rendered\n\toptions: see below\nOptions include:\nvariables: hashmap of variables to set\nparentWidget: optional parent widget for the root node\n*/\nexports.renderTiddler = function(outputType,title,options) {\n\toptions = options || {};\n\tvar parser = this.parseTiddler(title,options),\n\t\twidgetNode = this.makeWidget(parser,options);\n\tvar container = $tw.fakeDocument.createElement(\"div\");\n\twidgetNode.render(container,null);\n\treturn outputType === \"text/html\" ? container.innerHTML : (outputType === \"text/plain-formatted\" ? container.formattedTextContent : container.textContent);\n};\n\n/*\nReturn an array of tiddler titles that match a search string\n\ttext: The text string to search for\n\toptions: see below\nOptions available:\n\tsource: an iterator function for the source tiddlers, called source(iterator), where iterator is called as iterator(tiddler,title)\n\texclude: An array of tiddler titles to exclude from the search\n\tinvert: If true returns tiddlers that do not contain the specified string\n\tcaseSensitive: If true forces a case sensitive search\n\tliteral: If true, searches for literal string, rather than separate search terms\n\tfield: If specified, restricts the search to the specified field\n*/\nexports.search = function(text,options) {\n\toptions = options || {};\n\tvar self = this,\n\t\tt,\n\t\tinvert = !!options.invert;\n\t// Convert the search string into a regexp for each term\n\tvar terms, searchTermsRegExps,\n\t\tflags = options.caseSensitive ? \"\" : \"i\";\n\tif(options.literal) {\n\t\tif(text.length === 0) {\n\t\t\tsearchTermsRegExps = null;\n\t\t} else {\n\t\t\tsearchTermsRegExps = [new RegExp(\"(\" + $tw.utils.escapeRegExp(text) + \")\",flags)];\n\t\t}\n\t} else {\n\t\tterms = text.split(/ +/);\n\t\tif(terms.length === 1 && terms[0] === \"\") {\n\t\t\tsearchTermsRegExps = null;\n\t\t} else {\n\t\t\tsearchTermsRegExps = [];\n\t\t\tfor(t=0; t<terms.length; t++) {\n\t\t\t\tsearchTermsRegExps.push(new RegExp(\"(\" + $tw.utils.escapeRegExp(terms[t]) + \")\",flags));\n\t\t\t}\n\t\t}\n\t}\n\t// Function to check a given tiddler for the search term\n\tvar searchTiddler = function(title) {\n\t\tif(!searchTermsRegExps) {\n\t\t\treturn true;\n\t\t}\n\t\tvar tiddler = self.getTiddler(title);\n\t\tif(!tiddler) {\n\t\t\ttiddler = new $tw.Tiddler({title: title, text: \"\", type: \"text/vnd.tiddlywiki\"});\n\t\t}\n\t\tvar contentTypeInfo = $tw.config.contentTypeInfo[tiddler.fields.type] || $tw.config.contentTypeInfo[\"text/vnd.tiddlywiki\"],\n\t\t\tmatch;\n\t\tfor(var t=0; t<searchTermsRegExps.length; t++) {\n\t\t\tmatch = false;\n\t\t\tif(options.field) {\n\t\t\t\tmatch = searchTermsRegExps[t].test(tiddler.getFieldString(options.field));\n\t\t\t} else {\n\t\t\t\t// Search title, tags and body\n\t\t\t\tif(contentTypeInfo.encoding === \"utf8\") {\n\t\t\t\t\tmatch = match || searchTermsRegExps[t].test(tiddler.fields.text);\n\t\t\t\t}\n\t\t\t\tvar tags = tiddler.fields.tags ? tiddler.fields.tags.join(\"\\0\") : \"\";\n\t\t\t\tmatch = match || searchTermsRegExps[t].test(tags) || searchTermsRegExps[t].test(tiddler.fields.title);\n\t\t\t}\n\t\t\tif(!match) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t}\n\t\treturn true;\n\t};\n\t// Loop through all the tiddlers doing the search\n\tvar results = [],\n\t\tsource = options.source || this.each;\n\tsource(function(tiddler,title) {\n\t\tif(searchTiddler(title) !== options.invert) {\n\t\t\tresults.push(title);\n\t\t}\n\t});\n\t// Remove any of the results we have to exclude\n\tif(options.exclude) {\n\t\tfor(t=0; t<options.exclude.length; t++) {\n\t\t\tvar p = results.indexOf(options.exclude[t]);\n\t\t\tif(p !== -1) {\n\t\t\t\tresults.splice(p,1);\n\t\t\t}\n\t\t}\n\t}\n\treturn results;\n};\n\n/*\nTrigger a load for a tiddler if it is skinny. Returns the text, or undefined if the tiddler is missing, null if the tiddler is being lazily loaded.\n*/\nexports.getTiddlerText = function(title,defaultText) {\n\tvar tiddler = this.getTiddler(title);\n\t// Return undefined if the tiddler isn't found\n\tif(!tiddler) {\n\t\treturn defaultText;\n\t}\n\tif(tiddler.fields.text !== undefined) {\n\t\t// Just return the text if we've got it\n\t\treturn tiddler.fields.text;\n\t} else {\n\t\t// Tell any listeners about the need to lazily load this tiddler\n\t\tthis.dispatchEvent(\"lazyLoad\",title);\n\t\t// Indicate that the text is being loaded\n\t\treturn null;\n\t}\n};\n\n/*\nCheck whether the text of a tiddler matches a given value. By default, the comparison is case insensitive, and any spaces at either end of the tiddler text is trimmed\n*/\nexports.checkTiddlerText = function(title,targetText,options) {\n\toptions = options || {};\n\tvar text = this.getTiddlerText(title,\"\");\n\tif(!options.noTrim) {\n\t\ttext = text.trim();\n\t}\n\tif(!options.caseSensitive) {\n\t\ttext = text.toLowerCase();\n\t\ttargetText = targetText.toLowerCase();\n\t}\n\treturn text === targetText;\n}\n\n/*\nRead an array of browser File objects, invoking callback(tiddlerFieldsArray) once they're all read\n*/\nexports.readFiles = function(files,callback) {\n\tvar result = [],\n\t\toutstanding = files.length;\n\tfor(var f=0; f<files.length; f++) {\n\t\tthis.readFile(files[f],function(tiddlerFieldsArray) {\n\t\t\tresult.push.apply(result,tiddlerFieldsArray);\n\t\t\tif(--outstanding === 0) {\n\t\t\t\tcallback(result);\n\t\t\t}\n\t\t});\n\t}\n\treturn files.length;\n};\n\n/*\nRead a browser File object, invoking callback(tiddlerFieldsArray) with an array of tiddler fields objects\n*/\nexports.readFile = function(file,callback) {\n\t// Get the type, falling back to the filename extension\n\tvar self = this,\n\t\ttype = file.type;\n\tif(type === \"\" || !type) {\n\t\tvar dotPos = file.name.lastIndexOf(\".\");\n\t\tif(dotPos !== -1) {\n\t\t\tvar fileExtensionInfo = $tw.utils.getFileExtensionInfo(file.name.substr(dotPos));\n\t\t\tif(fileExtensionInfo) {\n\t\t\t\ttype = fileExtensionInfo.type;\n\t\t\t}\n\t\t}\n\t}\n\t// Figure out if we're reading a binary file\n\tvar contentTypeInfo = $tw.config.contentTypeInfo[type],\n\t\tisBinary = contentTypeInfo ? contentTypeInfo.encoding === \"base64\" : false;\n\t// Log some debugging information\n\tif($tw.log.IMPORT) {\n\t\tconsole.log(\"Importing file '\" + file.name + \"', type: '\" + type + \"', isBinary: \" + isBinary);\n\t}\n\t// Create the FileReader\n\tvar reader = new FileReader();\n\t// Onload\n\treader.onload = function(event) {\n\t\tvar text = event.target.result,\n\t\t\ttiddlerFields = {title: file.name || \"Untitled\", type: type};\n\t\tif(isBinary) {\n\t\t\tvar commaPos = text.indexOf(\",\");\n\t\t\tif(commaPos !== -1) {\n\t\t\t\ttext = text.substr(commaPos + 1);\n\t\t\t}\n\t\t}\n\t\t// Check whether this is an encrypted TiddlyWiki file\n\t\tvar encryptedJson = $tw.utils.extractEncryptedStoreArea(text);\n\t\tif(encryptedJson) {\n\t\t\t// If so, attempt to decrypt it with the current password\n\t\t\t$tw.utils.decryptStoreAreaInteractive(encryptedJson,function(tiddlers) {\n\t\t\t\tcallback(tiddlers);\n\t\t\t});\n\t\t} else {\n\t\t\t// Otherwise, just try to deserialise any tiddlers in the file\n\t\t\tcallback(self.deserializeTiddlers(type,text,tiddlerFields));\n\t\t}\n\t};\n\t// Kick off the read\n\tif(isBinary) {\n\t\treader.readAsDataURL(file);\n\t} else {\n\t\treader.readAsText(file);\n\t}\n};\n\n/*\nFind any existing draft of a specified tiddler\n*/\nexports.findDraft = function(targetTitle) {\n\tvar draftTitle = undefined;\n\tthis.forEachTiddler({includeSystem: true},function(title,tiddler) {\n\t\tif(tiddler.fields[\"draft.title\"] && tiddler.fields[\"draft.of\"] === targetTitle) {\n\t\t\tdraftTitle = title;\n\t\t}\n\t});\n\treturn draftTitle;\n}\n\n/*\nCheck whether the specified draft tiddler has been modified.\nIf the original tiddler doesn't exist, create a vanilla tiddler variable,\nto check if additional fields have been added.\n*/\nexports.isDraftModified = function(title) {\n\tvar tiddler = this.getTiddler(title);\n\tif(!tiddler.isDraft()) {\n\t\treturn false;\n\t}\n\tvar ignoredFields = [\"created\", \"modified\", \"title\", \"draft.title\", \"draft.of\"],\n\t\torigTiddler = this.getTiddler(tiddler.fields[\"draft.of\"]) || new $tw.Tiddler({text:\"\", tags:[]}),\n\t\ttitleModified = tiddler.fields[\"draft.title\"] !== tiddler.fields[\"draft.of\"];\n\treturn titleModified || !tiddler.isEqual(origTiddler,ignoredFields);\n};\n\n/*\nAdd a new record to the top of the history stack\ntitle: a title string or an array of title strings\nfromPageRect: page coordinates of the origin of the navigation\nhistoryTitle: title of history tiddler (defaults to $:/HistoryList)\n*/\nexports.addToHistory = function(title,fromPageRect,historyTitle) {\n\tvar story = new $tw.Story({wiki: this, historyTitle: historyTitle});\n\tstory.addToHistory(title,fromPageRect);\n};\n\n/*\nInvoke the available upgrader modules\ntitles: array of tiddler titles to be processed\ntiddlers: hashmap by title of tiddler fields of pending import tiddlers. These can be modified by the upgraders. An entry with no fields indicates a tiddler that was pending import has been suppressed. When entries are added to the pending import the tiddlers hashmap may have entries that are not present in the titles array\nReturns a hashmap of messages keyed by tiddler title.\n*/\nexports.invokeUpgraders = function(titles,tiddlers) {\n\t// Collect up the available upgrader modules\n\tvar self = this;\n\tif(!this.upgraderModules) {\n\t\tthis.upgraderModules = [];\n\t\t$tw.modules.forEachModuleOfType(\"upgrader\",function(title,module) {\n\t\t\tif(module.upgrade) {\n\t\t\t\tself.upgraderModules.push(module);\n\t\t\t}\n\t\t});\n\t}\n\t// Invoke each upgrader in turn\n\tvar messages = {};\n\tfor(var t=0; t<this.upgraderModules.length; t++) {\n\t\tvar upgrader = this.upgraderModules[t],\n\t\t\tupgraderMessages = upgrader.upgrade(this,titles,tiddlers);\n\t\t$tw.utils.extend(messages,upgraderMessages);\n\t}\n\treturn messages;\n};\n\n})();\n\n",
"title": "$:/core/modules/wiki.js",
"type": "application/javascript",
"module-type": "wikimethod"
},
"$:/palettes/Blanca": {
"title": "$:/palettes/Blanca",
"name": "Blanca",
"description": "A clean white palette to let you focus",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #66cccc\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #999999\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #ffffff\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #7897f3\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #ccc\nsidebar-foreground-shadow: rgba(255,255,255, 0.8)\nsidebar-foreground: #acacac\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: #ffffff\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #444444\nsidebar-tiddler-link-foreground: #7897f3\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #eeeeee\ntab-border-selected: #cccccc\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #ffeedd\ntag-foreground: #000\ntiddler-background: <<colour background>>\ntiddler-border: #eee\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #ff9900\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\ntoolbar-info-button:\ntoolbar-edit-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-cancel-button:\ntoolbar-done-button:\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/Blue": {
"title": "$:/palettes/Blue",
"name": "Blue",
"description": "A blue theme",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #fff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour foreground>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333353\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #999999\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #ddddff\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #5778d8\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #ffffff\nsidebar-foreground-shadow: rgba(255,255,255, 0.8)\nsidebar-foreground: #acacac\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: <<colour page-background>>\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #444444\nsidebar-tiddler-link-foreground: #5959c0\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: <<colour background>>\ntab-background: #ccccdd\ntab-border-selected: #ccccdd\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #eeeeff\ntag-foreground: #000\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #666666\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #ffffff\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #ffffff\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #5959c0\ntoolbar-new-button: #5eb95e\ntoolbar-options-button: rgb(128, 88, 165)\ntoolbar-save-button: #0e90d2\ntoolbar-info-button: #0e90d2\ntoolbar-edit-button: rgb(243, 123, 29)\ntoolbar-close-button: #dd514c\ntoolbar-delete-button: #dd514c\ntoolbar-cancel-button: rgb(243, 123, 29)\ntoolbar-done-button: #5eb95e\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/Muted": {
"title": "$:/palettes/Muted",
"name": "Muted",
"description": "Bright tiddlers on a muted background",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #bbb\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #6f6f70\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #29a6ee\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #c2c1c2\nsidebar-foreground-shadow: rgba(255,255,255,0)\nsidebar-foreground: #d3d2d4\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: #6f6f70\nsidebar-tab-background: #666667\nsidebar-tab-border-selected: #999\nsidebar-tab-border: #515151\nsidebar-tab-divider: #999\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: #999\nsidebar-tiddler-link-foreground-hover: #444444\nsidebar-tiddler-link-foreground: #d1d0d2\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #d8d8d8\ntab-border-selected: #d8d8d8\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #d5ad34\ntag-foreground: #ffffff\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #182955\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/ContrastLight": {
"title": "$:/palettes/ContrastLight",
"name": "Contrast (Light)",
"description": "High contrast and unambiguous (light version)",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #f00\nalert-border: <<colour background>>\nalert-highlight: <<colour foreground>>\nalert-muted-foreground: #800\nbackground: #fff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: <<colour background>>\nbutton-foreground: <<colour foreground>>\nbutton-border: <<colour foreground>>\ncode-background: <<colour background>>\ncode-border: <<colour foreground>>\ncode-foreground: <<colour foreground>>\ndirty-indicator: #f00\ndownload-background: #080\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: <<colour foreground>>\ndropdown-tab-background: <<colour foreground>>\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #00a\nexternal-link-foreground: #00e\nforeground: #000\nmessage-background: <<colour foreground>>\nmessage-border: <<colour background>>\nmessage-foreground: <<colour background>>\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: <<colour foreground>>\nmodal-footer-background: <<colour background>>\nmodal-footer-border: <<colour foreground>>\nmodal-header-border: <<colour foreground>>\nmuted-foreground: <<colour foreground>>\nnotification-background: <<colour background>>\nnotification-border: <<colour foreground>>\npage-background: <<colour background>>\npre-background: <<colour background>>\npre-border: <<colour foreground>>\nprimary: #00f\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: <<colour background>>\nsidebar-controls-foreground: <<colour foreground>>\nsidebar-foreground-shadow: rgba(0,0,0, 0)\nsidebar-foreground: <<colour foreground>>\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: <<colour foreground>>\nsidebar-tab-background-selected: <<colour background>>\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: <<colour foreground>>\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: <<colour foreground>>\nsidebar-tiddler-link-foreground: <<colour primary>>\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: <<colour background>>\ntab-background: <<colour foreground>>\ntab-border-selected: <<colour foreground>>\ntab-border: <<colour foreground>>\ntab-divider: <<colour foreground>>\ntab-foreground-selected: <<colour foreground>>\ntab-foreground: <<colour background>>\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #000\ntag-foreground: #fff\ntiddler-background: <<colour background>>\ntiddler-border: <<colour foreground>>\ntiddler-controls-foreground-hover: #ddd\ntiddler-controls-foreground-selected: #fdd\ntiddler-controls-foreground: <<colour foreground>>\ntiddler-editor-background: <<colour background>>\ntiddler-editor-border-image: <<colour foreground>>\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: <<colour background>>\ntiddler-editor-fields-odd: <<colour background>>\ntiddler-info-background: <<colour background>>\ntiddler-info-border: <<colour foreground>>\ntiddler-info-tab-background: <<colour background>>\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: <<colour foreground>>\ntiddler-title-foreground: <<colour foreground>>\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: <<colour foreground>>\nvery-muted-foreground: #888888\n"
},
"$:/palettes/ContrastDark": {
"title": "$:/palettes/ContrastDark",
"name": "Contrast (Dark)",
"description": "High contrast and unambiguous (dark version)",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #f00\nalert-border: <<colour background>>\nalert-highlight: <<colour foreground>>\nalert-muted-foreground: #800\nbackground: #000\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: <<colour background>>\nbutton-foreground: <<colour foreground>>\nbutton-border: <<colour foreground>>\ncode-background: <<colour background>>\ncode-border: <<colour foreground>>\ncode-foreground: <<colour foreground>>\ndirty-indicator: #f00\ndownload-background: #080\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: <<colour foreground>>\ndropdown-tab-background: <<colour foreground>>\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #00a\nexternal-link-foreground: #00e\nforeground: #fff\nmessage-background: <<colour foreground>>\nmessage-border: <<colour background>>\nmessage-foreground: <<colour background>>\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: <<colour foreground>>\nmodal-footer-background: <<colour background>>\nmodal-footer-border: <<colour foreground>>\nmodal-header-border: <<colour foreground>>\nmuted-foreground: <<colour foreground>>\nnotification-background: <<colour background>>\nnotification-border: <<colour foreground>>\npage-background: <<colour background>>\npre-background: <<colour background>>\npre-border: <<colour foreground>>\nprimary: #00f\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: <<colour background>>\nsidebar-controls-foreground: <<colour foreground>>\nsidebar-foreground-shadow: rgba(0,0,0, 0)\nsidebar-foreground: <<colour foreground>>\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: <<colour foreground>>\nsidebar-tab-background-selected: <<colour background>>\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: <<colour foreground>>\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: <<colour foreground>>\nsidebar-tiddler-link-foreground: <<colour primary>>\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: <<colour background>>\ntab-background: <<colour foreground>>\ntab-border-selected: <<colour foreground>>\ntab-border: <<colour foreground>>\ntab-divider: <<colour foreground>>\ntab-foreground-selected: <<colour foreground>>\ntab-foreground: <<colour background>>\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #fff\ntag-foreground: #000\ntiddler-background: <<colour background>>\ntiddler-border: <<colour foreground>>\ntiddler-controls-foreground-hover: #ddd\ntiddler-controls-foreground-selected: #fdd\ntiddler-controls-foreground: <<colour foreground>>\ntiddler-editor-background: <<colour background>>\ntiddler-editor-border-image: <<colour foreground>>\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: <<colour background>>\ntiddler-editor-fields-odd: <<colour background>>\ntiddler-info-background: <<colour background>>\ntiddler-info-border: <<colour foreground>>\ntiddler-info-tab-background: <<colour background>>\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: <<colour foreground>>\ntiddler-title-foreground: <<colour foreground>>\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: <<colour foreground>>\nvery-muted-foreground: #888888\n"
},
"$:/palettes/DarkPhotos": {
"created": "20150402111612188",
"description": "Good with dark photo backgrounds",
"modified": "20150402112344080",
"name": "DarkPhotos",
"tags": "$:/tags/Palette",
"title": "$:/palettes/DarkPhotos",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background: \nbutton-foreground: \nbutton-border: \ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #ddd\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #336438\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #5778d8\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #ccf\nsidebar-controls-foreground: #fff\nsidebar-foreground-shadow: rgba(0,0,0, 0.5)\nsidebar-foreground: #fff\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #eee\nsidebar-tab-background-selected: rgba(255,255,255, 0.8)\nsidebar-tab-background: rgba(255,255,255, 0.4)\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: rgba(255,255,255, 0.2)\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #aaf\nsidebar-tiddler-link-foreground: #ddf\nsite-title-foreground: #fff\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #d8d8d8\ntab-border-selected: #d8d8d8\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #ec6\ntag-foreground: #ffffff\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #182955\ntoolbar-new-button: \ntoolbar-options-button: \ntoolbar-save-button: \ntoolbar-info-button: \ntoolbar-edit-button: \ntoolbar-close-button: \ntoolbar-delete-button: \ntoolbar-cancel-button: \ntoolbar-done-button: \nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/Rocker": {
"title": "$:/palettes/Rocker",
"name": "Rocker",
"description": "A dark theme",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #999999\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #000\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #cc0000\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #ffffff\nsidebar-foreground-shadow: rgba(255,255,255, 0.0)\nsidebar-foreground: #acacac\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: #000\nsidebar-tab-background: <<colour tab-background>>\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: <<colour tab-divider>>\nsidebar-tab-foreground-selected: \nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #ffbb99\nsidebar-tiddler-link-foreground: #cc0000\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #d8d8d8\ntab-border-selected: #d8d8d8\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #ffbb99\ntag-foreground: #000\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #cc0000\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\ntoolbar-info-button:\ntoolbar-edit-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-cancel-button:\ntoolbar-done-button:\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/SolarFlare": {
"title": "$:/palettes/SolarFlare",
"name": "Solar Flare",
"description": "Warm, relaxing earth colours",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": ": Background Tones\n\nbase03: #002b36\nbase02: #073642\n\n: Content Tones\n\nbase01: #586e75\nbase00: #657b83\nbase0: #839496\nbase1: #93a1a1\n\n: Background Tones\n\nbase2: #eee8d5\nbase3: #fdf6e3\n\n: Accent Colors\n\nyellow: #b58900\norange: #cb4b16\nred: #dc322f\nmagenta: #d33682\nviolet: #6c71c4\nblue: #268bd2\ncyan: #2aa198\ngreen: #859900\n\n: Additional Tones (RA)\n\nbase10: #c0c4bb\nviolet-muted: #7c81b0\nblue-muted: #4e7baa\n\nyellow-hot: #ffcc44\norange-hot: #eb6d20\nred-hot: #ff2222\nblue-hot: #2298ee\ngreen-hot: #98ee22\n\n: Palette\n\n: Do not use colour macro for background and foreground\nbackground: #fdf6e3\n download-foreground: <<colour background>>\n dragger-foreground: <<colour background>>\n dropdown-background: <<colour background>>\n modal-background: <<colour background>>\n sidebar-foreground-shadow: <<colour background>>\n tiddler-background: <<colour background>>\n tiddler-border: <<colour background>>\n tiddler-link-background: <<colour background>>\n tab-background-selected: <<colour background>>\n dropdown-tab-background-selected: <<colour tab-background-selected>>\nforeground: #657b83\n dragger-background: <<colour foreground>>\n tab-foreground: <<colour foreground>>\n tab-foreground-selected: <<colour tab-foreground>>\n sidebar-tab-foreground-selected: <<colour tab-foreground-selected>>\n sidebar-tab-foreground: <<colour tab-foreground>>\n sidebar-button-foreground: <<colour foreground>>\n sidebar-controls-foreground: <<colour foreground>>\n sidebar-foreground: <<colour foreground>>\n: base03\n: base02\n: base01\n alert-muted-foreground: <<colour base01>>\n: base00\n code-foreground: <<colour base00>>\n message-foreground: <<colour base00>>\n tag-foreground: <<colour base00>>\n: base0\n sidebar-tiddler-link-foreground: <<colour base0>>\n: base1\n muted-foreground: <<colour base1>>\n blockquote-bar: <<colour muted-foreground>>\n dropdown-border: <<colour muted-foreground>>\n sidebar-muted-foreground: <<colour muted-foreground>>\n tiddler-title-foreground: <<colour muted-foreground>>\n site-title-foreground: <<colour tiddler-title-foreground>>\n: base2\n modal-footer-background: <<colour base2>>\n page-background: <<colour base2>>\n modal-backdrop: <<colour page-background>>\n notification-background: <<colour page-background>>\n code-background: <<colour page-background>>\n code-border: <<colour code-background>>\n pre-background: <<colour page-background>>\n pre-border: <<colour pre-background>>\n sidebar-tab-background-selected: <<colour page-background>>\n table-header-background: <<colour base2>>\n tag-background: <<colour base2>>\n tiddler-editor-background: <<colour base2>>\n tiddler-info-background: <<colour base2>>\n tiddler-info-tab-background: <<colour base2>>\n tab-background: <<colour base2>>\n dropdown-tab-background: <<colour tab-background>>\n: base3\n alert-background: <<colour base3>>\n message-background: <<colour base3>>\n: yellow\n: orange\n: red\n: magenta\n alert-highlight: <<colour magenta>>\n: violet\n external-link-foreground: <<colour violet>>\n: blue\n: cyan\n: green\n: base10\n tiddler-controls-foreground: <<colour base10>>\n: violet-muted\n external-link-foreground-visited: <<colour violet-muted>>\n: blue-muted\n primary: <<colour blue-muted>>\n download-background: <<colour primary>>\n tiddler-link-foreground: <<colour primary>>\n\nalert-border: #b99e2f\ndirty-indicator: #ff0000\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nmessage-border: #cfd6e6\nmodal-border: #999999\nsidebar-controls-foreground-hover:\nsidebar-muted-foreground-hover:\nsidebar-tab-background: #ded8c5\nsidebar-tiddler-link-foreground-hover:\nstatic-alert-foreground: #aaaaaa\ntab-border: #cccccc\n modal-footer-border: <<colour tab-border>>\n modal-header-border: <<colour tab-border>>\n notification-border: <<colour tab-border>>\n sidebar-tab-border: <<colour tab-border>>\n tab-border-selected: <<colour tab-border>>\n sidebar-tab-border-selected: <<colour tab-border-selected>>\ntab-divider: #d8d8d8\n sidebar-tab-divider: <<colour tab-divider>>\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-border: #dddddd\ntiddler-subtitle-foreground: #c0c0c0\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\ntoolbar-info-button:\ntoolbar-edit-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-cancel-button:\ntoolbar-done-button:\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/palettes/Vanilla": {
"title": "$:/palettes/Vanilla",
"name": "Vanilla",
"description": "Pale and unobtrusive",
"tags": "$:/tags/Palette",
"type": "application/x-tiddler-dictionary",
"text": "alert-background: #ffe476\nalert-border: #b99e2f\nalert-highlight: #881122\nalert-muted-foreground: #b99e2f\nbackground: #ffffff\nblockquote-bar: <<colour muted-foreground>>\nbutton-background:\nbutton-foreground:\nbutton-border:\ncode-background: #f7f7f9\ncode-border: #e1e1e8\ncode-foreground: #dd1144\ndirty-indicator: #ff0000\ndownload-background: #34c734\ndownload-foreground: <<colour background>>\ndragger-background: <<colour foreground>>\ndragger-foreground: <<colour background>>\ndropdown-background: <<colour background>>\ndropdown-border: <<colour muted-foreground>>\ndropdown-tab-background-selected: #fff\ndropdown-tab-background: #ececec\ndropzone-background: rgba(0,200,0,0.7)\nexternal-link-background-hover: inherit\nexternal-link-background-visited: inherit\nexternal-link-background: inherit\nexternal-link-foreground-hover: inherit\nexternal-link-foreground-visited: #0000aa\nexternal-link-foreground: #0000ee\nforeground: #333333\nmessage-background: #ecf2ff\nmessage-border: #cfd6e6\nmessage-foreground: #547599\nmodal-backdrop: <<colour foreground>>\nmodal-background: <<colour background>>\nmodal-border: #999999\nmodal-footer-background: #f5f5f5\nmodal-footer-border: #dddddd\nmodal-header-border: #eeeeee\nmuted-foreground: #bbb\nnotification-background: #ffffdd\nnotification-border: #999999\npage-background: #f4f4f4\npre-background: #f5f5f5\npre-border: #cccccc\nprimary: #5778d8\nsidebar-button-foreground: <<colour foreground>>\nsidebar-controls-foreground-hover: #000000\nsidebar-controls-foreground: #aaaaaa\nsidebar-foreground-shadow: rgba(255,255,255, 0.8)\nsidebar-foreground: #acacac\nsidebar-muted-foreground-hover: #444444\nsidebar-muted-foreground: #c0c0c0\nsidebar-tab-background-selected: #f4f4f4\nsidebar-tab-background: #e0e0e0\nsidebar-tab-border-selected: <<colour tab-border-selected>>\nsidebar-tab-border: <<colour tab-border>>\nsidebar-tab-divider: #e4e4e4\nsidebar-tab-foreground-selected:\nsidebar-tab-foreground: <<colour tab-foreground>>\nsidebar-tiddler-link-foreground-hover: #444444\nsidebar-tiddler-link-foreground: #999999\nsite-title-foreground: <<colour tiddler-title-foreground>>\nstatic-alert-foreground: #aaaaaa\ntab-background-selected: #ffffff\ntab-background: #d8d8d8\ntab-border-selected: #d8d8d8\ntab-border: #cccccc\ntab-divider: #d8d8d8\ntab-foreground-selected: <<colour tab-foreground>>\ntab-foreground: #666666\ntable-border: #dddddd\ntable-footer-background: #a8a8a8\ntable-header-background: #f0f0f0\ntag-background: #ec6\ntag-foreground: #ffffff\ntiddler-background: <<colour background>>\ntiddler-border: <<colour background>>\ntiddler-controls-foreground-hover: #888888\ntiddler-controls-foreground-selected: #444444\ntiddler-controls-foreground: #cccccc\ntiddler-editor-background: #f8f8f8\ntiddler-editor-border-image: #ffffff\ntiddler-editor-border: #cccccc\ntiddler-editor-fields-even: #e0e8e0\ntiddler-editor-fields-odd: #f0f4f0\ntiddler-info-background: #f8f8f8\ntiddler-info-border: #dddddd\ntiddler-info-tab-background: #f8f8f8\ntiddler-link-background: <<colour background>>\ntiddler-link-foreground: <<colour primary>>\ntiddler-subtitle-foreground: #c0c0c0\ntiddler-title-foreground: #182955\ntoolbar-new-button:\ntoolbar-options-button:\ntoolbar-save-button:\ntoolbar-info-button:\ntoolbar-edit-button:\ntoolbar-close-button:\ntoolbar-delete-button:\ntoolbar-cancel-button:\ntoolbar-done-button:\nuntagged-background: #999999\nvery-muted-foreground: #888888\n"
},
"$:/core/readme": {
"title": "$:/core/readme",
"text": "This plugin contains TiddlyWiki's core components, comprising:\n\n* JavaScript code modules\n* Icons\n* Templates needed to create TiddlyWiki's user interface\n* British English (''en-GB'') translations of the localisable strings used by the core\n"
},
"$:/library/sjcl.js/license": {
"title": "$:/library/sjcl.js/license",
"type": "text/plain",
"text": "SJCL is open. You can use, modify and redistribute it under a BSD\nlicense or under the GNU GPL, version 2.0.\n\n---------------------------------------------------------------------\n\nhttp://opensource.org/licenses/BSD-2-Clause\n\nCopyright (c) 2009-2015, Emily Stark, Mike Hamburg and Dan Boneh at\nStanford University. All rights reserved.\n\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are\nmet:\n\n1. Redistributions of source code must retain the above copyright\nnotice, this list of conditions and the following disclaimer.\n\n2. Redistributions in binary form must reproduce the above copyright\nnotice, this list of conditions and the following disclaimer in the\ndocumentation and/or other materials provided with the distribution.\n\nTHIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS\nIS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED\nTO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A\nPARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT\nHOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,\nSPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED\nTO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR\nPROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF\nLIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING\nNEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n---------------------------------------------------------------------\n\nhttp://opensource.org/licenses/GPL-2.0\n\nThe Stanford Javascript Crypto Library (hosted here on GitHub) is a\nproject by the Stanford Computer Security Lab to build a secure,\npowerful, fast, small, easy-to-use, cross-browser library for\ncryptography in Javascript.\n\nCopyright (c) 2009-2015, Emily Stark, Mike Hamburg and Dan Boneh at\nStanford University.\n\nThis program is free software; you can redistribute it and/or modify it\nunder the terms of the GNU General Public License as published by the\nFree Software Foundation; either version 2 of the License, or (at your\noption) any later version.\n\nThis program is distributed in the hope that it will be useful, but\nWITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General\nPublic License for more details.\n\nYou should have received a copy of the GNU General Public License along\nwith this program; if not, write to the Free Software Foundation, Inc.,\n59 Temple Place, Suite 330, Boston, MA 02111-1307 USA"
},
"$:/core/templates/MOTW.html": {
"title": "$:/core/templates/MOTW.html",
"text": "\\rules only filteredtranscludeinline transcludeinline entity\n<!-- The following comment is called a MOTW comment and is necessary for the TiddlyIE Internet Explorer extension -->\n<!-- saved from url=(0021)http://tiddlywiki.com --> "
},
"$:/core/templates/alltiddlers.template.html": {
"title": "$:/core/templates/alltiddlers.template.html",
"type": "text/vnd.tiddlywiki-html",
"text": "<!-- This template is provided for backwards compatibility with older versions of TiddlyWiki -->\n\n<$set name=\"exportFilter\" value=\"[!is[system]sort[title]]\">\n\n{{$:/core/templates/exporters/StaticRiver}}\n\n</$set>\n"
},
"$:/core/templates/canonical-uri-external-image": {
"title": "$:/core/templates/canonical-uri-external-image",
"text": "<!--\n\nThis template is used to assign the ''_canonical_uri'' field to external images.\n\nChange the `./images/` part to a different base URI. The URI can be relative or absolute.\n\n-->\n./images/<$view field=\"title\" format=\"doubleurlencoded\"/>"
},
"$:/core/templates/canonical-uri-external-text": {
"title": "$:/core/templates/canonical-uri-external-text",
"text": "<!--\n\nThis template is used to assign the ''_canonical_uri'' field to external text files.\n\nChange the `./text/` part to a different base URI. The URI can be relative or absolute.\n\n-->\n./text/<$view field=\"title\" format=\"doubleurlencoded\"/>.tid"
},
"$:/core/templates/css-tiddler": {
"title": "$:/core/templates/css-tiddler",
"text": "<!--\n\nThis template is used for saving CSS tiddlers as a style tag with data attributes representing the tiddler fields.\n\n-->`<style`<$fields template=' data-tiddler-$name$=\"$encoded_value$\"'></$fields>` type=\"text/css\">`<$view field=\"text\" format=\"text\" />`</style>`"
},
"$:/core/templates/exporters/CsvFile": {
"title": "$:/core/templates/exporters/CsvFile",
"tags": "$:/tags/Exporter",
"description": "{{$:/language/Exporters/CsvFile}}",
"extension": ".csv",
"text": "\\define renderContent()\n<$text text=<<csvtiddlers filter:\"\"\"$(exportFilter)$\"\"\" format:\"quoted-comma-sep\">>/>\n\\end\n<<renderContent>>\n"
},
"$:/core/templates/exporters/JsonFile": {
"title": "$:/core/templates/exporters/JsonFile",
"tags": "$:/tags/Exporter",
"description": "{{$:/language/Exporters/JsonFile}}",
"extension": ".json",
"text": "\\define renderContent()\n<$text text=<<jsontiddlers filter:\"\"\"$(exportFilter)$\"\"\">>/>\n\\end\n<<renderContent>>\n"
},
"$:/core/templates/exporters/StaticRiver": {
"title": "$:/core/templates/exporters/StaticRiver",
"tags": "$:/tags/Exporter",
"description": "{{$:/language/Exporters/StaticRiver}}",
"extension": ".html",
"text": "\\define tv-wikilink-template() #$uri_encoded$\n\\define tv-config-toolbar-icons() no\n\\define tv-config-toolbar-text() no\n\\define tv-config-toolbar-class() tc-btn-invisible\n\\rules only filteredtranscludeinline transcludeinline\n<!doctype html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"{{$:/core/templates/version}}\" />\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<title>{{$:/core/wiki/title}}</title>\n<div id=\"styleArea\">\n{{$:/boot/boot.css||$:/core/templates/css-tiddler}}\n</div>\n<style type=\"text/css\">\n{{$:/core/ui/PageStylesheet||$:/core/templates/wikified-tiddler}}\n</style>\n</head>\n<body class=\"tc-body\">\n{{$:/StaticBanner||$:/core/templates/html-tiddler}}\n<section class=\"tc-story-river\">\n{{$:/core/templates/exporters/StaticRiver/Content||$:/core/templates/html-tiddler}}\n</section>\n</body>\n</html>\n"
},
"$:/core/templates/exporters/StaticRiver/Content": {
"title": "$:/core/templates/exporters/StaticRiver/Content",
"text": "\\define renderContent()\n{{{ $(exportFilter)$ ||$:/core/templates/static-tiddler}}}\n\\end\n<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\">\n<<renderContent>>\n</$importvariables>\n"
},
"$:/core/templates/exporters/TidFile": {
"title": "$:/core/templates/exporters/TidFile",
"tags": "$:/tags/Exporter",
"description": "{{$:/language/Exporters/TidFile}}",
"extension": ".tid",
"text": "\\define renderContent()\n{{{ $(exportFilter)$ +[limit[1]] ||$:/core/templates/tid-tiddler}}}\n\\end\n<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\"><<renderContent>></$importvariables>"
},
"$:/core/templates/html-div-tiddler": {
"title": "$:/core/templates/html-div-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers as an HTML DIV tag with attributes representing the tiddler fields.\n\n-->`<div`<$fields template=' $name$=\"$encoded_value$\"'></$fields>`>\n<pre>`<$view field=\"text\" format=\"htmlencoded\" />`</pre>\n</div>`\n"
},
"$:/core/templates/html-tiddler": {
"title": "$:/core/templates/html-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers as raw HTML\n\n--><$view field=\"text\" format=\"htmlwikified\" />"
},
"$:/core/templates/javascript-tiddler": {
"title": "$:/core/templates/javascript-tiddler",
"text": "<!--\n\nThis template is used for saving JavaScript tiddlers as a script tag with data attributes representing the tiddler fields.\n\n-->`<script`<$fields template=' data-tiddler-$name$=\"$encoded_value$\"'></$fields>` type=\"text/javascript\">`<$view field=\"text\" format=\"text\" />`</script>`"
},
"$:/core/templates/json-tiddler": {
"title": "$:/core/templates/json-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers as raw JSON\n\n--><$text text=<<jsontiddler>>/>"
},
"$:/core/templates/module-tiddler": {
"title": "$:/core/templates/module-tiddler",
"text": "<!--\n\nThis template is used for saving JavaScript tiddlers as a script tag with data attributes representing the tiddler fields. The body of the tiddler is wrapped in a call to the `$tw.modules.define` function in order to define the body of the tiddler as a module\n\n-->`<script`<$fields template=' data-tiddler-$name$=\"$encoded_value$\"'></$fields>` type=\"text/javascript\" data-module=\"yes\">$tw.modules.define(\"`<$view field=\"title\" format=\"jsencoded\" />`\",\"`<$view field=\"module-type\" format=\"jsencoded\" />`\",function(module,exports,require) {`<$view field=\"text\" format=\"text\" />`});\n</script>`"
},
"$:/core/templates/plain-text-tiddler": {
"title": "$:/core/templates/plain-text-tiddler",
"text": "<$view field=\"text\" format=\"text\" />"
},
"$:/core/templates/raw-static-tiddler": {
"title": "$:/core/templates/raw-static-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers as static HTML\n\n--><$view field=\"text\" format=\"plainwikified\" />"
},
"$:/core/save/all": {
"title": "$:/core/save/all",
"text": "\\define saveTiddlerFilter()\n[is[tiddler]] -[prefix[$:/state/popup/]] -[[$:/HistoryList]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] +[sort[title]] $(publishFilter)$\n\\end\n{{$:/core/templates/tiddlywiki5.html}}\n"
},
"$:/core/save/empty": {
"title": "$:/core/save/empty",
"text": "\\define saveTiddlerFilter()\n[is[system]] -[prefix[$:/state/popup/]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] +[sort[title]]\n\\end\n{{$:/core/templates/tiddlywiki5.html}}\n"
},
"$:/core/save/lazy-all": {
"title": "$:/core/save/lazy-all",
"text": "\\define saveTiddlerFilter()\n[is[system]] -[prefix[$:/state/popup/]] -[[$:/HistoryList]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] +[sort[title]] \n\\end\n{{$:/core/templates/tiddlywiki5.html}}\n"
},
"$:/core/save/lazy-images": {
"title": "$:/core/save/lazy-images",
"text": "\\define saveTiddlerFilter()\n[is[tiddler]] -[prefix[$:/state/popup/]] -[[$:/HistoryList]] -[[$:/boot/boot.css]] -[type[application/javascript]library[yes]] -[[$:/boot/boot.js]] -[[$:/boot/bootprefix.js]] -[!is[system]is[image]] +[sort[title]] \n\\end\n{{$:/core/templates/tiddlywiki5.html}}\n"
},
"$:/core/templates/single.tiddler.window": {
"title": "$:/core/templates/single.tiddler.window",
"text": "<$set name=\"themeTitle\" value={{$:/view}}>\n\n<$set name=\"tempCurrentTiddler\" value=<<currentTiddler>>>\n\n<$set name=\"currentTiddler\" value={{$:/language}}>\n\n<$set name=\"languageTitle\" value={{!!name}}>\n\n<$set name=\"currentTiddler\" value=<<tempCurrentTiddler>>>\n\n<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\">\n\n<$navigator story=\"$:/StoryList\" history=\"$:/HistoryList\">\n\n<$transclude mode=\"block\"/>\n\n</$navigator>\n\n</$importvariables>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n"
},
"$:/core/templates/split-recipe": {
"title": "$:/core/templates/split-recipe",
"text": "<$list filter=\"[!is[system]]\">\ntiddler: <$view field=\"title\" format=\"urlencoded\"/>.tid\n</$list>\n"
},
"$:/core/templates/static-tiddler": {
"title": "$:/core/templates/static-tiddler",
"text": "<a name=<<currentTiddler>>>\n<$transclude tiddler=\"$:/core/ui/ViewTemplate\"/>\n</a>"
},
"$:/core/templates/static.area": {
"title": "$:/core/templates/static.area",
"text": "<$reveal type=\"nomatch\" state=\"$:/isEncrypted\" text=\"yes\">\n{{{ [all[shadows+tiddlers]tag[$:/tags/RawStaticContent]!has[draft.of]] ||$:/core/templates/raw-static-tiddler}}}\n{{$:/core/templates/static.content||$:/core/templates/html-tiddler}}\n</$reveal>\n<$reveal type=\"match\" state=\"$:/isEncrypted\" text=\"yes\">\nThis file contains an encrypted ~TiddlyWiki. Enable ~JavaScript and enter the decryption password when prompted.\n</$reveal>\n"
},
"$:/core/templates/static.content": {
"title": "$:/core/templates/static.content",
"type": "text/vnd.tiddlywiki",
"text": "<!-- For Google, and people without JavaScript-->\nThis [[TiddlyWiki|http://tiddlywiki.com]] contains the following tiddlers:\n\n<ul>\n<$list filter=<<saveTiddlerFilter>>>\n<li><$view field=\"title\" format=\"text\"></$view></li>\n</$list>\n</ul>\n"
},
"$:/core/templates/static.template.css": {
"title": "$:/core/templates/static.template.css",
"text": "{{$:/boot/boot.css||$:/core/templates/plain-text-tiddler}}\n\n{{$:/core/ui/PageStylesheet||$:/core/templates/wikified-tiddler}}\n"
},
"$:/core/templates/static.template.html": {
"title": "$:/core/templates/static.template.html",
"type": "text/vnd.tiddlywiki-html",
"text": "\\define tv-wikilink-template() static/$uri_doubleencoded$.html\n\\define tv-config-toolbar-icons() no\n\\define tv-config-toolbar-text() no\n\\define tv-config-toolbar-class() tc-btn-invisible\n\\rules only filteredtranscludeinline transcludeinline\n<!doctype html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"{{$:/core/templates/version}}\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<title>{{$:/core/wiki/title}}</title>\n<div id=\"styleArea\">\n{{$:/boot/boot.css||$:/core/templates/css-tiddler}}\n</div>\n<style type=\"text/css\">\n{{$:/core/ui/PageStylesheet||$:/core/templates/wikified-tiddler}}\n</style>\n</head>\n<body class=\"tc-body\">\n{{$:/StaticBanner||$:/core/templates/html-tiddler}}\n{{$:/core/ui/PageTemplate||$:/core/templates/html-tiddler}}\n</body>\n</html>\n"
},
"$:/core/templates/static.tiddler.html": {
"title": "$:/core/templates/static.tiddler.html",
"text": "\\define tv-wikilink-template() $uri_doubleencoded$.html\n\\define tv-config-toolbar-icons() no\n\\define tv-config-toolbar-text() no\n\\define tv-config-toolbar-class() tc-btn-invisible\n`<!doctype html>\n<html>\n<head>\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"`{{$:/core/templates/version}}`\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\">\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<link rel=\"stylesheet\" href=\"static.css\">\n<title>`<$view field=\"caption\"><$view field=\"title\"/></$view>: {{$:/core/wiki/title}}`</title>\n</head>\n<body class=\"tc-body\">\n`{{$:/StaticBanner||$:/core/templates/html-tiddler}}`\n<section class=\"tc-story-river\">\n`<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\">\n<$view tiddler=\"$:/core/ui/ViewTemplate\" format=\"htmlwikified\"/>\n</$importvariables>`\n</section>\n</body>\n</html>\n`"
},
"$:/core/templates/store.area.template.html": {
"title": "$:/core/templates/store.area.template.html",
"text": "<$reveal type=\"nomatch\" state=\"$:/isEncrypted\" text=\"yes\">\n`<div id=\"storeArea\" style=\"display:none;\">`\n<$list filter=<<saveTiddlerFilter>> template=\"$:/core/templates/html-div-tiddler\"/>\n`</div>`\n</$reveal>\n<$reveal type=\"match\" state=\"$:/isEncrypted\" text=\"yes\">\n`<!--~~ Encrypted tiddlers ~~-->`\n`<pre id=\"encryptedStoreArea\" type=\"text/plain\" style=\"display:none;\">`\n<$encrypt filter=<<saveTiddlerFilter>>/>\n`</pre>`\n</$reveal>"
},
"$:/core/templates/tid-tiddler": {
"title": "$:/core/templates/tid-tiddler",
"text": "<!--\n\nThis template is used for saving tiddlers in TiddlyWeb *.tid format\n\n--><$fields exclude='text bag' template='$name$: $value$\n'></$fields>`\n`<$view field=\"text\" format=\"text\" />"
},
"$:/core/templates/tiddler-metadata": {
"title": "$:/core/templates/tiddler-metadata",
"text": "<!--\n\nThis template is used for saving tiddler metadata *.meta files\n\n--><$fields exclude='text bag' template='$name$: $value$\n'></$fields>"
},
"$:/core/templates/tiddlywiki5.html": {
"title": "$:/core/templates/tiddlywiki5.html",
"text": "\\rules only filteredtranscludeinline transcludeinline\n<!doctype html>\n{{$:/core/templates/MOTW.html}}<html>\n<head>\n<meta http-equiv=\"X-UA-Compatible\" content=\"IE=Edge\">\n<meta http-equiv=\"Content-Type\" content=\"text/html;charset=utf-8\" />\n<meta name=\"application-name\" content=\"TiddlyWiki\" />\n<meta name=\"generator\" content=\"TiddlyWiki\" />\n<meta name=\"tiddlywiki-version\" content=\"{{$:/core/templates/version}}\" />\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\" />\n<meta name=\"apple-mobile-web-app-capable\" content=\"yes\" />\n<meta name=\"apple-mobile-web-app-status-bar-style\" content=\"black-translucent\" />\n<meta name=\"mobile-web-app-capable\" content=\"yes\"/>\n<meta name=\"format-detection\" content=\"telephone=no\" />\n<meta name=\"copyright\" content=\"{{$:/core/copyright.txt}}\" />\n<link id=\"faviconLink\" rel=\"shortcut icon\" href=\"favicon.ico\">\n<title>{{$:/core/wiki/title}}</title>\n<!--~~ This is a Tiddlywiki file. The points of interest in the file are marked with this pattern ~~-->\n\n<!--~~ Raw markup ~~-->\n{{{ [all[shadows+tiddlers]tag[$:/core/wiki/rawmarkup]] [all[shadows+tiddlers]tag[$:/tags/RawMarkup]] ||$:/core/templates/plain-text-tiddler}}}\n{{{ [all[shadows+tiddlers]tag[$:/tags/RawMarkupWikified]] ||$:/core/templates/raw-static-tiddler}}}\n</head>\n<body class=\"tc-body\">\n<!--~~ Static styles ~~-->\n<div id=\"styleArea\">\n{{$:/boot/boot.css||$:/core/templates/css-tiddler}}\n</div>\n<!--~~ Static content for Google and browsers without JavaScript ~~-->\n<noscript>\n<div id=\"splashArea\">\n{{$:/core/templates/static.area}}\n</div>\n</noscript>\n<!--~~ Ordinary tiddlers ~~-->\n{{$:/core/templates/store.area.template.html}}\n<!--~~ Library modules ~~-->\n<div id=\"libraryModules\" style=\"display:none;\">\n{{{ [is[system]type[application/javascript]library[yes]] ||$:/core/templates/javascript-tiddler}}}\n</div>\n<!--~~ Boot kernel prologue ~~-->\n<div id=\"bootKernelPrefix\" style=\"display:none;\">\n{{ $:/boot/bootprefix.js ||$:/core/templates/javascript-tiddler}}\n</div>\n<!--~~ Boot kernel ~~-->\n<div id=\"bootKernel\" style=\"display:none;\">\n{{ $:/boot/boot.js ||$:/core/templates/javascript-tiddler}}\n</div>\n</body>\n</html>\n"
},
"$:/core/templates/version": {
"title": "$:/core/templates/version",
"text": "<<version>>"
},
"$:/core/templates/wikified-tiddler": {
"title": "$:/core/templates/wikified-tiddler",
"text": "<$transclude />"
},
"$:/core/ui/AboveStory/tw2-plugin-check": {
"title": "$:/core/ui/AboveStory/tw2-plugin-check",
"tags": "$:/tags/AboveStory",
"text": "\\define lingo-base() $:/language/AboveStory/ClassicPlugin/\n<$list filter=\"[all[system+tiddlers]tag[systemConfig]limit[1]]\">\n\n<div class=\"tc-message-box\">\n\n<<lingo Warning>>\n\n<ul>\n\n<$list filter=\"[all[system+tiddlers]tag[systemConfig]]\">\n\n<li>\n\n<$link><$view field=\"title\"/></$link>\n\n</li>\n\n</$list>\n\n</ul>\n\n</div>\n\n</$list>\n"
},
"$:/core/ui/AdvancedSearch/Filter": {
"title": "$:/core/ui/AdvancedSearch/Filter",
"tags": "$:/tags/AdvancedSearch",
"caption": "{{$:/language/Search/Filter/Caption}}",
"text": "\\define lingo-base() $:/language/Search/\n<<lingo Filter/Hint>>\n\n<div class=\"tc-search tc-advanced-search\">\n<$edit-text tiddler=\"$:/temp/advancedsearch\" type=\"search\" tag=\"input\"/>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/AdvancedSearch/FilterButton]!has[draft.of]]\"><$transclude/></$list>\n</div>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$set name=\"resultCount\" value=\"\"\"<$count filter={{$:/temp/advancedsearch}}/>\"\"\">\n<div class=\"tc-search-results\">\n<<lingo Filter/Matches>>\n<$list filter={{$:/temp/advancedsearch}} template=\"$:/core/ui/ListItemTemplate\"/>\n</div>\n</$set>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Filter/FilterButtons/clear": {
"title": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/clear",
"tags": "$:/tags/AdvancedSearch/FilterButton",
"text": "<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Filter/FilterButtons/delete": {
"title": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/delete",
"tags": "$:/tags/AdvancedSearch/FilterButton",
"text": "<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button popup=<<qualify \"$:/state/filterDeleteDropdown\">> class=\"tc-btn-invisible\">\n{{$:/core/images/delete-button}}\n</$button>\n</$reveal>\n\n<$reveal state=<<qualify \"$:/state/filterDeleteDropdown\">> type=\"popup\" position=\"belowleft\" animate=\"yes\">\n<div class=\"tc-block-dropdown-wrapper\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown\">\n<div class=\"tc-dropdown-item-plain\">\n<$set name=\"resultCount\" value=\"\"\"<$count filter={{$:/temp/advancedsearch}}/>\"\"\">\nAre you sure you wish to delete <<resultCount>> tiddler(s)?\n</$set>\n</div>\n<div class=\"tc-dropdown-item-plain\">\n<$button class=\"tc-btn\">\n<$action-deletetiddler $filter={{$:/temp/advancedsearch}}/>\nDelete these tiddlers\n</$button>\n</div>\n</div>\n</div>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Filter/FilterButtons/dropdown": {
"title": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/dropdown",
"tags": "$:/tags/AdvancedSearch/FilterButton",
"text": "<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/filterDropdown\">> class=\"tc-btn-invisible\">\n{{$:/core/images/down-arrow}}\n</$button>\n</span>\n\n<$reveal state=<<qualify \"$:/state/filterDropdown\">> type=\"popup\" position=\"belowleft\" animate=\"yes\">\n<$linkcatcher to=\"$:/temp/advancedsearch\">\n<div class=\"tc-block-dropdown-wrapper\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Filter]]\"><$link to={{!!filter}}><$transclude field=\"description\"/></$link>\n</$list>\n</div>\n</div>\n</$linkcatcher>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Filter/FilterButtons/export": {
"title": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/export",
"tags": "$:/tags/AdvancedSearch/FilterButton",
"text": "<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$macrocall $name=\"exportButton\" exportFilter={{$:/temp/advancedsearch}} lingoBase=\"$:/language/Buttons/ExportTiddlers/\"/>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Shadows": {
"title": "$:/core/ui/AdvancedSearch/Shadows",
"tags": "$:/tags/AdvancedSearch",
"caption": "{{$:/language/Search/Shadows/Caption}}",
"text": "\\define lingo-base() $:/language/Search/\n<$linkcatcher to=\"$:/temp/advancedsearch\">\n\n<<lingo Shadows/Hint>>\n\n<div class=\"tc-search\">\n<$edit-text tiddler=\"$:/temp/advancedsearch\" type=\"search\" tag=\"input\"/>\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</div>\n\n</$linkcatcher>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n\n<$list filter=\"[{$:/temp/advancedsearch}minlength{$:/config/Search/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n\n<$set name=\"resultCount\" value=\"\"\"<$count filter=\"[all[shadows]search{$:/temp/advancedsearch}] -[[$:/temp/advancedsearch]]\"/>\"\"\">\n\n<div class=\"tc-search-results\">\n\n<<lingo Shadows/Matches>>\n\n<$list filter=\"[all[shadows]search{$:/temp/advancedsearch}sort[title]limit[250]] -[[$:/temp/advancedsearch]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n\n</div>\n\n</$set>\n\n</$list>\n\n</$reveal>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"match\" text=\"\">\n\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/Standard": {
"title": "$:/core/ui/AdvancedSearch/Standard",
"tags": "$:/tags/AdvancedSearch",
"caption": "{{$:/language/Search/Standard/Caption}}",
"text": "\\define lingo-base() $:/language/Search/\n<$linkcatcher to=\"$:/temp/advancedsearch\">\n\n<<lingo Standard/Hint>>\n\n<div class=\"tc-search\">\n<$edit-text tiddler=\"$:/temp/advancedsearch\" type=\"search\" tag=\"input\"/>\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</div>\n\n</$linkcatcher>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$list filter=\"[{$:/temp/advancedsearch}minlength{$:/config/Search/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n<$set name=\"searchTiddler\" value=\"$:/temp/advancedsearch\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]butfirst[]limit[1]]\" emptyMessage=\"\"\"\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]]\">\n<$transclude/>\n</$list>\n\"\"\">\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]]\" default={{$:/config/SearchResults/Default}}/>\n</$list>\n</$set>\n</$list>\n</$reveal>\n"
},
"$:/core/ui/AdvancedSearch/System": {
"title": "$:/core/ui/AdvancedSearch/System",
"tags": "$:/tags/AdvancedSearch",
"caption": "{{$:/language/Search/System/Caption}}",
"text": "\\define lingo-base() $:/language/Search/\n<$linkcatcher to=\"$:/temp/advancedsearch\">\n\n<<lingo System/Hint>>\n\n<div class=\"tc-search\">\n<$edit-text tiddler=\"$:/temp/advancedsearch\" type=\"search\" tag=\"input\"/>\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</div>\n\n</$linkcatcher>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"nomatch\" text=\"\">\n\n<$list filter=\"[{$:/temp/advancedsearch}minlength{$:/config/Search/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n\n<$set name=\"resultCount\" value=\"\"\"<$count filter=\"[is[system]search{$:/temp/advancedsearch}] -[[$:/temp/advancedsearch]]\"/>\"\"\">\n\n<div class=\"tc-search-results\">\n\n<<lingo System/Matches>>\n\n<$list filter=\"[is[system]search{$:/temp/advancedsearch}sort[title]limit[250]] -[[$:/temp/advancedsearch]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n\n</div>\n\n</$set>\n\n</$list>\n\n</$reveal>\n\n<$reveal state=\"$:/temp/advancedsearch\" type=\"match\" text=\"\">\n\n</$reveal>\n"
},
"$:/AdvancedSearch": {
"title": "$:/AdvancedSearch",
"icon": "$:/core/images/advanced-search-button",
"color": "#bbb",
"text": "<div class=\"tc-advanced-search\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/AdvancedSearch]!has[draft.of]]\" \"$:/core/ui/AdvancedSearch/System\">>\n</div>\n"
},
"$:/core/ui/AlertTemplate": {
"title": "$:/core/ui/AlertTemplate",
"text": "<div class=\"tc-alert\">\n<div class=\"tc-alert-toolbar\">\n<$button class=\"tc-btn-invisible\"><$action-deletetiddler $tiddler=<<currentTiddler>>/>{{$:/core/images/delete-button}}</$button>\n</div>\n<div class=\"tc-alert-subtitle\">\n<$view field=\"component\"/> - <$view field=\"modified\" format=\"date\" template=\"0hh:0mm:0ss DD MM YYYY\"/> <$reveal type=\"nomatch\" state=\"!!count\" text=\"\"><span class=\"tc-alert-highlight\">({{$:/language/Count}}: <$view field=\"count\"/>)</span></$reveal>\n</div>\n<div class=\"tc-alert-body\">\n\n<$transclude/>\n\n</div>\n</div>\n"
},
"$:/core/ui/BinaryWarning": {
"title": "$:/core/ui/BinaryWarning",
"text": "\\define lingo-base() $:/language/BinaryWarning/\n<div class=\"tc-binary-warning\">\n\n<<lingo Prompt>>\n\n</div>\n"
},
"$:/core/ui/Components/plugin-info": {
"title": "$:/core/ui/Components/plugin-info",
"text": "\\define lingo-base() $:/language/ControlPanel/Plugins/\n\n\\define popup-state-macro()\n$(qualified-state)$-$(currentTiddler)$\n\\end\n\n\\define tabs-state-macro()\n$(popup-state)$-$(pluginInfoType)$\n\\end\n\n\\define plugin-icon-title()\n$(currentTiddler)$/icon\n\\end\n\n\\define plugin-disable-title()\n$:/config/Plugins/Disabled/$(currentTiddler)$\n\\end\n\n\\define plugin-table-body(type,disabledMessage,default-popup-state)\n<div class=\"tc-plugin-info-chunk tc-small-icon\">\n<$reveal type=\"nomatch\" state=<<popup-state>> text=\"yes\" default=\"\"\"$default-popup-state$\"\"\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<popup-state>> setTo=\"yes\">\n{{$:/core/images/right-arrow}}\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<popup-state>> text=\"yes\" default=\"\"\"$default-popup-state$\"\"\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<popup-state>> setTo=\"no\">\n{{$:/core/images/down-arrow}}\n</$button>\n</$reveal>\n</div>\n<div class=\"tc-plugin-info-chunk\">\n<$transclude tiddler=<<currentTiddler>> subtiddler=<<plugin-icon-title>>>\n<$transclude tiddler=\"$:/core/images/plugin-generic-$type$\"/>\n</$transclude>\n</div>\n<div class=\"tc-plugin-info-chunk\">\n<h1>\n''<$view field=\"description\"><$view field=\"title\"/></$view>'' $disabledMessage$\n</h1>\n<h2>\n<$view field=\"title\"/>\n</h2>\n<h2>\n<div><em><$view field=\"version\"/></em></div>\n</h2>\n</div>\n\\end\n\n\\define plugin-info(type,default-popup-state)\n<$set name=\"popup-state\" value=<<popup-state-macro>>>\n<$reveal type=\"nomatch\" state=<<plugin-disable-title>> text=\"yes\">\n<$link to={{!!title}} class=\"tc-plugin-info\">\n<<plugin-table-body type:\"$type$\" default-popup-state:\"\"\"$default-popup-state$\"\"\">>\n</$link>\n</$reveal>\n<$reveal type=\"match\" state=<<plugin-disable-title>> text=\"yes\">\n<$link to={{!!title}} class=\"tc-plugin-info tc-plugin-info-disabled\">\n<<plugin-table-body type:\"$type$\" default-popup-state:\"\"\"$default-popup-state$\"\"\" disabledMessage:\"<$macrocall $name='lingo' title='Disabled/Status'/>\">>\n</$link>\n</$reveal>\n<$reveal type=\"match\" text=\"yes\" state=<<popup-state>> default=\"\"\"$default-popup-state$\"\"\">\n<div class=\"tc-plugin-info-dropdown\">\n<div class=\"tc-plugin-info-dropdown-body\">\n<$list filter=\"[all[current]] -[[$:/core]]\">\n<div style=\"float:right;\">\n<$reveal type=\"nomatch\" state=<<plugin-disable-title>> text=\"yes\">\n<$button set=<<plugin-disable-title>> setTo=\"yes\" tooltip={{$:/language/ControlPanel/Plugins/Disable/Hint}} aria-label={{$:/language/ControlPanel/Plugins/Disable/Caption}}>\n<<lingo Disable/Caption>>\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<plugin-disable-title>> text=\"yes\">\n<$button set=<<plugin-disable-title>> setTo=\"no\" tooltip={{$:/language/ControlPanel/Plugins/Enable/Hint}} aria-label={{$:/language/ControlPanel/Plugins/Enable/Caption}}>\n<<lingo Enable/Caption>>\n</$button>\n</$reveal>\n</div>\n</$list>\n<$reveal type=\"nomatch\" text=\"\" state=\"!!list\">\n<$set name=\"tabsList\" filter=\"[<currentTiddler>list[]] contents\">\n<$macrocall $name=\"tabs\" state=<<tabs-state-macro>> tabsList=<<tabsList>> default=\"readme\" template=\"$:/core/ui/PluginInfo\"/>\n</$set>\n</$reveal>\n<$reveal type=\"match\" text=\"\" state=\"!!list\">\n<<lingo NoInformation/Hint>>\n</$reveal>\n</div>\n</div>\n</$reveal>\n</$set>\n\\end\n\n<$macrocall $name=\"plugin-info\" type=<<plugin-type>> default-popup-state=<<default-popup-state>>/>\n"
},
"$:/core/ui/Components/tag-link": {
"title": "$:/core/ui/Components/tag-link",
"text": "<$link>\n<$set name=\"backgroundColor\" value={{!!color}}>\n<span style=<<tag-styles>> class=\"tc-tag-label\">\n<$view field=\"title\" format=\"text\"/>\n</span>\n</$set>\n</$link>"
},
"$:/core/ui/ControlPanel/Advanced": {
"title": "$:/core/ui/ControlPanel/Advanced",
"tags": "$:/tags/ControlPanel/Info",
"caption": "{{$:/language/ControlPanel/Advanced/Caption}}",
"text": "{{$:/language/ControlPanel/Advanced/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Advanced]!has[draft.of]]\" \"$:/core/ui/ControlPanel/TiddlerFields\">>\n</div>\n"
},
"$:/core/ui/ControlPanel/Appearance": {
"title": "$:/core/ui/ControlPanel/Appearance",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Appearance/Caption}}",
"text": "{{$:/language/ControlPanel/Appearance/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Appearance]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Theme\">>\n</div>\n"
},
"$:/core/ui/ControlPanel/Basics": {
"title": "$:/core/ui/ControlPanel/Basics",
"tags": "$:/tags/ControlPanel/Info",
"caption": "{{$:/language/ControlPanel/Basics/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Basics/\n\n\\define show-filter-count(filter)\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" $value=\"\"\"$filter$\"\"\"/>\n<$action-setfield $tiddler=\"$:/state/tab--1498284803\" $value=\"$:/core/ui/AdvancedSearch/Filter\"/>\n<$action-navigate $to=\"$:/AdvancedSearch\"/>\n''<$count filter=\"\"\"$filter$\"\"\"/>''\n{{$:/core/images/advanced-search-button}}\n</$button>\n\\end\n\n|<<lingo Version/Prompt>> |''<<version>>'' |\n|<$link to=\"$:/SiteTitle\"><<lingo Title/Prompt>></$link> |<$edit-text tiddler=\"$:/SiteTitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/SiteSubtitle\"><<lingo Subtitle/Prompt>></$link> |<$edit-text tiddler=\"$:/SiteSubtitle\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/status/UserName\"><<lingo Username/Prompt>></$link> |<$edit-text tiddler=\"$:/status/UserName\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/config/AnimationDuration\"><<lingo AnimDuration/Prompt>></$link> |<$edit-text tiddler=\"$:/config/AnimationDuration\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/DefaultTiddlers\"><<lingo DefaultTiddlers/Prompt>></$link> |<<lingo DefaultTiddlers/TopHint>><br> <$edit tag=\"textarea\" tiddler=\"$:/DefaultTiddlers\" class=\"tc-edit-texteditor\"/><br>//<<lingo DefaultTiddlers/BottomHint>>// |\n|<$link to=\"$:/config/NewJournal/Title\"><<lingo NewJournal/Title/Prompt>></$link> |<$edit-text tiddler=\"$:/config/NewJournal/Title\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/config/NewJournal/Text\"><<lingo NewJournal/Text/Prompt>></$link> |<$edit tiddler=\"$:/config/NewJournal/Text\" tag=\"textarea\" class=\"tc-edit-texteditor\" default=\"\"/> |\n|<$link to=\"$:/config/NewJournal/Tags\"><<lingo NewJournal/Tags/Prompt>></$link> |<$edit-text tiddler=\"$:/config/NewJournal/Tags\" default=\"\" tag=\"input\"/> |\n|<<lingo Language/Prompt>> |{{$:/snippets/minilanguageswitcher}} |\n|<<lingo Tiddlers/Prompt>> |<<show-filter-count \"[!is[system]sort[title]]\">> |\n|<<lingo Tags/Prompt>> |<<show-filter-count \"[tags[]sort[title]]\">> |\n|<<lingo SystemTiddlers/Prompt>> |<<show-filter-count \"[is[system]sort[title]]\">> |\n|<<lingo ShadowTiddlers/Prompt>> |<<show-filter-count \"[all[shadows]sort[title]]\">> |\n|<<lingo OverriddenShadowTiddlers/Prompt>> |<<show-filter-count \"[is[tiddler]is[shadow]sort[title]]\">> |\n"
},
"$:/core/ui/ControlPanel/EditorTypes": {
"title": "$:/core/ui/ControlPanel/EditorTypes",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/EditorTypes/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/EditorTypes/\n\n<<lingo Hint>>\n\n<table>\n<tbody>\n<tr>\n<th><<lingo Type/Caption>></th>\n<th><<lingo Editor/Caption>></th>\n</tr>\n<$list filter=\"[all[shadows+tiddlers]prefix[$:/config/EditorTypeMappings/]sort[title]]\">\n<tr>\n<td>\n<$link>\n<$list filter=\"[all[current]removeprefix[$:/config/EditorTypeMappings/]]\">\n<$text text={{!!title}}/>\n</$list>\n</$link>\n</td>\n<td>\n<$view field=\"text\"/>\n</td>\n</tr>\n</$list>\n</tbody>\n</table>\n"
},
"$:/core/ui/ControlPanel/Info": {
"title": "$:/core/ui/ControlPanel/Info",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Info/Caption}}",
"text": "{{$:/language/ControlPanel/Info/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Info]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Basics\">>\n</div>\n"
},
"$:/core/ui/ControlPanel/KeyboardShortcuts": {
"title": "$:/core/ui/ControlPanel/KeyboardShortcuts",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/KeyboardShortcuts/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/KeyboardShortcuts/\n\n\\define new-shortcut(title)\n<div class=\"tc-dropdown-item-plain\">\n<$edit-shortcut tiddler=\"$title$\" placeholder={{$:/language/ControlPanel/KeyboardShortcuts/Add/Prompt}} style=\"width:auto;\"/> <$button>\n<<lingo Add/Caption>>\n<$action-listops\n\t$tiddler=\"$(shortcutTitle)$\"\n\t$field=\"text\"\n\t$subfilter=\"[{$title$}]\"\n/>\n<$action-deletetiddler\n\t$tiddler=\"$title$\"\n/>\n</$button>\n</div>\n\\end\n\n\\define shortcut-list-item(caption)\n<td>\n</td>\n<td style=\"text-align:right;font-size:0.7em;\">\n<<lingo Platform/$caption$>>\n</td>\n<td>\n<div style=\"position:relative;\">\n<$button popup=<<qualify \"$:/state/dropdown/$(shortcutTitle)$\">> class=\"tc-btn-invisible\">\n{{$:/core/images/edit-button}}\n</$button>\n<$macrocall $name=\"displayshortcuts\" $output=\"text/html\" shortcuts={{$(shortcutTitle)$}} prefix=\"<kbd>\" separator=\"</kbd> <kbd>\" suffix=\"</kbd>\"/>\n\n<$reveal state=<<qualify \"$:/state/dropdown/$(shortcutTitle)$\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-block-dropdown-wrapper\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown tc-popup-keep\">\n<$list filter=\"[list[$(shortcutTitle)$!!text]sort[title]]\" variable=\"shortcut\" emptyMessage=\"\"\"\n<div class=\"tc-dropdown-item-plain\">\n//<<lingo NoShortcuts/Caption>>//\n</div>\n\"\"\">\n<div class=\"tc-dropdown-item-plain\">\n<$button class=\"tc-btn-invisible\" tooltip=<<lingo Remove/Hint>>>\n<$action-listops\n\t$tiddler=\"$(shortcutTitle)$\"\n\t$field=\"text\"\n\t$subfilter=\"+[remove<shortcut>]\"\n/>\n×\n</$button>\n<kbd>\n<$macrocall $name=\"displayshortcuts\" $output=\"text/html\" shortcuts=<<shortcut>>/>\n</kbd>\n</div>\n</$list>\n<hr/>\n<$macrocall $name=\"new-shortcut\" title=<<qualify \"$:/state/new-shortcut/$(shortcutTitle)$\">>/>\n</div>\n</div>\n</$reveal>\n</div>\n</td>\n\\end\n\n\\define shortcut-list(caption,prefix)\n<tr>\n<$list filter=\"[all[tiddlers+shadows][$prefix$$(shortcutName)$]]\" variable=\"shortcutTitle\">\n<<shortcut-list-item \"$caption$\">>\n</$list>\n</tr>\n\\end\n\n\\define shortcut-editor()\n<<shortcut-list \"All\" \"$:/config/shortcuts/\">>\n<<shortcut-list \"Mac\" \"$:/config/shortcuts-mac/\">>\n<<shortcut-list \"NonMac\" \"$:/config/shortcuts-not-mac/\">>\n<<shortcut-list \"Linux\" \"$:/config/shortcuts-linux/\">>\n<<shortcut-list \"NonLinux\" \"$:/config/shortcuts-not-linux/\">>\n<<shortcut-list \"Windows\" \"$:/config/shortcuts-windows/\">>\n<<shortcut-list \"NonWindows\" \"$:/config/shortcuts-not-windows/\">>\n\\end\n\n\\define shortcut-preview()\n<$macrocall $name=\"displayshortcuts\" $output=\"text/html\" shortcuts={{$(shortcutPrefix)$$(shortcutName)$}} prefix=\"<kbd>\" separator=\"</kbd> <kbd>\" suffix=\"</kbd>\"/>\n\\end\n\n\\define shortcut-item-inner()\n<tr>\n<td>\n<$reveal type=\"nomatch\" state=<<dropdownStateTitle>> text=\"open\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield\n\t$tiddler=<<dropdownStateTitle>>\n\t$value=\"open\"\n/>\n{{$:/core/images/right-arrow}}\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<dropdownStateTitle>> text=\"open\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield\n\t$tiddler=<<dropdownStateTitle>>\n\t$value=\"close\"\n/>\n{{$:/core/images/down-arrow}}\n</$button>\n</$reveal>\n''<$text text=<<shortcutName>>/>''\n</td>\n<td>\n<$transclude tiddler=\"$:/config/ShortcutInfo/$(shortcutName)$\"/>\n</td>\n<td>\n<$list filter=\"$:/config/shortcuts/ $:/config/shortcuts-mac/ $:/config/shortcuts-not-mac/ $:/config/shortcuts-linux/ $:/config/shortcuts-not-linux/ $:/config/shortcuts-windows/ $:/config/shortcuts-not-windows/\" variable=\"shortcutPrefix\">\n<<shortcut-preview>>\n</$list>\n</td>\n</tr>\n<$set name=\"dropdownState\" value={{$(dropdownStateTitle)$}}>\n<$list filter=\"[<dropdownState>prefix[open]]\" variable=\"listItem\">\n<<shortcut-editor>>\n</$list>\n</$set>\n\\end\n\n\\define shortcut-item()\n<$set name=\"dropdownStateTitle\" value=<<qualify \"$:/state/dropdown/keyboardshortcut/$(shortcutName)$\">>>\n<<shortcut-item-inner>>\n</$set>\n\\end\n\n<table>\n<tbody>\n<$list filter=\"[all[shadows+tiddlers]removeprefix[$:/config/ShortcutInfo/]]\" variable=\"shortcutName\">\n<<shortcut-item>>\n</$list>\n</tbody>\n</table>\n"
},
"$:/core/ui/ControlPanel/LoadedModules": {
"title": "$:/core/ui/ControlPanel/LoadedModules",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/LoadedModules/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/\n<<lingo LoadedModules/Hint>>\n\n{{$:/snippets/modules}}\n"
},
"$:/core/ui/ControlPanel/Modals/AddPlugins": {
"title": "$:/core/ui/ControlPanel/Modals/AddPlugins",
"subtitle": "{{$:/core/images/download-button}} {{$:/language/ControlPanel/Plugins/Add/Caption}}",
"text": "\\define install-plugin-button()\n<$button>\n<$action-sendmessage $message=\"tm-load-plugin-from-library\" url={{!!url}} title={{$(assetInfo)$!!original-title}}/>\n<$list filter=\"[<assetInfo>get[original-title]get[version]]\" variable=\"installedVersion\" emptyMessage=\"\"\"{{$:/language/ControlPanel/Plugins/Install/Caption}}\"\"\">\n{{$:/language/ControlPanel/Plugins/Reinstall/Caption}}\n</$list>\n</$button>\n\\end\n\n\\define popup-state-macro()\n$:/state/add-plugin-info/$(connectionTiddler)$/$(assetInfo)$\n\\end\n\n\\define display-plugin-info(type)\n<$set name=\"popup-state\" value=<<popup-state-macro>>>\n<div class=\"tc-plugin-info\">\n<div class=\"tc-plugin-info-chunk tc-small-icon\">\n<$reveal type=\"nomatch\" state=<<popup-state>> text=\"yes\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<popup-state>> setTo=\"yes\">\n{{$:/core/images/right-arrow}}\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<popup-state>> text=\"yes\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<popup-state>> setTo=\"no\">\n{{$:/core/images/down-arrow}}\n</$button>\n</$reveal>\n</div>\n<div class=\"tc-plugin-info-chunk\">\n<$list filter=\"[<assetInfo>has[icon]]\" emptyMessage=\"\"\"<$transclude tiddler=\"$:/core/images/plugin-generic-$type$\"/>\"\"\">\n<img src={{$(assetInfo)$!!icon}}/>\n</$list>\n</div>\n<div class=\"tc-plugin-info-chunk\">\n<h1><$view tiddler=<<assetInfo>> field=\"description\"/></h1>\n<h2><$view tiddler=<<assetInfo>> field=\"original-title\"/></h2>\n<div><em><$view tiddler=<<assetInfo>> field=\"version\"/></em></div>\n</div>\n<div class=\"tc-plugin-info-chunk\">\n<<install-plugin-button>>\n</div>\n</div>\n<$reveal type=\"match\" text=\"yes\" state=<<popup-state>>>\n<div class=\"tc-plugin-info-dropdown\">\n<div class=\"tc-plugin-info-dropdown-message\">\n<$list filter=\"[<assetInfo>get[original-title]get[version]]\" variable=\"installedVersion\" emptyMessage=\"\"\"{{$:/language/ControlPanel/Plugins/NotInstalled/Hint}}\"\"\">\n<em>\n{{$:/language/ControlPanel/Plugins/AlreadyInstalled/Hint}}\n</em>\n</$list>\n</div>\n<div class=\"tc-plugin-info-dropdown-body\">\n<$transclude tiddler=<<assetInfo>> field=\"readme\" mode=\"block\"/>\n</div>\n</div>\n</$reveal>\n</$set>\n\\end\n\n\\define load-plugin-library-button()\n<$button class=\"tc-btn-big-green\">\n<$action-sendmessage $message=\"tm-load-plugin-library\" url={{!!url}} infoTitlePrefix=\"$:/temp/RemoteAssetInfo/\"/>\n{{$:/core/images/chevron-right}} {{$:/language/ControlPanel/Plugins/OpenPluginLibrary}}\n</$button>\n\\end\n\n\\define display-server-assets(type)\n{{$:/language/Search/Search}}: <$edit-text tiddler=\"\"\"$:/temp/RemoteAssetSearch/$(currentTiddler)$\"\"\" default=\"\" type=\"search\" tag=\"input\"/>\n<$reveal state=\"\"\"$:/temp/RemoteAssetSearch/$(currentTiddler)$\"\"\" type=\"nomatch\" text=\"\">\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"\"\"$:/temp/RemoteAssetSearch/$(currentTiddler)$\"\"\" $field=\"text\" $value=\"\"/>\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n<div class=\"tc-plugin-library-listing\">\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[$type$]search{$:/temp/RemoteAssetSearch/$(currentTiddler)$}sort[description]]\" variable=\"assetInfo\">\n<<display-plugin-info \"$type$\">>\n</$list>\n</div>\n\\end\n\n\\define display-server-connection()\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/ServerConnection]suffix{!!url}]\" variable=\"connectionTiddler\" emptyMessage=<<load-plugin-library-button>>>\n\n<<tabs \"[[$:/core/ui/ControlPanel/Plugins/Add/Plugins]] [[$:/core/ui/ControlPanel/Plugins/Add/Themes]] [[$:/core/ui/ControlPanel/Plugins/Add/Languages]]\" \"$:/core/ui/ControlPanel/Plugins/Add/Plugins\">>\n\n</$list>\n\\end\n\n\\define close-library-button()\n<$reveal type='nomatch' state='$:/temp/ServerConnection/$(PluginLibraryURL)$' text=''>\n<$button class='tc-btn-big-green'>\n<$action-sendmessage $message=\"tm-unload-plugin-library\" url={{!!url}}/>\n{{$:/core/images/chevron-left}} {{$:/language/ControlPanel/Plugins/ClosePluginLibrary}}\n<$action-deletetiddler $filter=\"[prefix[$:/temp/ServerConnection/$(PluginLibraryURL)$]][prefix[$:/temp/RemoteAssetInfo/$(PluginLibraryURL)$]]\"/>\n</$button>\n</$reveal>\n\\end\n\n\\define plugin-library-listing()\n<$list filter=\"[all[tiddlers+shadows]tag[$:/tags/PluginLibrary]]\">\n<div class=\"tc-plugin-library\">\n\n!! <$link><$transclude field=\"caption\"><$view field=\"title\"/></$transclude></$link>\n\n//<$view field=\"url\"/>//\n\n<$transclude/>\n\n<$set name=PluginLibraryURL value={{!!url}}>\n<<close-library-button>>\n</$set>\n\n<<display-server-connection>>\n</div>\n</$list>\n\\end\n\n<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\">\n\n<div>\n<<plugin-library-listing>>\n</div>\n\n</$importvariables>\n"
},
"$:/core/ui/ControlPanel/Palette": {
"title": "$:/core/ui/ControlPanel/Palette",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ControlPanel/Palette/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Palette/\n\n{{$:/snippets/paletteswitcher}}\n\n<$reveal type=\"nomatch\" state=\"$:/state/ShowPaletteEditor\" text=\"yes\">\n\n<$button set=\"$:/state/ShowPaletteEditor\" setTo=\"yes\"><<lingo ShowEditor/Caption>></$button>\n\n</$reveal>\n\n<$reveal type=\"match\" state=\"$:/state/ShowPaletteEditor\" text=\"yes\">\n\n<$button set=\"$:/state/ShowPaletteEditor\" setTo=\"no\"><<lingo HideEditor/Caption>></$button>\n{{$:/snippets/paletteeditor}}\n\n</$reveal>\n\n"
},
"$:/core/ui/ControlPanel/Parsing": {
"title": "$:/core/ui/ControlPanel/Parsing",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/Parsing/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Parsing/\n\n\\define toggle(Type)\n<$checkbox\ntiddler=\"\"\"$:/config/WikiParserRules/$Type$/$(rule)$\"\"\"\nfield=\"text\"\nchecked=\"enable\"\nunchecked=\"disable\"\ndefault=\"enable\">\n<<rule>>\n</$checkbox>\n\\end\n\n\\define rules(type,Type)\n<$list filter=\"[wikiparserrules[$type$]]\" variable=\"rule\">\n<dd><<toggle $Type$>></dd>\n</$list>\n\\end\n\n<<lingo Hint>>\n\n<dl>\n<dt><<lingo Pragma/Caption>></dt>\n<<rules pragma Pragma>>\n<dt><<lingo Inline/Caption>></dt>\n<<rules inline Inline>>\n<dt><<lingo Block/Caption>></dt>\n<<rules block Block>>\n</dl>"
},
"$:/core/ui/ControlPanel/Plugins/Add/Languages": {
"title": "$:/core/ui/ControlPanel/Plugins/Add/Languages",
"caption": "{{$:/language/ControlPanel/Plugins/Languages/Caption}} (<$count filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[language]]\"/>)",
"text": "<<display-server-assets language>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Add/Plugins": {
"title": "$:/core/ui/ControlPanel/Plugins/Add/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Plugins/Caption}} (<$count filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[plugin]]\"/>)",
"text": "<<display-server-assets plugin>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Add/Themes": {
"title": "$:/core/ui/ControlPanel/Plugins/Add/Themes",
"caption": "{{$:/language/ControlPanel/Plugins/Themes/Caption}} (<$count filter=\"[all[tiddlers+shadows]tag[$:/tags/RemoteAssetInfo]server-url{!!url}original-plugin-type[theme]]\"/>)",
"text": "<<display-server-assets theme>>\n"
},
"$:/core/ui/ControlPanel/Plugins/AddPlugins": {
"title": "$:/core/ui/ControlPanel/Plugins/AddPlugins",
"text": "\\define lingo-base() $:/language/ControlPanel/Plugins/\n\n<$button message=\"tm-modal\" param=\"$:/core/ui/ControlPanel/Modals/AddPlugins\" tooltip={{$:/language/ControlPanel/Plugins/Add/Hint}} class=\"tc-btn-big-green\" style=\"background:blue;\">\n{{$:/core/images/download-button}} <<lingo Add/Caption>>\n</$button>\n"
},
"$:/core/ui/ControlPanel/Plugins/Installed/Languages": {
"title": "$:/core/ui/ControlPanel/Plugins/Installed/Languages",
"caption": "{{$:/language/ControlPanel/Plugins/Languages/Caption}} (<$count filter=\"[!has[draft.of]plugin-type[language]]\"/>)",
"text": "<<plugin-table language>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Installed/Plugins": {
"title": "$:/core/ui/ControlPanel/Plugins/Installed/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Plugins/Caption}} (<$count filter=\"[!has[draft.of]plugin-type[plugin]]\"/>)",
"text": "<<plugin-table plugin>>\n"
},
"$:/core/ui/ControlPanel/Plugins/Installed/Themes": {
"title": "$:/core/ui/ControlPanel/Plugins/Installed/Themes",
"caption": "{{$:/language/ControlPanel/Plugins/Themes/Caption}} (<$count filter=\"[!has[draft.of]plugin-type[theme]]\"/>)",
"text": "<<plugin-table theme>>\n"
},
"$:/core/ui/ControlPanel/Plugins": {
"title": "$:/core/ui/ControlPanel/Plugins",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Plugins/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Plugins/\n\n\\define plugin-table(type)\n<$set name=\"plugin-type\" value=\"\"\"$type$\"\"\">\n<$set name=\"qualified-state\" value=<<qualify \"$:/state/plugin-info\">>>\n<$list filter=\"[!has[draft.of]plugin-type[$type$]sort[description]]\" emptyMessage=<<lingo \"Empty/Hint\">> template=\"$:/core/ui/Components/plugin-info\"/>\n</$set>\n</$set>\n\\end\n\n{{$:/core/ui/ControlPanel/Plugins/AddPlugins}}\n\n<<lingo Installed/Hint>>\n\n<<tabs \"[[$:/core/ui/ControlPanel/Plugins/Installed/Plugins]] [[$:/core/ui/ControlPanel/Plugins/Installed/Themes]] [[$:/core/ui/ControlPanel/Plugins/Installed/Languages]]\" \"$:/core/ui/ControlPanel/Plugins/Installed/Plugins\">>\n"
},
"$:/core/ui/ControlPanel/Saving/DownloadSaver": {
"title": "$:/core/ui/ControlPanel/Saving/DownloadSaver",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/DownloadSaver/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Saving/DownloadSaver/\n\n<<lingo Hint>>\n\n!! <$link to=\"$:/config/DownloadSaver/AutoSave\"><<lingo AutoSave/Hint>></$link>\n\n<$checkbox tiddler=\"$:/config/DownloadSaver/AutoSave\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"no\"> <<lingo AutoSave/Description>> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Saving/General": {
"title": "$:/core/ui/ControlPanel/Saving/General",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/General/Caption}}",
"list-before": "",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/\n\n{{$:/language/ControlPanel/Saving/General/Hint}}\n\n!! <$link to=\"$:/config/AutoSave\"><<lingo AutoSave/Caption>></$link>\n\n<<lingo AutoSave/Hint>>\n\n<$radio tiddler=\"$:/config/AutoSave\" value=\"yes\"> <<lingo AutoSave/Enabled/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/AutoSave\" value=\"no\"> <<lingo AutoSave/Disabled/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Saving/TiddlySpot": {
"title": "$:/core/ui/ControlPanel/Saving/TiddlySpot",
"tags": "$:/tags/ControlPanel/Saving",
"caption": "{{$:/language/ControlPanel/Saving/TiddlySpot/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Saving/TiddlySpot/\n\n\\define backupURL()\nhttp://$(userName)$.tiddlyspot.com/backup/\n\\end\n\\define backupLink()\n<$reveal type=\"nomatch\" state=\"$:/UploadName\" text=\"\">\n<$set name=\"userName\" value={{$:/UploadName}}>\n<$reveal type=\"match\" state=\"$:/UploadURL\" text=\"\">\n<<backupURL>>\n</$reveal>\n<$reveal type=\"nomatch\" state=\"$:/UploadURL\" text=\"\">\n<$macrocall $name=resolvePath source={{$:/UploadBackupDir}} root={{$:/UploadURL}}>>\n</$reveal>\n</$set>\n</$reveal>\n\\end\n\n<<lingo Description>>\n\n|<<lingo UserName>> |<$edit-text tiddler=\"$:/UploadName\" default=\"\" tag=\"input\"/> |\n|<<lingo Password>> |<$password name=\"upload\"/> |\n|<<lingo Backups>> |<<backupLink>> |\n\n''<<lingo Advanced/Heading>>''\n\n|<<lingo ServerURL>> |<$edit-text tiddler=\"$:/UploadURL\" default=\"\" tag=\"input\"/> |\n|<<lingo Filename>> |<$edit-text tiddler=\"$:/UploadFilename\" default=\"index.html\" tag=\"input\"/> |\n|<<lingo UploadDir>> |<$edit-text tiddler=\"$:/UploadDir\" default=\".\" tag=\"input\"/> |\n|<<lingo BackupDir>> |<$edit-text tiddler=\"$:/UploadBackupDir\" default=\".\" tag=\"input\"/> |\n\n<<lingo TiddlySpot/Hint>>"
},
"$:/core/ui/ControlPanel/Saving": {
"title": "$:/core/ui/ControlPanel/Saving",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Saving/Caption}}",
"text": "{{$:/language/ControlPanel/Saving/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Saving]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Saving/General\">>\n</div>\n"
},
"$:/core/buttonstyles/Borderless": {
"title": "$:/core/buttonstyles/Borderless",
"tags": "$:/tags/ToolbarButtonStyle",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Borderless}}",
"text": "tc-btn-invisible"
},
"$:/core/buttonstyles/Boxed": {
"title": "$:/core/buttonstyles/Boxed",
"tags": "$:/tags/ToolbarButtonStyle",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Boxed}}",
"text": "tc-btn-boxed"
},
"$:/core/buttonstyles/Rounded": {
"title": "$:/core/buttonstyles/Rounded",
"tags": "$:/tags/ToolbarButtonStyle",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtonStyle/Styles/Rounded}}",
"text": "tc-btn-rounded"
},
"$:/core/ui/ControlPanel/Settings/CamelCase": {
"title": "$:/core/ui/ControlPanel/Settings/CamelCase",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/CamelCase/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/CamelCase/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/WikiParserRules/Inline/wikilink\" field=\"text\" checked=\"enable\" unchecked=\"disable\" default=\"enable\"> <$link to=\"$:/config/WikiParserRules/Inline/wikilink\"><<lingo Description>></$link> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Settings/DefaultSidebarTab": {
"caption": "{{$:/language/ControlPanel/Settings/DefaultSidebarTab/Caption}}",
"tags": "$:/tags/ControlPanel/Settings",
"title": "$:/core/ui/ControlPanel/Settings/DefaultSidebarTab",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/DefaultSidebarTab/\n\n<$link to=\"$:/config/DefaultSidebarTab\"><<lingo Hint>></$link>\n\n<$select tiddler=\"$:/config/DefaultSidebarTab\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SideBar]!has[draft.of]]\">\n<option value=<<currentTiddler>>><$transclude field=\"caption\"><$text text=<<currentTiddler>>/></$transclude></option>\n</$list>\n</$select>\n"
},
"$:/core/ui/ControlPanel/Settings/EditorToolbar": {
"title": "$:/core/ui/ControlPanel/Settings/EditorToolbar",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/EditorToolbar/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/EditorToolbar/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/TextEditor/EnableToolbar\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"yes\"> <$link to=\"$:/config/TextEditor/EnableToolbar\"><<lingo Description>></$link> </$checkbox>\n\n"
},
"$:/core/ui/ControlPanel/Settings/InfoPanelMode": {
"title": "$:/core/ui/ControlPanel/Settings/InfoPanelMode",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/InfoPanelMode/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/InfoPanelMode/\n<$link to=\"$:/config/TiddlerInfo/Mode\"><<lingo Hint>></$link>\n\n<$radio tiddler=\"$:/config/TiddlerInfo/Mode\" value=\"popup\"> <<lingo Popup/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/TiddlerInfo/Mode\" value=\"sticky\"> <<lingo Sticky/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Settings/LinkToBehaviour": {
"title": "$:/core/ui/ControlPanel/Settings/LinkToBehaviour",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/LinkToBehaviour/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/LinkToBehaviour/\n\n<$link to=\"$:/config/Navigation/openLinkFromInsideRiver\"><<lingo \"InsideRiver/Hint\">></$link>\n\n<$select tiddler=\"$:/config/Navigation/openLinkFromInsideRiver\">\n <option value=\"above\"><<lingo \"OpenAbove\">></option>\n <option value=\"below\"><<lingo \"OpenBelow\">></option>\n <option value=\"top\"><<lingo \"OpenAtTop\">></option>\n <option value=\"bottom\"><<lingo \"OpenAtBottom\">></option>\n</$select>\n\n<$link to=\"$:/config/Navigation/openLinkFromOutsideRiver\"><<lingo \"OutsideRiver/Hint\">></$link>\n\n<$select tiddler=\"$:/config/Navigation/openLinkFromOutsideRiver\">\n <option value=\"top\"><<lingo \"OpenAtTop\">></option>\n <option value=\"bottom\"><<lingo \"OpenAtBottom\">></option>\n</$select>\n"
},
"$:/core/ui/ControlPanel/Settings/MissingLinks": {
"title": "$:/core/ui/ControlPanel/Settings/MissingLinks",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/MissingLinks/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/MissingLinks/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/MissingLinks\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"yes\"> <$link to=\"$:/config/MissingLinks\"><<lingo Description>></$link> </$checkbox>\n\n"
},
"$:/core/ui/ControlPanel/Settings/NavigationAddressBar": {
"title": "$:/core/ui/ControlPanel/Settings/NavigationAddressBar",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/NavigationAddressBar/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/NavigationAddressBar/\n\n<$link to=\"$:/config/Navigation/UpdateAddressBar\"><<lingo Hint>></$link>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateAddressBar\" value=\"permaview\"> <<lingo Permaview/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateAddressBar\" value=\"permalink\"> <<lingo Permalink/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateAddressBar\" value=\"no\"> <<lingo No/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Settings/NavigationHistory": {
"title": "$:/core/ui/ControlPanel/Settings/NavigationHistory",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/NavigationHistory/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/NavigationHistory/\n<$link to=\"$:/config/Navigation/UpdateHistory\"><<lingo Hint>></$link>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateHistory\" value=\"yes\"> <<lingo Yes/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/Navigation/UpdateHistory\" value=\"no\"> <<lingo No/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Settings/PerformanceInstrumentation": {
"title": "$:/core/ui/ControlPanel/Settings/PerformanceInstrumentation",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/PerformanceInstrumentation/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/PerformanceInstrumentation/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/Performance/Instrumentation\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"no\"> <$link to=\"$:/config/Performance/Instrumentation\"><<lingo Description>></$link> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Settings/TitleLinks": {
"title": "$:/core/ui/ControlPanel/Settings/TitleLinks",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/TitleLinks/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/TitleLinks/\n<$link to=\"$:/config/Tiddlers/TitleLinks\"><<lingo Hint>></$link>\n\n<$radio tiddler=\"$:/config/Tiddlers/TitleLinks\" value=\"yes\"> <<lingo Yes/Description>> </$radio>\n\n<$radio tiddler=\"$:/config/Tiddlers/TitleLinks\" value=\"no\"> <<lingo No/Description>> </$radio>\n"
},
"$:/core/ui/ControlPanel/Settings/ToolbarButtonStyle": {
"title": "$:/core/ui/ControlPanel/Settings/ToolbarButtonStyle",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtonStyle/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/ToolbarButtonStyle/\n<$link to=\"$:/config/Toolbar/ButtonClass\"><<lingo \"Hint\">></$link>\n\n<$select tiddler=\"$:/config/Toolbar/ButtonClass\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ToolbarButtonStyle]]\">\n<option value={{!!text}}>{{!!caption}}</option>\n</$list>\n</$select>\n"
},
"$:/core/ui/ControlPanel/Settings/ToolbarButtons": {
"title": "$:/core/ui/ControlPanel/Settings/ToolbarButtons",
"tags": "$:/tags/ControlPanel/Settings",
"caption": "{{$:/language/ControlPanel/Settings/ToolbarButtons/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/ToolbarButtons/\n<<lingo Hint>>\n\n<$checkbox tiddler=\"$:/config/Toolbar/Icons\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"yes\"> <$link to=\"$:/config/Toolbar/Icons\"><<lingo Icons/Description>></$link> </$checkbox>\n\n<$checkbox tiddler=\"$:/config/Toolbar/Text\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"no\"> <$link to=\"$:/config/Toolbar/Text\"><<lingo Text/Description>></$link> </$checkbox>\n"
},
"$:/core/ui/ControlPanel/Settings": {
"title": "$:/core/ui/ControlPanel/Settings",
"tags": "$:/tags/ControlPanel",
"caption": "{{$:/language/ControlPanel/Settings/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/Settings/\n\n<<lingo Hint>>\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Settings]]\">\n\n<div style=\"border-top:1px solid #eee;\">\n\n!! <$link><$transclude field=\"caption\"/></$link>\n\n<$transclude/>\n\n</div>\n\n</$list>\n"
},
"$:/core/ui/ControlPanel/StoryView": {
"title": "$:/core/ui/ControlPanel/StoryView",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ControlPanel/StoryView/Caption}}",
"text": "{{$:/snippets/viewswitcher}}\n"
},
"$:/core/ui/ControlPanel/Theme": {
"title": "$:/core/ui/ControlPanel/Theme",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ControlPanel/Theme/Caption}}",
"text": "{{$:/snippets/themeswitcher}}\n"
},
"$:/core/ui/ControlPanel/TiddlerFields": {
"title": "$:/core/ui/ControlPanel/TiddlerFields",
"tags": "$:/tags/ControlPanel/Advanced",
"caption": "{{$:/language/ControlPanel/TiddlerFields/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/\n\n<<lingo TiddlerFields/Hint>>\n\n{{$:/snippets/allfields}}"
},
"$:/core/ui/ControlPanel/Toolbars/EditToolbar": {
"title": "$:/core/ui/ControlPanel/Toolbars/EditToolbar",
"tags": "$:/tags/ControlPanel/Toolbars",
"caption": "{{$:/language/ControlPanel/Toolbars/EditToolbar/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\n\\define config-base() $:/config/EditToolbarButtons/Visibility/\n\n{{$:/language/ControlPanel/Toolbars/EditToolbar/Hint}}\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$macrocall $name=\"list-tagged-draggable\" tag=\"$:/tags/EditToolbar\" itemTemplate=\"$:/core/ui/ControlPanel/Toolbars/ItemTemplate\"/>\n\n</$set>\n\n</$set>"
},
"$:/core/ui/ControlPanel/Toolbars/EditorItemTemplate": {
"title": "$:/core/ui/ControlPanel/Toolbars/EditorItemTemplate",
"text": "\\define config-title()\n$(config-base)$$(currentTiddler)$\n\\end\n\n<$draggable tiddler=<<currentTiddler>>>\n<$checkbox tiddler=<<config-title>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"show\"/> <span class=\"tc-icon-wrapper\"><$transclude tiddler={{!!icon}}/></span> <$transclude field=\"caption\"/> -- <i class=\"tc-muted\"><$transclude field=\"description\"/></i>\n</$draggable>\n"
},
"$:/core/ui/ControlPanel/Toolbars/EditorToolbar": {
"title": "$:/core/ui/ControlPanel/Toolbars/EditorToolbar",
"tags": "$:/tags/ControlPanel/Toolbars",
"caption": "{{$:/language/ControlPanel/Toolbars/EditorToolbar/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\n\\define config-base() $:/config/EditorToolbarButtons/Visibility/\n\n{{$:/language/ControlPanel/Toolbars/EditorToolbar/Hint}}\n\n<$macrocall $name=\"list-tagged-draggable\" tag=\"$:/tags/EditorToolbar\" itemTemplate=\"$:/core/ui/ControlPanel/Toolbars/EditorItemTemplate\"/>\n"
},
"$:/core/ui/ControlPanel/Toolbars/ItemTemplate": {
"title": "$:/core/ui/ControlPanel/Toolbars/ItemTemplate",
"text": "\\define config-title()\n$(config-base)$$(currentTiddler)$\n\\end\n\n<$draggable tiddler=<<currentTiddler>>>\n<$checkbox tiddler=<<config-title>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"show\"/> <span class=\"tc-icon-wrapper\"> <$transclude field=\"caption\"/> <i class=\"tc-muted\">-- <$transclude field=\"description\"/></i></span>\n</$draggable>\n"
},
"$:/core/ui/ControlPanel/Toolbars/PageControls": {
"title": "$:/core/ui/ControlPanel/Toolbars/PageControls",
"tags": "$:/tags/ControlPanel/Toolbars",
"caption": "{{$:/language/ControlPanel/Toolbars/PageControls/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\n\\define config-base() $:/config/PageControlButtons/Visibility/\n\n{{$:/language/ControlPanel/Toolbars/PageControls/Hint}}\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$macrocall $name=\"list-tagged-draggable\" tag=\"$:/tags/PageControls\" itemTemplate=\"$:/core/ui/ControlPanel/Toolbars/ItemTemplate\"/>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/ControlPanel/Toolbars/ViewToolbar": {
"title": "$:/core/ui/ControlPanel/Toolbars/ViewToolbar",
"tags": "$:/tags/ControlPanel/Toolbars",
"caption": "{{$:/language/ControlPanel/Toolbars/ViewToolbar/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\n\\define config-base() $:/config/ViewToolbarButtons/Visibility/\n\n{{$:/language/ControlPanel/Toolbars/ViewToolbar/Hint}}\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$macrocall $name=\"list-tagged-draggable\" tag=\"$:/tags/ViewToolbar\" itemTemplate=\"$:/core/ui/ControlPanel/Toolbars/ItemTemplate\"/>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/ControlPanel/Toolbars": {
"title": "$:/core/ui/ControlPanel/Toolbars",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ControlPanel/Toolbars/Caption}}",
"text": "{{$:/language/ControlPanel/Toolbars/Hint}}\n\n<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel/Toolbars]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Toolbars/ViewToolbar\" \"$:/state/tabs/controlpanel/toolbars\" \"tc-vertical\">>\n</div>\n"
},
"$:/ControlPanel": {
"title": "$:/ControlPanel",
"icon": "$:/core/images/options-button",
"color": "#bbb",
"text": "<div class=\"tc-control-panel\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/ControlPanel]!has[draft.of]]\" \"$:/core/ui/ControlPanel/Info\">>\n</div>\n"
},
"$:/core/ui/DefaultSearchResultList": {
"title": "$:/core/ui/DefaultSearchResultList",
"tags": "$:/tags/SearchResults",
"caption": "{{$:/language/Search/DefaultResults/Caption}}",
"text": "\\define searchResultList()\n//<small>{{$:/language/Search/Matches/Title}}</small>//\n\n<$list filter=\"[!is[system]search:title{$(searchTiddler)$}sort[title]limit[250]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n\n//<small>{{$:/language/Search/Matches/All}}</small>//\n\n<$list filter=\"[!is[system]search{$(searchTiddler)$}sort[title]limit[250]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n\n\\end\n<<searchResultList>>\n"
},
"$:/core/ui/EditTemplate/body/preview/output": {
"title": "$:/core/ui/EditTemplate/body/preview/output",
"tags": "$:/tags/EditPreview",
"caption": "{{$:/language/EditTemplate/Body/Preview/Type/Output}}",
"text": "<$set name=\"tv-tiddler-preview\" value=\"yes\">\n\n<$transclude />\n\n</$set>\n"
},
"$:/core/ui/EditTemplate/body/editor": {
"title": "$:/core/ui/EditTemplate/body/editor",
"text": "<$edit\n\n field=\"text\"\n class=\"tc-edit-texteditor\"\n placeholder={{$:/language/EditTemplate/Body/Placeholder}}\n\n><$set\n\n name=\"targetTiddler\"\n value=<<currentTiddler>>\n\n><$list\n\n filter=\"[all[shadows+tiddlers]tag[$:/tags/EditorToolbar]!has[draft.of]]\"\n\n><$reveal\n\n type=\"nomatch\"\n state=<<config-visibility-title>>\n text=\"hide\"\n class=\"tc-text-editor-toolbar-item-wrapper\"\n\n><$transclude\n\n tiddler=\"$:/core/ui/EditTemplate/body/toolbar/button\"\n mode=\"inline\"\n\n/></$reveal></$list></$set></$edit>\n"
},
"$:/core/ui/EditTemplate/body/toolbar/button": {
"title": "$:/core/ui/EditTemplate/body/toolbar/button",
"text": "\\define toolbar-button-icon()\n<$list\n\n filter=\"[all[current]!has[custom-icon]]\"\n variable=\"no-custom-icon\"\n\n><$transclude\n\n tiddler={{!!icon}}\n\n/></$list>\n\\end\n\n\\define toolbar-button-tooltip()\n{{!!description}}<$macrocall $name=\"displayshortcuts\" $output=\"text/plain\" shortcuts={{!!shortcuts}} prefix=\"` - [\" separator=\"] [\" suffix=\"]`\"/>\n\\end\n\n\\define toolbar-button()\n<$list\n\n filter={{!!condition}}\n variable=\"list-condition\"\n\n><$wikify\n\n name=\"tooltip-text\"\n text=<<toolbar-button-tooltip>>\n mode=\"inline\"\n output=\"text\"\n\n><$list\n\n filter=\"[all[current]!has[dropdown]]\"\n variable=\"no-dropdown\"\n\n><$button\n\n class=\"tc-btn-invisible $(buttonClasses)$\"\n tooltip=<<tooltip-text>>\n\n><span\n\n data-tw-keyboard-shortcut={{!!shortcuts}}\n\n/><<toolbar-button-icon>><$transclude\n\n tiddler=<<currentTiddler>>\n field=\"text\"\n\n/></$button></$list><$list\n\n filter=\"[all[current]has[dropdown]]\"\n variable=\"dropdown\"\n\n><$set\n\n name=\"dropdown-state\"\n value=<<qualify \"$:/state/EditorToolbarDropdown\">>\n\n><$button\n\n popup=<<dropdown-state>>\n class=\"tc-popup-keep tc-btn-invisible $(buttonClasses)$\"\n selectedClass=\"tc-selected\"\n tooltip=<<tooltip-text>>\n\n><span\n\n data-tw-keyboard-shortcut={{!!shortcuts}}\n\n/><<toolbar-button-icon>><$transclude\n\n tiddler=<<currentTiddler>>\n field=\"text\"\n\n/></$button><$reveal\n\n state=<<dropdown-state>>\n type=\"popup\"\n position=\"below\"\n animate=\"yes\"\n tag=\"span\"\n\n><div\n\n class=\"tc-drop-down tc-popup-keep\"\n\n><$transclude\n\n tiddler={{!!dropdown}}\n mode=\"block\"\n\n/></div></$reveal></$set></$list></$wikify></$list>\n\\end\n\n\\define toolbar-button-outer()\n<$set\n\n name=\"buttonClasses\"\n value={{!!button-classes}}\n\n><<toolbar-button>></$set>\n\\end\n\n<<toolbar-button-outer>>"
},
"$:/core/ui/EditTemplate/body": {
"title": "$:/core/ui/EditTemplate/body",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/Body/\n\\define config-visibility-title()\n$:/config/EditorToolbarButtons/Visibility/$(currentTiddler)$\n\\end\n<$list filter=\"[is[current]has[_canonical_uri]]\">\n\n<div class=\"tc-message-box\">\n\n<<lingo External/Hint>>\n\n<a href={{!!_canonical_uri}}><$text text={{!!_canonical_uri}}/></a>\n\n<$edit-text field=\"_canonical_uri\" class=\"tc-edit-fields\"></$edit-text>\n\n</div>\n\n</$list>\n\n<$list filter=\"[is[current]!has[_canonical_uri]]\">\n\n<$reveal state=\"$:/state/showeditpreview\" type=\"match\" text=\"yes\">\n\n<div class=\"tc-tiddler-preview\">\n\n<$transclude tiddler=\"$:/core/ui/EditTemplate/body/editor\" mode=\"inline\"/>\n\n<div class=\"tc-tiddler-preview-preview\">\n\n<$transclude tiddler={{$:/state/editpreviewtype}} mode=\"inline\">\n\n<$transclude tiddler=\"$:/core/ui/EditTemplate/body/preview/output\" mode=\"inline\"/>\n\n</$transclude>\n\n</div>\n\n</div>\n\n</$reveal>\n\n<$reveal state=\"$:/state/showeditpreview\" type=\"nomatch\" text=\"yes\">\n\n<$transclude tiddler=\"$:/core/ui/EditTemplate/body/editor\" mode=\"inline\"/>\n\n</$reveal>\n\n</$list>\n"
},
"$:/core/ui/EditTemplate/controls": {
"title": "$:/core/ui/EditTemplate/controls",
"tags": "$:/tags/EditTemplate",
"text": "\\define config-title()\n$:/config/EditToolbarButtons/Visibility/$(listItem)$\n\\end\n<div class=\"tc-tiddler-title tc-tiddler-edit-title\">\n<$view field=\"title\"/>\n<span class=\"tc-tiddler-controls tc-titlebar\"><$list filter=\"[all[shadows+tiddlers]tag[$:/tags/EditToolbar]!has[draft.of]]\" variable=\"listItem\"><$reveal type=\"nomatch\" state=<<config-title>> text=\"hide\"><$transclude tiddler=<<listItem>>/></$reveal></$list></span>\n<div style=\"clear: both;\"></div>\n</div>\n"
},
"$:/core/ui/EditTemplate/fields": {
"title": "$:/core/ui/EditTemplate/fields",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/\n\\define config-title()\n$:/config/EditTemplateFields/Visibility/$(currentField)$\n\\end\n\n\\define config-filter()\n[[hide]] -[title{$(config-title)$}]\n\\end\n\n\\define new-field-inner()\n<$reveal type=\"nomatch\" text=\"\" default=<<name>>>\n<$button>\n<$action-sendmessage $message=\"tm-add-field\" $name=<<name>> $value=<<value>>/>\n<$action-deletetiddler $tiddler=\"$:/temp/newfieldname\"/>\n<$action-deletetiddler $tiddler=\"$:/temp/newfieldvalue\"/>\n<<lingo Fields/Add/Button>>\n</$button>\n</$reveal>\n<$reveal type=\"match\" text=\"\" default=<<name>>>\n<$button>\n<<lingo Fields/Add/Button>>\n</$button>\n</$reveal>\n\\end\n\n\\define new-field()\n<$set name=\"name\" value={{$:/temp/newfieldname}}>\n<$set name=\"value\" value={{$:/temp/newfieldvalue}}>\n<<new-field-inner>>\n</$set>\n</$set>\n\\end\n\n<div class=\"tc-edit-fields\">\n<table class=\"tc-edit-fields\">\n<tbody>\n<$list filter=\"[all[current]fields[]] +[sort[title]]\" variable=\"currentField\">\n<$list filter=<<config-filter>> variable=\"temp\">\n<tr class=\"tc-edit-field\">\n<td class=\"tc-edit-field-name\">\n<$text text=<<currentField>>/>:</td>\n<td class=\"tc-edit-field-value\">\n<$edit-text tiddler=<<currentTiddler>> field=<<currentField>> placeholder={{$:/language/EditTemplate/Fields/Add/Value/Placeholder}}/>\n</td>\n<td class=\"tc-edit-field-remove\">\n<$button class=\"tc-btn-invisible\" tooltip={{$:/language/EditTemplate/Field/Remove/Hint}} aria-label={{$:/language/EditTemplate/Field/Remove/Caption}}>\n<$action-deletefield $field=<<currentField>>/>\n{{$:/core/images/delete-button}}\n</$button>\n</td>\n</tr>\n</$list>\n</$list>\n</tbody>\n</table>\n</div>\n\n<$fieldmangler>\n<div class=\"tc-edit-field-add\">\n<em class=\"tc-edit\">\n<<lingo Fields/Add/Prompt>>\n</em>\n<span class=\"tc-edit-field-add-name\">\n<$edit-text tiddler=\"$:/temp/newfieldname\" tag=\"input\" default=\"\" placeholder={{$:/language/EditTemplate/Fields/Add/Name/Placeholder}} focusPopup=<<qualify \"$:/state/popup/field-dropdown\">> class=\"tc-edit-texteditor tc-popup-handle\"/>\n</span>\n<$button popup=<<qualify \"$:/state/popup/field-dropdown\">> class=\"tc-btn-invisible tc-btn-dropdown\" tooltip={{$:/language/EditTemplate/Field/Dropdown/Hint}} aria-label={{$:/language/EditTemplate/Field/Dropdown/Caption}}>{{$:/core/images/down-arrow}}</$button>\n<$reveal state=<<qualify \"$:/state/popup/field-dropdown\">> type=\"nomatch\" text=\"\" default=\"\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown\">\n<$linkcatcher to=\"$:/temp/newfieldname\">\n<div class=\"tc-dropdown-item\">\n<<lingo Fields/Add/Dropdown/User>>\n</div>\n<$list filter=\"[!is[shadow]!is[system]fields[]search:title{$:/temp/newfieldname}sort[]] -created -creator -draft.of -draft.title -modified -modifier -tags -text -title -type\" variable=\"currentField\">\n<$link to=<<currentField>>>\n<<currentField>>\n</$link>\n</$list>\n<div class=\"tc-dropdown-item\">\n<<lingo Fields/Add/Dropdown/System>>\n</div>\n<$list filter=\"[fields[]search:title{$:/temp/newfieldname}sort[]] -[!is[shadow]!is[system]fields[]]\" variable=\"currentField\">\n<$link to=<<currentField>>>\n<<currentField>>\n</$link>\n</$list>\n</$linkcatcher>\n</div>\n</$reveal>\n<span class=\"tc-edit-field-add-value\">\n<$edit-text tiddler=\"$:/temp/newfieldvalue\" tag=\"input\" default=\"\" placeholder={{$:/language/EditTemplate/Fields/Add/Value/Placeholder}} class=\"tc-edit-texteditor\"/>\n</span>\n<span class=\"tc-edit-field-add-button\">\n<$macrocall $name=\"new-field\"/>\n</span>\n</div>\n</$fieldmangler>\n"
},
"$:/core/ui/EditTemplate/shadow": {
"title": "$:/core/ui/EditTemplate/shadow",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/Shadow/\n\\define pluginLinkBody()\n<$link to=\"\"\"$(pluginTitle)$\"\"\">\n<$text text=\"\"\"$(pluginTitle)$\"\"\"/>\n</$link>\n\\end\n<$list filter=\"[all[current]get[draft.of]is[shadow]!is[tiddler]]\">\n\n<$list filter=\"[all[current]shadowsource[]]\" variable=\"pluginTitle\">\n\n<$set name=\"pluginLink\" value=<<pluginLinkBody>>>\n<div class=\"tc-message-box\">\n\n<<lingo Warning>>\n\n</div>\n</$set>\n</$list>\n\n</$list>\n\n<$list filter=\"[all[current]get[draft.of]is[shadow]is[tiddler]]\">\n\n<$list filter=\"[all[current]shadowsource[]]\" variable=\"pluginTitle\">\n\n<$set name=\"pluginLink\" value=<<pluginLinkBody>>>\n<div class=\"tc-message-box\">\n\n<<lingo OverriddenWarning>>\n\n</div>\n</$set>\n</$list>\n\n</$list>"
},
"$:/core/ui/EditTemplate/tags": {
"title": "$:/core/ui/EditTemplate/tags",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/\n\n\\define tag-styles()\nbackground-color:$(backgroundColor)$;\nfill:$(foregroundColor)$;\ncolor:$(foregroundColor)$;\n\\end\n\n\\define tag-body-inner(colour,fallbackTarget,colourA,colourB)\n<$vars foregroundColor=<<contrastcolour target:\"\"\"$colour$\"\"\" fallbackTarget:\"\"\"$fallbackTarget$\"\"\" colourA:\"\"\"$colourA$\"\"\" colourB:\"\"\"$colourB$\"\"\">> backgroundColor=\"\"\"$colour$\"\"\">\n<span style=<<tag-styles>> class=\"tc-tag-label\">\n<$view field=\"title\" format=\"text\" />\n<$button message=\"tm-remove-tag\" param={{!!title}} class=\"tc-btn-invisible tc-remove-tag-button\">×</$button>\n</span>\n</$vars>\n\\end\n\n\\define tag-body(colour,palette)\n<$macrocall $name=\"tag-body-inner\" colour=\"\"\"$colour$\"\"\" fallbackTarget={{$palette$##tag-background}} colourA={{$palette$##foreground}} colourB={{$palette$##background}}/>\n\\end\n\n\\define tag-picker-actions()\n<$action-listops\n\t$tiddler=<<currentTiddler>>\n\t$field=\"tags\"\n\t$subfilter=\"[<tag>] [all[current]tags[]]\"\n/>\n\\end\n\n<div class=\"tc-edit-tags\">\n<$fieldmangler>\n<$list filter=\"[all[current]tags[]sort[title]]\" storyview=\"pop\">\n<$macrocall $name=\"tag-body\" colour={{!!color}} palette={{$:/palette}}/>\n</$list>\n</$fieldmangler>\n<$macrocall $name=\"tag-picker\" actions=<<tag-picker-actions>>/>\n</div>\n"
},
"$:/core/ui/EditTemplate/title": {
"title": "$:/core/ui/EditTemplate/title",
"tags": "$:/tags/EditTemplate",
"text": "<$edit-text field=\"draft.title\" class=\"tc-titlebar tc-edit-texteditor\" focus=\"true\"/>\n\n<$vars pattern=\"\"\"[\\|\\[\\]{}]\"\"\" bad-chars=\"\"\"`| [ ] { }`\"\"\">\n\n<$list filter=\"[is[current]regexp:draft.title<pattern>]\" variable=\"listItem\">\n\n<div class=\"tc-message-box\">\n\n{{$:/core/images/warning}} {{$:/language/EditTemplate/Title/BadCharacterWarning}}\n\n</div>\n\n</$list>\n\n</$vars>\n\n<$reveal state=\"!!draft.title\" type=\"nomatch\" text={{!!draft.of}} tag=\"div\">\n\n<$list filter=\"[{!!draft.title}!is[missing]]\" variable=\"listItem\">\n\n<div class=\"tc-message-box\">\n\n{{$:/core/images/warning}} {{$:/language/EditTemplate/Title/Exists/Prompt}}\n\n</div>\n\n</$list>\n\n<$list filter=\"[{!!draft.of}!is[missing]]\" variable=\"listItem\">\n\n<$vars fromTitle={{!!draft.of}} toTitle={{!!draft.title}}>\n\n<$checkbox tiddler=\"$:/config/RelinkOnRename\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"no\"> {{$:/language/EditTemplate/Title/Relink/Prompt}}</$checkbox>\n\n</$vars>\n\n</$list>\n\n</$reveal>\n\n\n"
},
"$:/core/ui/EditTemplate/type": {
"title": "$:/core/ui/EditTemplate/type",
"tags": "$:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/EditTemplate/\n<div class=\"tc-type-selector\"><$fieldmangler>\n<em class=\"tc-edit\"><<lingo Type/Prompt>></em> <$edit-text field=\"type\" tag=\"input\" default=\"\" placeholder={{$:/language/EditTemplate/Type/Placeholder}} focusPopup=<<qualify \"$:/state/popup/type-dropdown\">> class=\"tc-edit-typeeditor tc-popup-handle\"/> <$button popup=<<qualify \"$:/state/popup/type-dropdown\">> class=\"tc-btn-invisible tc-btn-dropdown\" tooltip={{$:/language/EditTemplate/Type/Dropdown/Hint}} aria-label={{$:/language/EditTemplate/Type/Dropdown/Caption}}>{{$:/core/images/down-arrow}}</$button> <$button message=\"tm-remove-field\" param=\"type\" class=\"tc-btn-invisible tc-btn-icon\" tooltip={{$:/language/EditTemplate/Type/Delete/Hint}} aria-label={{$:/language/EditTemplate/Type/Delete/Caption}}>{{$:/core/images/delete-button}}</$button>\n</$fieldmangler></div>\n\n<div class=\"tc-block-dropdown-wrapper\">\n<$reveal state=<<qualify \"$:/state/popup/type-dropdown\">> type=\"nomatch\" text=\"\" default=\"\">\n<div class=\"tc-block-dropdown tc-edit-type-dropdown\">\n<$linkcatcher to=\"!!type\">\n<$list filter='[all[shadows+tiddlers]prefix[$:/language/Docs/Types/]each[group]sort[group-sort]]'>\n<div class=\"tc-dropdown-item\">\n<$text text={{!!group}}/>\n</div>\n<$list filter=\"[all[shadows+tiddlers]prefix[$:/language/Docs/Types/]group{!!group}] +[sort[description]]\"><$link to={{!!name}}><$view field=\"description\"/> (<$view field=\"name\"/>)</$link>\n</$list>\n</$list>\n</$linkcatcher>\n</div>\n</$reveal>\n</div>"
},
"$:/core/ui/EditTemplate": {
"title": "$:/core/ui/EditTemplate",
"text": "\\define actions()\n<$action-sendmessage $message=\"tm-add-tag\" $param={{$:/temp/NewTagName}}/>\n<$action-deletetiddler $tiddler=\"$:/temp/NewTagName\"/>\n<$action-sendmessage $message=\"tm-add-field\" $name={{$:/temp/newfieldname}} $value={{$:/temp/newfieldvalue}}/>\n<$action-deletetiddler $tiddler=\"$:/temp/newfieldname\"/>\n<$action-deletetiddler $tiddler=\"$:/temp/newfieldvalue\"/>\n<$action-sendmessage $message=\"tm-save-tiddler\"/>\n\\end\n\\define frame-classes()\ntc-tiddler-frame tc-tiddler-edit-frame $(missingTiddlerClass)$ $(shadowTiddlerClass)$ $(systemTiddlerClass)$\n\\end\n<div class=<<frame-classes>>>\n<$fieldmangler>\n<$set name=\"storyTiddler\" value=<<currentTiddler>>>\n<$keyboard key=\"((cancel-edit-tiddler))\" message=\"tm-cancel-tiddler\">\n<$keyboard key=\"((save-tiddler))\" actions=<<actions>>>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/EditTemplate]!has[draft.of]]\" variable=\"listItem\">\n<$transclude tiddler=<<listItem>>/>\n</$list>\n</$keyboard>\n</$keyboard>\n</$set>\n</$fieldmangler>\n</div>\n"
},
"$:/core/ui/Buttons/cancel": {
"title": "$:/core/ui/Buttons/cancel",
"tags": "$:/tags/EditToolbar",
"caption": "{{$:/core/images/cancel-button}} {{$:/language/Buttons/Cancel/Caption}}",
"description": "{{$:/language/Buttons/Cancel/Hint}}",
"text": "<$button message=\"tm-cancel-tiddler\" tooltip={{$:/language/Buttons/Cancel/Hint}} aria-label={{$:/language/Buttons/Cancel/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/cancel-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Cancel/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/delete": {
"title": "$:/core/ui/Buttons/delete",
"tags": "$:/tags/EditToolbar $:/tags/ViewToolbar",
"caption": "{{$:/core/images/delete-button}} {{$:/language/Buttons/Delete/Caption}}",
"description": "{{$:/language/Buttons/Delete/Hint}}",
"text": "<$button message=\"tm-delete-tiddler\" tooltip={{$:/language/Buttons/Delete/Hint}} aria-label={{$:/language/Buttons/Delete/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/delete-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Delete/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/save": {
"title": "$:/core/ui/Buttons/save",
"tags": "$:/tags/EditToolbar",
"caption": "{{$:/core/images/done-button}} {{$:/language/Buttons/Save/Caption}}",
"description": "{{$:/language/Buttons/Save/Hint}}",
"text": "<$fieldmangler><$button tooltip={{$:/language/Buttons/Save/Hint}} aria-label={{$:/language/Buttons/Save/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-add-tag\" $param={{$:/temp/NewTagName}}/>\n<$action-deletetiddler $tiddler=\"$:/temp/NewTagName\"/>\n<$action-sendmessage $message=\"tm-add-field\" $name={{$:/temp/newfieldname}} $value={{$:/temp/newfieldvalue}}/>\n<$action-deletetiddler $tiddler=\"$:/temp/newfieldname\"/>\n<$action-deletetiddler $tiddler=\"$:/temp/newfieldvalue\"/>\n<$action-sendmessage $message=\"tm-save-tiddler\"/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/done-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Save/Caption}}/></span>\n</$list>\n</$button>\n</$fieldmangler>\n"
},
"$:/core/ui/EditorToolbar/bold": {
"title": "$:/core/ui/EditorToolbar/bold",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/bold",
"caption": "{{$:/language/Buttons/Bold/Caption}}",
"description": "{{$:/language/Buttons/Bold/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((bold))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"''\"\n\tsuffix=\"''\"\n/>\n"
},
"$:/core/ui/EditorToolbar/clear-dropdown": {
"title": "$:/core/ui/EditorToolbar/clear-dropdown",
"text": "''{{$:/language/Buttons/Clear/Hint}}''\n\n<div class=\"tc-colour-chooser\">\n\n<$macrocall $name=\"colour-picker\" actions=\"\"\"\n\n<$action-sendmessage\n\t$message=\"tm-edit-bitmap-operation\"\n\t$param=\"clear\"\n\tcolour=<<colour-picker-value>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n\"\"\"/>\n\n</div>\n"
},
"$:/core/ui/EditorToolbar/clear": {
"title": "$:/core/ui/EditorToolbar/clear",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/erase",
"caption": "{{$:/language/Buttons/Clear/Caption}}",
"description": "{{$:/language/Buttons/Clear/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/clear-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/editor-height-dropdown": {
"title": "$:/core/ui/EditorToolbar/editor-height-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/EditorHeight/\n''<<lingo Hint>>''\n\n<$radio tiddler=\"$:/config/TextEditor/EditorHeight/Mode\" value=\"auto\"> {{$:/core/images/auto-height}} <<lingo Caption/Auto>></$radio>\n\n<$radio tiddler=\"$:/config/TextEditor/EditorHeight/Mode\" value=\"fixed\"> {{$:/core/images/fixed-height}} <<lingo Caption/Fixed>> <$edit-text tag=\"input\" tiddler=\"$:/config/TextEditor/EditorHeight/Height\" default=\"100px\"/></$radio>\n"
},
"$:/core/ui/EditorToolbar/editor-height": {
"title": "$:/core/ui/EditorToolbar/editor-height",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/fixed-height",
"custom-icon": "yes",
"caption": "{{$:/language/Buttons/EditorHeight/Caption}}",
"description": "{{$:/language/Buttons/EditorHeight/Hint}}",
"condition": "[<targetTiddler>!is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/editor-height-dropdown",
"text": "<$reveal tag=\"span\" state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"fixed\">\n{{$:/core/images/fixed-height}}\n</$reveal>\n<$reveal tag=\"span\" state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"auto\">\n{{$:/core/images/auto-height}}\n</$reveal>\n"
},
"$:/core/ui/EditorToolbar/excise-dropdown": {
"title": "$:/core/ui/EditorToolbar/excise-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/Excise/\n\n\\define body(config-title)\n''<<lingo Hint>>''\n\n<<lingo Caption/NewTitle>> <$edit-text tag=\"input\" tiddler=\"$config-title$/new-title\" default=\"\" focus=\"true\"/>\n\n<$set name=\"new-title\" value={{$config-title$/new-title}}>\n<$list filter=\"\"\"[<new-title>is[tiddler]]\"\"\">\n<div class=\"tc-error\">\n<<lingo Caption/TiddlerExists>>\n</div>\n</$list>\n</$set>\n\n<$checkbox tiddler=\"\"\"$config-title$/tagnew\"\"\" field=\"text\" checked=\"yes\" unchecked=\"no\" default=\"false\"> <<lingo Caption/Tag>></$checkbox>\n\n<<lingo Caption/Replace>> <$select tiddler=\"\"\"$config-title$/type\"\"\" default=\"transclude\">\n<option value=\"link\"><<lingo Caption/Replace/Link>></option>\n<option value=\"transclude\"><<lingo Caption/Replace/Transclusion>></option>\n<option value=\"macro\"><<lingo Caption/Replace/Macro>></option>\n</$select>\n\n<$reveal state=\"\"\"$config-title$/type\"\"\" type=\"match\" text=\"macro\">\n<<lingo Caption/MacroName>> <$edit-text tag=\"input\" tiddler=\"\"\"$config-title$/macro-title\"\"\" default=\"translink\"/>\n</$reveal>\n\n<$button>\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"excise\"\n\ttitle={{$config-title$/new-title}}\n\ttype={{$config-title$/type}}\n\tmacro={{$config-title$/macro-title}}\n\ttagnew={{$config-title$/tagnew}}\n/>\n<$action-deletetiddler\n\t$tiddler=\"$config-title$/new-title\"\n/>\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n<<lingo Caption/Excise>>\n</$button>\n\\end\n\n<$macrocall $name=\"body\" config-title=<<qualify \"$:/state/Excise/\">>/>\n"
},
"$:/core/ui/EditorToolbar/excise": {
"title": "$:/core/ui/EditorToolbar/excise",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/excise",
"caption": "{{$:/language/Buttons/Excise/Caption}}",
"description": "{{$:/language/Buttons/Excise/Hint}}",
"condition": "[<targetTiddler>!is[image]]",
"shortcuts": "((excise))",
"dropdown": "$:/core/ui/EditorToolbar/excise-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/heading-1": {
"title": "$:/core/ui/EditorToolbar/heading-1",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-1",
"caption": "{{$:/language/Buttons/Heading1/Caption}}",
"description": "{{$:/language/Buttons/Heading1/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"button-classes": "tc-text-editor-toolbar-item-start-group",
"shortcuts": "((heading-1))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"1\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-2": {
"title": "$:/core/ui/EditorToolbar/heading-2",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-2",
"caption": "{{$:/language/Buttons/Heading2/Caption}}",
"description": "{{$:/language/Buttons/Heading2/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-2))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"2\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-3": {
"title": "$:/core/ui/EditorToolbar/heading-3",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-3",
"caption": "{{$:/language/Buttons/Heading3/Caption}}",
"description": "{{$:/language/Buttons/Heading3/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-3))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"3\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-4": {
"title": "$:/core/ui/EditorToolbar/heading-4",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-4",
"caption": "{{$:/language/Buttons/Heading4/Caption}}",
"description": "{{$:/language/Buttons/Heading4/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-4))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"4\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-5": {
"title": "$:/core/ui/EditorToolbar/heading-5",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-5",
"caption": "{{$:/language/Buttons/Heading5/Caption}}",
"description": "{{$:/language/Buttons/Heading5/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-5))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"5\"\n/>\n"
},
"$:/core/ui/EditorToolbar/heading-6": {
"title": "$:/core/ui/EditorToolbar/heading-6",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/heading-6",
"caption": "{{$:/language/Buttons/Heading6/Caption}}",
"description": "{{$:/language/Buttons/Heading6/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((heading-6))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"!\"\n\tcount=\"6\"\n/>\n"
},
"$:/core/ui/EditorToolbar/italic": {
"title": "$:/core/ui/EditorToolbar/italic",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/italic",
"caption": "{{$:/language/Buttons/Italic/Caption}}",
"description": "{{$:/language/Buttons/Italic/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((italic))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"//\"\n\tsuffix=\"//\"\n/>\n"
},
"$:/core/ui/EditorToolbar/line-width-dropdown": {
"title": "$:/core/ui/EditorToolbar/line-width-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/LineWidth/\n\n\\define toolbar-line-width-inner()\n<$button tag=\"a\" tooltip=\"\"\"$(line-width)$\"\"\">\n\n<$action-setfield\n\t$tiddler=\"$:/config/BitmapEditor/LineWidth\"\n\t$value=\"$(line-width)$\"\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<div style=\"display: inline-block; margin: 4px calc(80px - $(line-width)$); background-color: #000; width: calc(100px + $(line-width)$ * 2); height: $(line-width)$; border-radius: 120px; vertical-align: middle;\"/>\n\n<span style=\"margin-left: 8px;\">\n\n<$text text=\"\"\"$(line-width)$\"\"\"/>\n\n<$reveal state=\"$:/config/BitmapEditor/LineWidth\" type=\"match\" text=\"\"\"$(line-width)$\"\"\" tag=\"span\">\n\n<$entity entity=\" \"/>\n\n<$entity entity=\"✓\"/>\n\n</$reveal>\n\n</span>\n\n</$button>\n\\end\n\n''<<lingo Hint>>''\n\n<$list filter={{$:/config/BitmapEditor/LineWidths}} variable=\"line-width\">\n\n<<toolbar-line-width-inner>>\n\n</$list>\n"
},
"$:/core/ui/EditorToolbar/line-width": {
"title": "$:/core/ui/EditorToolbar/line-width",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/line-width",
"caption": "{{$:/language/Buttons/LineWidth/Caption}}",
"description": "{{$:/language/Buttons/LineWidth/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/line-width-dropdown",
"text": "<$text text={{$:/config/BitmapEditor/LineWidth}}/>"
},
"$:/core/ui/EditorToolbar/link-dropdown": {
"title": "$:/core/ui/EditorToolbar/link-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/Link/\n\n\\define link-actions()\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"make-link\"\n\ttext={{$(linkTiddler)$}}\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<searchTiddler>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<linkTiddler>>\n/>\n\\end\n\n\\define external-link()\n<$button class=\"tc-btn-invisible\" style=\"width: auto; display: inline-block; background-colour: inherit;\">\n<$action-sendmessage $message=\"tm-edit-text-operation\" $param=\"make-link\" text={{$(searchTiddler)$}}\n/>\n{{$:/core/images/chevron-right}}\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<searchTiddler>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<linkTiddler>>\n/>\n</$button>\n\\end\n\n\n\\define body(config-title)\n''<<lingo Hint>>''\n\n<$vars searchTiddler=\"\"\"$config-title$/search\"\"\" linkTiddler=\"\"\"$config-title$/link\"\"\" linktext=\"\" >\n\n<$edit-text tiddler=<<searchTiddler>> type=\"search\" tag=\"input\" focus=\"true\" placeholder={{$:/language/Search/Search}} default=\"\"/>\n<$reveal tag=\"span\" state=<<searchTiddler>> type=\"nomatch\" text=\"\">\n<<external-link>>\n<$button class=\"tc-btn-invisible\" style=\"width: auto; display: inline-block; background-colour: inherit;\">\n<$action-setfield $tiddler=<<searchTiddler>> text=\"\" />\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n\n<$reveal tag=\"div\" state=<<searchTiddler>> type=\"nomatch\" text=\"\">\n\n<$linkcatcher actions=<<link-actions>> to=<<linkTiddler>>>\n\n{{$:/core/ui/SearchResults}}\n\n</$linkcatcher>\n\n</$reveal>\n\n</$vars>\n\n\\end\n\n<$macrocall $name=\"body\" config-title=<<qualify \"$:/state/Link/\">>/>"
},
"$:/core/ui/EditorToolbar/link": {
"title": "$:/core/ui/EditorToolbar/link",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/link",
"caption": "{{$:/language/Buttons/Link/Caption}}",
"description": "{{$:/language/Buttons/Link/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"button-classes": "tc-text-editor-toolbar-item-start-group",
"shortcuts": "((link))",
"dropdown": "$:/core/ui/EditorToolbar/link-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/list-bullet": {
"title": "$:/core/ui/EditorToolbar/list-bullet",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/list-bullet",
"caption": "{{$:/language/Buttons/ListBullet/Caption}}",
"description": "{{$:/language/Buttons/ListBullet/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((list-bullet))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"*\"\n\tcount=\"1\"\n/>\n"
},
"$:/core/ui/EditorToolbar/list-number": {
"title": "$:/core/ui/EditorToolbar/list-number",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/list-number",
"caption": "{{$:/language/Buttons/ListNumber/Caption}}",
"description": "{{$:/language/Buttons/ListNumber/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((list-number))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"prefix-lines\"\n\tcharacter=\"#\"\n\tcount=\"1\"\n/>\n"
},
"$:/core/ui/EditorToolbar/mono-block": {
"title": "$:/core/ui/EditorToolbar/mono-block",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/mono-block",
"caption": "{{$:/language/Buttons/MonoBlock/Caption}}",
"description": "{{$:/language/Buttons/MonoBlock/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"button-classes": "tc-text-editor-toolbar-item-start-group",
"shortcuts": "((mono-block))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-lines\"\n\tprefix=\"\n```\"\n\tsuffix=\"```\"\n/>\n"
},
"$:/core/ui/EditorToolbar/mono-line": {
"title": "$:/core/ui/EditorToolbar/mono-line",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/mono-line",
"caption": "{{$:/language/Buttons/MonoLine/Caption}}",
"description": "{{$:/language/Buttons/MonoLine/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((mono-line))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"`\"\n\tsuffix=\"`\"\n/>\n"
},
"$:/core/ui/EditorToolbar/more-dropdown": {
"title": "$:/core/ui/EditorToolbar/more-dropdown",
"text": "\\define config-title()\n$:/config/EditorToolbarButtons/Visibility/$(toolbarItem)$\n\\end\n\n\\define conditional-button()\n<$list filter={{$(toolbarItem)$!!condition}} variable=\"condition\">\n<$transclude tiddler=\"$:/core/ui/EditTemplate/body/toolbar/button\" mode=\"inline\"/> <$transclude tiddler=<<toolbarItem>> field=\"description\"/>\n</$list>\n\\end\n\n<div class=\"tc-text-editor-toolbar-more\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/EditorToolbar]!has[draft.of]] -[[$:/core/ui/EditorToolbar/more]]\">\n<$reveal type=\"match\" state=<<config-visibility-title>> text=\"hide\" tag=\"div\">\n<<conditional-button>>\n</$reveal>\n</$list>\n</div>\n"
},
"$:/core/ui/EditorToolbar/more": {
"title": "$:/core/ui/EditorToolbar/more",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/down-arrow",
"caption": "{{$:/language/Buttons/More/Caption}}",
"description": "{{$:/language/Buttons/More/Hint}}",
"condition": "[<targetTiddler>]",
"dropdown": "$:/core/ui/EditorToolbar/more-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/opacity-dropdown": {
"title": "$:/core/ui/EditorToolbar/opacity-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/Opacity/\n\n\\define toolbar-opacity-inner()\n<$button tag=\"a\" tooltip=\"\"\"$(opacity)$\"\"\">\n\n<$action-setfield\n\t$tiddler=\"$:/config/BitmapEditor/Opacity\"\n\t$value=\"$(opacity)$\"\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<div style=\"display: inline-block; vertical-align: middle; background-color: $(current-paint-colour)$; opacity: $(opacity)$; width: 1em; height: 1em; border-radius: 50%;\"/>\n\n<span style=\"margin-left: 8px;\">\n\n<$text text=\"\"\"$(opacity)$\"\"\"/>\n\n<$reveal state=\"$:/config/BitmapEditor/Opacity\" type=\"match\" text=\"\"\"$(opacity)$\"\"\" tag=\"span\">\n\n<$entity entity=\" \"/>\n\n<$entity entity=\"✓\"/>\n\n</$reveal>\n\n</span>\n\n</$button>\n\\end\n\n\\define toolbar-opacity()\n''<<lingo Hint>>''\n\n<$list filter={{$:/config/BitmapEditor/Opacities}} variable=\"opacity\">\n\n<<toolbar-opacity-inner>>\n\n</$list>\n\\end\n\n<$set name=\"current-paint-colour\" value={{$:/config/BitmapEditor/Colour}}>\n\n<$set name=\"current-opacity\" value={{$:/config/BitmapEditor/Opacity}}>\n\n<<toolbar-opacity>>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/EditorToolbar/opacity": {
"title": "$:/core/ui/EditorToolbar/opacity",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/opacity",
"caption": "{{$:/language/Buttons/Opacity/Caption}}",
"description": "{{$:/language/Buttons/Opacity/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/opacity-dropdown",
"text": "<$text text={{$:/config/BitmapEditor/Opacity}}/>\n"
},
"$:/core/ui/EditorToolbar/paint-dropdown": {
"title": "$:/core/ui/EditorToolbar/paint-dropdown",
"text": "''{{$:/language/Buttons/Paint/Hint}}''\n\n<$macrocall $name=\"colour-picker\" actions=\"\"\"\n\n<$action-setfield\n\t$tiddler=\"$:/config/BitmapEditor/Colour\"\n\t$value=<<colour-picker-value>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n\"\"\"/>\n"
},
"$:/core/ui/EditorToolbar/paint": {
"title": "$:/core/ui/EditorToolbar/paint",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/paint",
"caption": "{{$:/language/Buttons/Paint/Caption}}",
"description": "{{$:/language/Buttons/Paint/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/paint-dropdown",
"text": "\\define toolbar-paint()\n<div style=\"display: inline-block; vertical-align: middle; background-color: $(colour-picker-value)$; width: 1em; height: 1em; border-radius: 50%;\"/>\n\\end\n<$set name=\"colour-picker-value\" value={{$:/config/BitmapEditor/Colour}}>\n<<toolbar-paint>>\n</$set>\n"
},
"$:/core/ui/EditorToolbar/picture-dropdown": {
"title": "$:/core/ui/EditorToolbar/picture-dropdown",
"text": "\\define replacement-text()\n[img[$(imageTitle)$]]\n\\end\n\n''{{$:/language/Buttons/Picture/Hint}}''\n\n<$macrocall $name=\"image-picker\" actions=\"\"\"\n\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"replace-selection\"\n\ttext=<<replacement-text>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n\"\"\"/>\n"
},
"$:/core/ui/EditorToolbar/picture": {
"title": "$:/core/ui/EditorToolbar/picture",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/picture",
"caption": "{{$:/language/Buttons/Picture/Caption}}",
"description": "{{$:/language/Buttons/Picture/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((picture))",
"dropdown": "$:/core/ui/EditorToolbar/picture-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/preview-type-dropdown": {
"title": "$:/core/ui/EditorToolbar/preview-type-dropdown",
"text": "\\define preview-type-button()\n<$button tag=\"a\">\n\n<$action-setfield $tiddler=\"$:/state/editpreviewtype\" $value=\"$(previewType)$\"/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<$transclude tiddler=<<previewType>> field=\"caption\" mode=\"inline\">\n\n<$view tiddler=<<previewType>> field=\"title\" mode=\"inline\"/>\n\n</$transclude> \n\n<$reveal tag=\"span\" state=\"$:/state/editpreviewtype\" type=\"match\" text=<<previewType>> default=\"$:/core/ui/EditTemplate/body/preview/output\">\n\n<$entity entity=\" \"/>\n\n<$entity entity=\"✓\"/>\n\n</$reveal>\n\n</$button>\n\\end\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/EditPreview]!has[draft.of]]\" variable=\"previewType\">\n\n<<preview-type-button>>\n\n</$list>\n"
},
"$:/core/ui/EditorToolbar/preview-type": {
"title": "$:/core/ui/EditorToolbar/preview-type",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/chevron-down",
"caption": "{{$:/language/Buttons/PreviewType/Caption}}",
"description": "{{$:/language/Buttons/PreviewType/Hint}}",
"condition": "[all[shadows+tiddlers]tag[$:/tags/EditPreview]!has[draft.of]butfirst[]limit[1]]",
"button-classes": "tc-text-editor-toolbar-item-adjunct",
"dropdown": "$:/core/ui/EditorToolbar/preview-type-dropdown"
},
"$:/core/ui/EditorToolbar/preview": {
"title": "$:/core/ui/EditorToolbar/preview",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/preview-open",
"custom-icon": "yes",
"caption": "{{$:/language/Buttons/Preview/Caption}}",
"description": "{{$:/language/Buttons/Preview/Hint}}",
"condition": "[<targetTiddler>]",
"button-classes": "tc-text-editor-toolbar-item-start-group",
"shortcuts": "((preview))",
"text": "<$reveal state=\"$:/state/showeditpreview\" type=\"match\" text=\"yes\" tag=\"span\">\n{{$:/core/images/preview-open}}\n<$action-setfield $tiddler=\"$:/state/showeditpreview\" $value=\"no\"/>\n</$reveal>\n<$reveal state=\"$:/state/showeditpreview\" type=\"nomatch\" text=\"yes\" tag=\"span\">\n{{$:/core/images/preview-closed}}\n<$action-setfield $tiddler=\"$:/state/showeditpreview\" $value=\"yes\"/>\n</$reveal>\n"
},
"$:/core/ui/EditorToolbar/quote": {
"title": "$:/core/ui/EditorToolbar/quote",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/quote",
"caption": "{{$:/language/Buttons/Quote/Caption}}",
"description": "{{$:/language/Buttons/Quote/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((quote))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-lines\"\n\tprefix=\"\n<<<\"\n\tsuffix=\"<<<\"\n/>\n"
},
"$:/core/ui/EditorToolbar/size-dropdown": {
"title": "$:/core/ui/EditorToolbar/size-dropdown",
"text": "\\define lingo-base() $:/language/Buttons/Size/\n\n\\define toolbar-button-size-preset(config-title)\n<$set name=\"width\" filter=\"$(sizePair)$ +[first[]]\">\n\n<$set name=\"height\" filter=\"$(sizePair)$ +[last[]]\">\n\n<$button tag=\"a\">\n\n<$action-setfield\n\t$tiddler=\"\"\"$config-title$/new-width\"\"\"\n\t$value=<<width>>\n/>\n\n<$action-setfield\n\t$tiddler=\"\"\"$config-title$/new-height\"\"\"\n\t$value=<<height>>\n/>\n\n<$action-deletetiddler\n\t$tiddler=\"\"\"$config-title$/presets-popup\"\"\"\n/>\n\n<$text text=<<width>>/> × <$text text=<<height>>/>\n\n</$button>\n\n</$set>\n\n</$set>\n\\end\n\n\\define toolbar-button-size(config-title)\n''{{$:/language/Buttons/Size/Hint}}''\n\n<<lingo Caption/Width>> <$edit-text tag=\"input\" tiddler=\"\"\"$config-title$/new-width\"\"\" default=<<tv-bitmap-editor-width>> focus=\"true\" size=\"8\"/> <<lingo Caption/Height>> <$edit-text tag=\"input\" tiddler=\"\"\"$config-title$/new-height\"\"\" default=<<tv-bitmap-editor-height>> size=\"8\"/> <$button popup=\"\"\"$config-title$/presets-popup\"\"\" class=\"tc-btn-invisible tc-popup-keep\" style=\"width: auto; display: inline-block; background-colour: inherit;\" selectedClass=\"tc-selected\">\n{{$:/core/images/down-arrow}}\n</$button>\n\n<$reveal tag=\"span\" state=\"\"\"$config-title$/presets-popup\"\"\" type=\"popup\" position=\"belowleft\" animate=\"yes\">\n\n<div class=\"tc-drop-down tc-popup-keep\">\n\n<$list filter={{$:/config/BitmapEditor/ImageSizes}} variable=\"sizePair\">\n\n<$macrocall $name=\"toolbar-button-size-preset\" config-title=\"$config-title$\"/>\n\n</$list>\n\n</div>\n\n</$reveal>\n\n<$button>\n<$action-sendmessage\n\t$message=\"tm-edit-bitmap-operation\"\n\t$param=\"resize\"\n\twidth={{$config-title$/new-width}}\n\theight={{$config-title$/new-height}}\n/>\n<$action-deletetiddler\n\t$tiddler=\"\"\"$config-title$/new-width\"\"\"\n/>\n<$action-deletetiddler\n\t$tiddler=\"\"\"$config-title$/new-height\"\"\"\n/>\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n<<lingo Caption/Resize>>\n</$button>\n\\end\n\n<$macrocall $name=\"toolbar-button-size\" config-title=<<qualify \"$:/state/Size/\">>/>\n"
},
"$:/core/ui/EditorToolbar/size": {
"title": "$:/core/ui/EditorToolbar/size",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/size",
"caption": "{{$:/language/Buttons/Size/Caption}}",
"description": "{{$:/language/Buttons/Size/Hint}}",
"condition": "[<targetTiddler>is[image]]",
"dropdown": "$:/core/ui/EditorToolbar/size-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/stamp-dropdown": {
"title": "$:/core/ui/EditorToolbar/stamp-dropdown",
"text": "\\define toolbar-button-stamp-inner()\n<$button tag=\"a\">\n\n<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"replace-selection\"\n\ttext={{$(snippetTitle)$}}\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<$view tiddler=<<snippetTitle>> field=\"caption\" mode=\"inline\">\n\n<$view tiddler=<<snippetTitle>> field=\"title\" mode=\"inline\"/>\n\n</$view>\n\n</$button>\n\\end\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TextEditor/Snippet]!has[draft.of]sort[caption]]\" variable=\"snippetTitle\">\n\n<<toolbar-button-stamp-inner>>\n\n</$list>\n\n----\n\n<$button tag=\"a\">\n\n<$action-sendmessage\n\t$message=\"tm-new-tiddler\"\n\ttags=\"$:/tags/TextEditor/Snippet\"\n\tcaption={{$:/language/Buttons/Stamp/New/Title}}\n\ttext={{$:/language/Buttons/Stamp/New/Text}}\n/>\n\n<$action-deletetiddler\n\t$tiddler=<<dropdown-state>>\n/>\n\n<em>\n\n<$text text={{$:/language/Buttons/Stamp/Caption/New}}/>\n\n</em>\n\n</$button>\n"
},
"$:/core/ui/EditorToolbar/stamp": {
"title": "$:/core/ui/EditorToolbar/stamp",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/stamp",
"caption": "{{$:/language/Buttons/Stamp/Caption}}",
"description": "{{$:/language/Buttons/Stamp/Hint}}",
"condition": "[<targetTiddler>!is[image]]",
"shortcuts": "((stamp))",
"dropdown": "$:/core/ui/EditorToolbar/stamp-dropdown",
"text": ""
},
"$:/core/ui/EditorToolbar/strikethrough": {
"title": "$:/core/ui/EditorToolbar/strikethrough",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/strikethrough",
"caption": "{{$:/language/Buttons/Strikethrough/Caption}}",
"description": "{{$:/language/Buttons/Strikethrough/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((strikethrough))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"~~\"\n\tsuffix=\"~~\"\n/>\n"
},
"$:/core/ui/EditorToolbar/subscript": {
"title": "$:/core/ui/EditorToolbar/subscript",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/subscript",
"caption": "{{$:/language/Buttons/Subscript/Caption}}",
"description": "{{$:/language/Buttons/Subscript/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((subscript))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\",,\"\n\tsuffix=\",,\"\n/>\n"
},
"$:/core/ui/EditorToolbar/superscript": {
"title": "$:/core/ui/EditorToolbar/superscript",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/superscript",
"caption": "{{$:/language/Buttons/Superscript/Caption}}",
"description": "{{$:/language/Buttons/Superscript/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((superscript))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"^^\"\n\tsuffix=\"^^\"\n/>\n"
},
"$:/core/ui/EditorToolbar/underline": {
"title": "$:/core/ui/EditorToolbar/underline",
"tags": "$:/tags/EditorToolbar",
"icon": "$:/core/images/underline",
"caption": "{{$:/language/Buttons/Underline/Caption}}",
"description": "{{$:/language/Buttons/Underline/Hint}}",
"condition": "[<targetTiddler>!has[type]] [<targetTiddler>type[text/vnd.tiddlywiki]]",
"shortcuts": "((underline))",
"text": "<$action-sendmessage\n\t$message=\"tm-edit-text-operation\"\n\t$param=\"wrap-selection\"\n\tprefix=\"__\"\n\tsuffix=\"__\"\n/>\n"
},
"$:/core/Filters/AllTags": {
"title": "$:/core/Filters/AllTags",
"tags": "$:/tags/Filter",
"filter": "[tags[]!is[system]sort[title]]",
"description": "{{$:/language/Filters/AllTags}}",
"text": ""
},
"$:/core/Filters/AllTiddlers": {
"title": "$:/core/Filters/AllTiddlers",
"tags": "$:/tags/Filter",
"filter": "[!is[system]sort[title]]",
"description": "{{$:/language/Filters/AllTiddlers}}",
"text": ""
},
"$:/core/Filters/Drafts": {
"title": "$:/core/Filters/Drafts",
"tags": "$:/tags/Filter",
"filter": "[has[draft.of]sort[title]]",
"description": "{{$:/language/Filters/Drafts}}",
"text": ""
},
"$:/core/Filters/Missing": {
"title": "$:/core/Filters/Missing",
"tags": "$:/tags/Filter",
"filter": "[all[missing]sort[title]]",
"description": "{{$:/language/Filters/Missing}}",
"text": ""
},
"$:/core/Filters/Orphans": {
"title": "$:/core/Filters/Orphans",
"tags": "$:/tags/Filter",
"filter": "[all[orphans]sort[title]]",
"description": "{{$:/language/Filters/Orphans}}",
"text": ""
},
"$:/core/Filters/OverriddenShadowTiddlers": {
"title": "$:/core/Filters/OverriddenShadowTiddlers",
"tags": "$:/tags/Filter",
"filter": "[is[shadow]]",
"description": "{{$:/language/Filters/OverriddenShadowTiddlers}}",
"text": ""
},
"$:/core/Filters/RecentSystemTiddlers": {
"title": "$:/core/Filters/RecentSystemTiddlers",
"tags": "$:/tags/Filter",
"filter": "[has[modified]!sort[modified]limit[50]]",
"description": "{{$:/language/Filters/RecentSystemTiddlers}}",
"text": ""
},
"$:/core/Filters/RecentTiddlers": {
"title": "$:/core/Filters/RecentTiddlers",
"tags": "$:/tags/Filter",
"filter": "[!is[system]has[modified]!sort[modified]limit[50]]",
"description": "{{$:/language/Filters/RecentTiddlers}}",
"text": ""
},
"$:/core/Filters/ShadowTiddlers": {
"title": "$:/core/Filters/ShadowTiddlers",
"tags": "$:/tags/Filter",
"filter": "[all[shadows]sort[title]]",
"description": "{{$:/language/Filters/ShadowTiddlers}}",
"text": ""
},
"$:/core/Filters/StoryList": {
"title": "$:/core/Filters/StoryList",
"tags": "$:/tags/Filter",
"filter": "[list[$:/StoryList]] -$:/AdvancedSearch",
"description": "{{$:/language/Filters/StoryList}}",
"text": ""
},
"$:/core/Filters/SystemTags": {
"title": "$:/core/Filters/SystemTags",
"tags": "$:/tags/Filter",
"filter": "[all[shadows+tiddlers]tags[]is[system]sort[title]]",
"description": "{{$:/language/Filters/SystemTags}}",
"text": ""
},
"$:/core/Filters/SystemTiddlers": {
"title": "$:/core/Filters/SystemTiddlers",
"tags": "$:/tags/Filter",
"filter": "[is[system]sort[title]]",
"description": "{{$:/language/Filters/SystemTiddlers}}",
"text": ""
},
"$:/core/Filters/TypedTiddlers": {
"title": "$:/core/Filters/TypedTiddlers",
"tags": "$:/tags/Filter",
"filter": "[!is[system]has[type]each[type]sort[type]] -[type[text/vnd.tiddlywiki]]",
"description": "{{$:/language/Filters/TypedTiddlers}}",
"text": ""
},
"$:/core/ui/ImportListing": {
"title": "$:/core/ui/ImportListing",
"text": "\\define lingo-base() $:/language/Import/\n\n\\define messageField()\nmessage-$(payloadTiddler)$\n\\end\n\n\\define selectionField()\nselection-$(payloadTiddler)$\n\\end\n\n\\define previewPopupState()\n$(currentTiddler)$!!popup-$(payloadTiddler)$\n\\end\n\n\\define select-all-actions()\n<$list filter=\"[all[current]plugintiddlers[]sort[title]]\" variable=\"payloadTiddler\">\n<$action-setfield $field={{{ [<payloadTiddler>addprefix[selection-]] }}} $value={{$:/state/import/select-all}}/>\n</$list>\n\\end\n\n<table>\n<tbody>\n<tr>\n<th>\n<$checkbox tiddler=\"$:/state/import/select-all\" field=\"text\" checked=\"checked\" unchecked=\"unchecked\" default=\"checked\" actions=<<select-all-actions>>>\n<<lingo Listing/Select/Caption>>\n</$checkbox>\n</th>\n<th>\n<<lingo Listing/Title/Caption>>\n</th>\n<th>\n<<lingo Listing/Status/Caption>>\n</th>\n</tr>\n<$list filter=\"[all[current]plugintiddlers[]sort[title]]\" variable=\"payloadTiddler\">\n<tr>\n<td>\n<$checkbox field=<<selectionField>> checked=\"checked\" unchecked=\"unchecked\" default=\"checked\"/>\n</td>\n<td>\n<$reveal type=\"nomatch\" state=<<previewPopupState>> text=\"yes\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<previewPopupState>> setTo=\"yes\">\n{{$:/core/images/right-arrow}} <$text text=<<payloadTiddler>>/>\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=<<previewPopupState>> text=\"yes\">\n<$button class=\"tc-btn-invisible tc-btn-dropdown\" set=<<previewPopupState>> setTo=\"no\">\n{{$:/core/images/down-arrow}} <$text text=<<payloadTiddler>>/>\n</$button>\n</$reveal>\n</td>\n<td>\n<$view field=<<messageField>>/>\n</td>\n</tr>\n<tr>\n<td colspan=\"3\">\n<$reveal type=\"match\" text=\"yes\" state=<<previewPopupState>>>\n<$transclude subtiddler=<<payloadTiddler>> mode=\"block\"/>\n</$reveal>\n</td>\n</tr>\n</$list>\n</tbody>\n</table>\n"
},
"$:/core/ui/ListItemTemplate": {
"title": "$:/core/ui/ListItemTemplate",
"text": "<div class=\"tc-menu-list-item\">\n<$link to={{!!title}}>\n<$view field=\"title\"/>\n</$link>\n</div>"
},
"$:/Manager/ItemMain/Fields": {
"title": "$:/Manager/ItemMain/Fields",
"tags": "$:/tags/Manager/ItemMain",
"caption": "{{$:/language/Manager/Item/Fields}}",
"text": "<table>\n<tbody>\n<$list filter=\"[all[current]fields[]sort[title]] -text\" template=\"$:/core/ui/TiddlerFieldTemplate\" variable=\"listItem\"/>\n</tbody>\n</table>\n"
},
"$:/Manager/ItemMain/RawText": {
"title": "$:/Manager/ItemMain/RawText",
"tags": "$:/tags/Manager/ItemMain",
"caption": "{{$:/language/Manager/Item/RawText}}",
"text": "<pre><code><$view/></code></pre>\n"
},
"$:/Manager/ItemMain/WikifiedText": {
"title": "$:/Manager/ItemMain/WikifiedText",
"tags": "$:/tags/Manager/ItemMain",
"caption": "{{$:/language/Manager/Item/WikifiedText}}",
"text": "<$transclude mode=\"block\"/>\n"
},
"$:/Manager/ItemSidebar/Colour": {
"title": "$:/Manager/ItemSidebar/Colour",
"tags": "$:/tags/Manager/ItemSidebar",
"caption": "{{$:/language/Manager/Item/Colour}}",
"text": "\\define swatch-styles()\nheight: 1em;\nbackground-color: $(colour)$\n\\end\n\n<$vars colour={{!!color}}>\n<p style=<<swatch-styles>>/>\n</$vars>\n<p>\n<$edit-text field=\"color\" tag=\"input\" type=\"color\"/> / <$edit-text field=\"color\" tag=\"input\" type=\"text\" size=\"9\"/>\n</p>\n"
},
"$:/Manager/ItemSidebar/Icon": {
"title": "$:/Manager/ItemSidebar/Icon",
"tags": "$:/tags/Manager/ItemSidebar",
"caption": "{{$:/language/Manager/Item/Icon}}",
"text": "<p>\n<div class=\"tc-manager-icon-editor\">\n<$button popup=<<qualify \"$:/state/popup/image-picker\">> class=\"tc-btn-invisible\">\n<$transclude tiddler={{!!icon}}>\n{{$:/language/Manager/Item/Icon/None}}\n</$transclude>\n</$button>\n<div class=\"tc-block-dropdown-wrapper\" style=\"position: static;\">\n<$reveal state=<<qualify \"$:/state/popup/image-picker\">> type=\"nomatch\" text=\"\" default=\"\" tag=\"div\" class=\"tc-popup\">\n<div class=\"tc-block-dropdown tc-popup-keep\" style=\"width: 80%; left: 10%; right: 10%; padding: 0.5em;\">\n<$macrocall $name=\"image-picker-include-tagged-images\" actions=\"\"\"\n<$action-setfield $field=\"icon\" $value=<<imageTitle>>/>\n<$action-deletetiddler $tiddler=<<qualify \"$:/state/popup/image-picker\">>/>\n\"\"\"/>\n</div>\n</$reveal>\n</div>\n</div>\n</p>\n"
},
"$:/Manager/ItemSidebar/Tags": {
"title": "$:/Manager/ItemSidebar/Tags",
"tags": "$:/tags/Manager/ItemSidebar",
"caption": "{{$:/language/Manager/Item/Tags}}",
"text": "\\define tag-checkbox-actions()\n<$action-listops\n\t$tiddler=\"$:/config/Manager/RecentTags\"\n\t$subfilter=\"[<tag>] [list[$:/config/Manager/RecentTags]] +[limit[12]]\"\n/>\n\\end\n\n\\define tag-picker-actions()\n<<tag-checkbox-actions>>\n<$action-listops\n\t$tiddler=<<currentTiddler>>\n\t$field=\"tags\"\n\t$subfilter=\"[<tag>] [all[current]tags[]]\"\n/>\n\\end\n\n<p>\n<$list filter=\"[is[current]tags[]] [list[$:/config/Manager/RecentTags]] +[sort[title]] \" variable=\"tag\">\n<div>\n<$checkbox tiddler=<<currentTiddler>> tag=<<tag>> actions=<<tag-checkbox-actions>>>\n<$macrocall $name=\"tag-pill\" tag=<<tag>>/>\n</$checkbox>\n</div>\n</$list>\n</p>\n<p>\n<$macrocall $name=\"tag-picker\" actions=<<tag-picker-actions>>/>\n</p>\n"
},
"$:/Manager/ItemSidebar/Tools": {
"title": "$:/Manager/ItemSidebar/Tools",
"tags": "$:/tags/Manager/ItemSidebar",
"caption": "{{$:/language/Manager/Item/Tools}}",
"text": "<p>\n<$button to=<<currentTiddler>>>{{$:/core/images/link}} open</$button>\n</p>\n<p>\n<$button message=\"tm-edit-tiddler\" param=<<currentTiddler>>>{{$:/core/images/edit-button}} edit</$button>\n</p>\n"
},
"$:/Manager": {
"title": "$:/Manager",
"icon": "$:/core/images/list",
"color": "#bbb",
"text": "\\define lingo-base() $:/language/Manager/\n\n\\define list-item-content-item()\n<div class=\"tc-manager-list-item-content-item\">\n\t<$vars state-title=\"\"\"$:/state/popup/manager/item/$(listItem)$\"\"\">\n\t\t<$reveal state=<<state-title>> type=\"match\" text=\"show\" default=\"show\" tag=\"div\">\n\t\t\t<$button set=<<state-title>> setTo=\"hide\" class=\"tc-btn-invisible tc-manager-list-item-content-item-heading\">\n\t\t\t\t{{$:/core/images/down-arrow}} <$transclude tiddler=<<listItem>> field=\"caption\"/>\n\t\t\t</$button>\n\t\t</$reveal>\n\t\t<$reveal state=<<state-title>> type=\"nomatch\" text=\"show\" default=\"show\" tag=\"div\">\n\t\t\t<$button set=<<state-title>> setTo=\"show\" class=\"tc-btn-invisible tc-manager-list-item-content-item-heading\">\n\t\t\t\t{{$:/core/images/right-arrow}} <$transclude tiddler=<<listItem>> field=\"caption\"/>\n\t\t\t</$button>\n\t\t</$reveal>\n\t\t<$reveal state=<<state-title>> type=\"match\" text=\"show\" default=\"show\" tag=\"div\" class=\"tc-manager-list-item-content-item-body\">\n\t\t\t<$transclude tiddler=<<listItem>>/>\n\t\t</$reveal>\n\t</$vars>\n</div>\n\\end\n\n<div class=\"tc-manager-wrapper\">\n\t<div class=\"tc-manager-controls\">\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<<lingo Controls/Show/Prompt>> <$select tiddler=\"$:/config/Manager/Show\" default=\"tiddlers\">\n\t\t\t\t<option value=\"tiddlers\"><<lingo Controls/Show/Option/Tiddlers>></option>\n\t\t\t\t<option value=\"tags\"><<lingo Controls/Show/Option/Tags>></option>\n\t\t\t</$select>\n\t\t</div>\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<<lingo Controls/Search/Prompt>> <$edit-text tiddler=\"$:/config/Manager/Filter\" tag=\"input\" default=\"\" placeholder={{$:/language/Manager/Controls/Search/Placeholder}}/>\n\t\t</div>\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<<lingo Controls/FilterByTag/Prompt>> <$select tiddler=\"$:/config/Manager/Tag\" default=\"\">\n\t\t\t\t<option value=\"\"><<lingo Controls/FilterByTag/None>></option>\n\t\t\t\t<$list filter=\"[!is{$:/config/Manager/System}tags[]!is[system]sort[title]]\" variable=\"tag\">\n\t\t\t\t\t<option value=<<tag>>><$text text=<<tag>>/></option>\n\t\t\t\t</$list>\n\t\t\t</$select>\n\t\t</div>\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<<lingo Controls/Sort/Prompt>> <$select tiddler=\"$:/config/Manager/Sort\" default=\"title\">\n\t\t\t\t<optgroup label=\"Common\">\n\t\t\t\t\t<$list filter=\"title modified modifier created creator created\" variable=\"field\">\n\t\t\t\t\t\t<option value=<<field>>><$text text=<<field>>/></option>\n\t\t\t\t\t</$list>\n\t\t\t\t</optgroup>\n\t\t\t\t<optgroup label=\"All\">\n\t\t\t\t\t<$list filter=\"[all{$:/config/Manager/Show}!is{$:/config/Manager/System}fields[]sort[title]] -title -modified -modifier -created -creator -created\" variable=\"field\">\n\t\t\t\t\t\t<option value=<<field>>><$text text=<<field>>/></option>\n\t\t\t\t\t</$list>\n\t\t\t\t</optgroup>\n\t\t\t</$select>\n\t\t\t<$checkbox tiddler=\"$:/config/Manager/Order\" field=\"text\" checked=\"reverse\" unchecked=\"forward\" default=\"forward\">\n\t\t\t\t<<lingo Controls/Order/Prompt>>\n\t\t\t</$checkbox>\n\t\t</div>\n\t\t<div class=\"tc-manager-control\">\n\t\t\t<$checkbox tiddler=\"$:/config/Manager/System\" field=\"text\" checked=\"\" unchecked=\"system\" default=\"system\">\n\t\t\t\t{{$:/language/SystemTiddlers/Include/Prompt}}\n\t\t\t</$checkbox>\n\t\t</div>\n\t</div>\n\t<div class=\"tc-manager-list\">\n\t\t<$list filter=\"[all{$:/config/Manager/Show}!is{$:/config/Manager/System}search{$:/config/Manager/Filter}tag:strict{$:/config/Manager/Tag}sort{$:/config/Manager/Sort}order{$:/config/Manager/Order}]\">\n\t\t\t<$vars transclusion=<<currentTiddler>>>\n\t\t\t\t<div style=\"tc-manager-list-item\">\n\t\t\t\t\t<$button popup=<<qualify \"$:/state/manager/popup\">> class=\"tc-btn-invisible tc-manager-list-item-heading\" selectedClass=\"tc-manager-list-item-heading-selected\">\n\t\t\t\t\t\t<$text text=<<currentTiddler>>/>\n\t\t\t\t\t</$button>\n\t\t\t\t\t<$reveal state=<<qualify \"$:/state/manager/popup\">> type=\"nomatch\" text=\"\" default=\"\" tag=\"div\" class=\"tc-manager-list-item-content tc-popup-handle\">\n\t\t\t\t\t\t<div class=\"tc-manager-list-item-content-tiddler\">\n\t\t\t\t\t\t\t<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Manager/ItemMain]!has[draft.of]]\" variable=\"listItem\">\n\t\t\t\t\t\t\t\t<<list-item-content-item>>\n\t\t\t\t\t\t\t</$list>\n\t\t\t\t\t\t</div>\n\t\t\t\t\t\t<div class=\"tc-manager-list-item-content-sidebar\">\n\t\t\t\t\t\t\t<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Manager/ItemSidebar]!has[draft.of]]\" variable=\"listItem\">\n\t\t\t\t\t\t\t\t<<list-item-content-item>>\n\t\t\t\t\t\t\t</$list>\n\t\t\t\t\t\t</div>\n\t\t\t\t\t</$reveal>\n\t\t\t\t</div>\n\t\t\t</$vars>\n\t\t</$list>\n\t</div>\n</div>\n"
},
"$:/core/ui/MissingTemplate": {
"title": "$:/core/ui/MissingTemplate",
"text": "<div class=\"tc-tiddler-missing\">\n<$button popup=<<qualify \"$:/state/popup/missing\">> class=\"tc-btn-invisible tc-missing-tiddler-label\">\n<$view field=\"title\" format=\"text\" />\n</$button>\n<$reveal state=<<qualify \"$:/state/popup/missing\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n<$transclude tiddler=\"$:/core/ui/ListItemTemplate\"/>\n<hr>\n<$list filter=\"[all[current]backlinks[]sort[title]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n</div>\n</$reveal>\n</div>\n"
},
"$:/core/ui/MoreSideBar/All": {
"title": "$:/core/ui/MoreSideBar/All",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/All/Caption}}",
"text": "<$list filter={{$:/core/Filters/AllTiddlers!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Drafts": {
"title": "$:/core/ui/MoreSideBar/Drafts",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Drafts/Caption}}",
"text": "<$list filter={{$:/core/Filters/Drafts!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Missing": {
"title": "$:/core/ui/MoreSideBar/Missing",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Missing/Caption}}",
"text": "<$list filter={{$:/core/Filters/Missing!!filter}} template=\"$:/core/ui/MissingTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Orphans": {
"title": "$:/core/ui/MoreSideBar/Orphans",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Orphans/Caption}}",
"text": "<$list filter={{$:/core/Filters/Orphans!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Plugins": {
"title": "$:/core/ui/MoreSideBar/Plugins",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/ControlPanel/Plugins/Caption}}",
"text": "\n{{$:/language/ControlPanel/Plugins/Installed/Hint}}\n\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/MoreSideBar/Plugins]!has[draft.of]]\" \"$:/core/ui/MoreSideBar/Plugins/Plugins\">>\n"
},
"$:/core/ui/MoreSideBar/Recent": {
"title": "$:/core/ui/MoreSideBar/Recent",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Recent/Caption}}",
"text": "<$macrocall $name=\"timeline\" format={{$:/language/RecentChanges/DateFormat}}/>\n"
},
"$:/core/ui/MoreSideBar/Shadows": {
"title": "$:/core/ui/MoreSideBar/Shadows",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Shadows/Caption}}",
"text": "<$list filter={{$:/core/Filters/ShadowTiddlers!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/System": {
"title": "$:/core/ui/MoreSideBar/System",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/System/Caption}}",
"text": "<$list filter={{$:/core/Filters/SystemTiddlers!!filter}} template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/MoreSideBar/Tags": {
"title": "$:/core/ui/MoreSideBar/Tags",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Tags/Caption}}",
"text": "<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"\">\n\n{{$:/core/ui/Buttons/tag-manager}}\n\n</$set>\n\n</$set>\n\n</$set>\n\n<$list filter={{$:/core/Filters/AllTags!!filter}}>\n\n<$transclude tiddler=\"$:/core/ui/TagTemplate\"/>\n\n</$list>\n\n<hr class=\"tc-untagged-separator\">\n\n{{$:/core/ui/UntaggedTemplate}}\n"
},
"$:/core/ui/MoreSideBar/Types": {
"title": "$:/core/ui/MoreSideBar/Types",
"tags": "$:/tags/MoreSideBar",
"caption": "{{$:/language/SideBar/Types/Caption}}",
"text": "<$list filter={{$:/core/Filters/TypedTiddlers!!filter}}>\n<div class=\"tc-menu-list-item\">\n<$view field=\"type\"/>\n<$list filter=\"[type{!!type}!is[system]sort[title]]\">\n<div class=\"tc-menu-list-subitem\">\n<$link to={{!!title}}><$view field=\"title\"/></$link>\n</div>\n</$list>\n</div>\n</$list>\n"
},
"$:/core/ui/MoreSideBar/Plugins/Languages": {
"title": "$:/core/ui/MoreSideBar/Plugins/Languages",
"tags": "$:/tags/MoreSideBar/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Languages/Caption}}",
"text": "<$list filter=\"[!has[draft.of]plugin-type[language]sort[description]]\" template=\"$:/core/ui/PluginListItemTemplate\" emptyMessage={{$:/language/ControlPanel/Plugins/Empty/Hint}}/>\n"
},
"$:/core/ui/MoreSideBar/Plugins/Plugins": {
"title": "$:/core/ui/MoreSideBar/Plugins/Plugins",
"tags": "$:/tags/MoreSideBar/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Plugins/Caption}}",
"text": "<$list filter=\"[!has[draft.of]plugin-type[plugin]sort[description]]\" template=\"$:/core/ui/PluginListItemTemplate\" emptyMessage={{$:/language/ControlPanel/Plugins/Empty/Hint}}>>/>\n"
},
"$:/core/ui/MoreSideBar/Plugins/Theme": {
"title": "$:/core/ui/MoreSideBar/Plugins/Theme",
"tags": "$:/tags/MoreSideBar/Plugins",
"caption": "{{$:/language/ControlPanel/Plugins/Themes/Caption}}",
"text": "<$list filter=\"[!has[draft.of]plugin-type[theme]sort[description]]\" template=\"$:/core/ui/PluginListItemTemplate\" emptyMessage={{$:/language/ControlPanel/Plugins/Empty/Hint}}/>\n"
},
"$:/core/ui/Buttons/advanced-search": {
"title": "$:/core/ui/Buttons/advanced-search",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/advanced-search-button}} {{$:/language/Buttons/AdvancedSearch/Caption}}",
"description": "{{$:/language/Buttons/AdvancedSearch/Hint}}",
"text": "\\define control-panel-button(class)\n<$button to=\"$:/AdvancedSearch\" tooltip={{$:/language/Buttons/AdvancedSearch/Hint}} aria-label={{$:/language/Buttons/AdvancedSearch/Caption}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/advanced-search-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/AdvancedSearch/Caption}}/></span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/AdvancedSearch]]\" emptyMessage=<<control-panel-button>>>\n<<control-panel-button \"tc-selected\">>\n</$list>\n"
},
"$:/core/ui/Buttons/close-all": {
"title": "$:/core/ui/Buttons/close-all",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/close-all-button}} {{$:/language/Buttons/CloseAll/Caption}}",
"description": "{{$:/language/Buttons/CloseAll/Hint}}",
"text": "<$button message=\"tm-close-all-tiddlers\" tooltip={{$:/language/Buttons/CloseAll/Hint}} aria-label={{$:/language/Buttons/CloseAll/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/close-all-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/CloseAll/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/control-panel": {
"title": "$:/core/ui/Buttons/control-panel",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/options-button}} {{$:/language/Buttons/ControlPanel/Caption}}",
"description": "{{$:/language/Buttons/ControlPanel/Hint}}",
"text": "\\define control-panel-button(class)\n<$button to=\"$:/ControlPanel\" tooltip={{$:/language/Buttons/ControlPanel/Hint}} aria-label={{$:/language/Buttons/ControlPanel/Caption}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/options-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/ControlPanel/Caption}}/></span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/ControlPanel]]\" emptyMessage=<<control-panel-button>>>\n<<control-panel-button \"tc-selected\">>\n</$list>\n"
},
"$:/core/ui/Buttons/encryption": {
"title": "$:/core/ui/Buttons/encryption",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/locked-padlock}} {{$:/language/Buttons/Encryption/Caption}}",
"description": "{{$:/language/Buttons/Encryption/Hint}}",
"text": "<$reveal type=\"match\" state=\"$:/isEncrypted\" text=\"yes\">\n<$button message=\"tm-clear-password\" tooltip={{$:/language/Buttons/Encryption/ClearPassword/Hint}} aria-label={{$:/language/Buttons/Encryption/ClearPassword/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/locked-padlock}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Encryption/ClearPassword/Caption}}/></span>\n</$list>\n</$button>\n</$reveal>\n<$reveal type=\"nomatch\" state=\"$:/isEncrypted\" text=\"yes\">\n<$button message=\"tm-set-password\" tooltip={{$:/language/Buttons/Encryption/SetPassword/Hint}} aria-label={{$:/language/Buttons/Encryption/SetPassword/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/unlocked-padlock}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Encryption/SetPassword/Caption}}/></span>\n</$list>\n</$button>\n</$reveal>"
},
"$:/core/ui/Buttons/export-page": {
"title": "$:/core/ui/Buttons/export-page",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/export-button}} {{$:/language/Buttons/ExportPage/Caption}}",
"description": "{{$:/language/Buttons/ExportPage/Hint}}",
"text": "<$macrocall $name=\"exportButton\" exportFilter=\"[!is[system]sort[title]]\" lingoBase=\"$:/language/Buttons/ExportPage/\"/>"
},
"$:/core/ui/Buttons/fold-all": {
"title": "$:/core/ui/Buttons/fold-all",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/fold-all-button}} {{$:/language/Buttons/FoldAll/Caption}}",
"description": "{{$:/language/Buttons/FoldAll/Hint}}",
"text": "<$button tooltip={{$:/language/Buttons/FoldAll/Hint}} aria-label={{$:/language/Buttons/FoldAll/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-fold-all-tiddlers\" $param=<<currentTiddler>> foldedStatePrefix=\"$:/state/folded/\"/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\" variable=\"listItem\">\n{{$:/core/images/fold-all-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/FoldAll/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/full-screen": {
"title": "$:/core/ui/Buttons/full-screen",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/full-screen-button}} {{$:/language/Buttons/FullScreen/Caption}}",
"description": "{{$:/language/Buttons/FullScreen/Hint}}",
"text": "<$button message=\"tm-full-screen\" tooltip={{$:/language/Buttons/FullScreen/Hint}} aria-label={{$:/language/Buttons/FullScreen/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/full-screen-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/FullScreen/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/home": {
"title": "$:/core/ui/Buttons/home",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/home-button}} {{$:/language/Buttons/Home/Caption}}",
"description": "{{$:/language/Buttons/Home/Hint}}",
"text": "<$button message=\"tm-home\" tooltip={{$:/language/Buttons/Home/Hint}} aria-label={{$:/language/Buttons/Home/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/home-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Home/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/import": {
"title": "$:/core/ui/Buttons/import",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/import-button}} {{$:/language/Buttons/Import/Caption}}",
"description": "{{$:/language/Buttons/Import/Hint}}",
"text": "<div class=\"tc-file-input-wrapper\">\n<$button tooltip={{$:/language/Buttons/Import/Hint}} aria-label={{$:/language/Buttons/Import/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/import-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Import/Caption}}/></span>\n</$list>\n</$button>\n<$browse tooltip={{$:/language/Buttons/Import/Hint}}/>\n</div>"
},
"$:/core/ui/Buttons/language": {
"title": "$:/core/ui/Buttons/language",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/globe}} {{$:/language/Buttons/Language/Caption}}",
"description": "{{$:/language/Buttons/Language/Hint}}",
"text": "\\define flag-title()\n$(languagePluginTitle)$/icon\n\\end\n<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/language\">> tooltip={{$:/language/Buttons/Language/Hint}} aria-label={{$:/language/Buttons/Language/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n<span class=\"tc-image-button\">\n<$set name=\"languagePluginTitle\" value={{$:/language}}>\n<$image source=<<flag-title>>/>\n</$set>\n</span>\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Language/Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/language\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down tc-drop-down-language-chooser\">\n<$linkcatcher to=\"$:/language\">\n<$list filter=\"[[$:/languages/en-GB]] [plugin-type[language]sort[description]]\">\n<$link>\n<span class=\"tc-drop-down-bullet\">\n<$reveal type=\"match\" state=\"$:/language\" text=<<currentTiddler>>>\n•\n</$reveal>\n<$reveal type=\"nomatch\" state=\"$:/language\" text=<<currentTiddler>>>\n \n</$reveal>\n</span>\n<span class=\"tc-image-button\">\n<$set name=\"languagePluginTitle\" value=<<currentTiddler>>>\n<$transclude subtiddler=<<flag-title>>>\n<$list filter=\"[all[current]field:title[$:/languages/en-GB]]\">\n<$transclude tiddler=\"$:/languages/en-GB/icon\"/>\n</$list>\n</$transclude>\n</$set>\n</span>\n<$view field=\"description\">\n<$view field=\"name\">\n<$view field=\"title\"/>\n</$view>\n</$view>\n</$link>\n</$list>\n</$linkcatcher>\n</div>\n</$reveal>"
},
"$:/core/ui/Buttons/manager": {
"title": "$:/core/ui/Buttons/manager",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/list}} {{$:/language/Buttons/Manager/Caption}}",
"description": "{{$:/language/Buttons/Manager/Hint}}",
"text": "\\define manager-button(class)\n<$button to=\"$:/Manager\" tooltip={{$:/language/Buttons/Manager/Hint}} aria-label={{$:/language/Buttons/Manager/Caption}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/list}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Manager/Caption}}/></span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/Manager]]\" emptyMessage=<<manager-button>>>\n<<manager-button \"tc-selected\">>\n</$list>\n"
},
"$:/core/ui/Buttons/more-page-actions": {
"title": "$:/core/ui/Buttons/more-page-actions",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/down-arrow}} {{$:/language/Buttons/More/Caption}}",
"description": "{{$:/language/Buttons/More/Hint}}",
"text": "\\define config-title()\n$:/config/PageControlButtons/Visibility/$(listItem)$\n\\end\n<$button popup=<<qualify \"$:/state/popup/more\">> tooltip={{$:/language/Buttons/More/Hint}} aria-label={{$:/language/Buttons/More/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/down-arrow}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/More/Caption}}/></span>\n</$list>\n</$button><$reveal state=<<qualify \"$:/state/popup/more\">> type=\"popup\" position=\"below\" animate=\"yes\">\n\n<div class=\"tc-drop-down\">\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"tc-btn-invisible\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/PageControls]!has[draft.of]] -[[$:/core/ui/Buttons/more-page-actions]]\" variable=\"listItem\">\n\n<$reveal type=\"match\" state=<<config-title>> text=\"hide\">\n\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n\n</$reveal>\n\n</$list>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</div>\n\n</$reveal>"
},
"$:/core/ui/Buttons/new-image": {
"title": "$:/core/ui/Buttons/new-image",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/new-image-button}} {{$:/language/Buttons/NewImage/Caption}}",
"description": "{{$:/language/Buttons/NewImage/Hint}}",
"text": "<$button tooltip={{$:/language/Buttons/NewImage/Hint}} aria-label={{$:/language/Buttons/NewImage/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-new-tiddler\" type=\"image/jpeg\"/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/new-image-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewImage/Caption}}/></span>\n</$list>\n</$button>\n"
},
"$:/core/ui/Buttons/new-journal": {
"title": "$:/core/ui/Buttons/new-journal",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/new-journal-button}} {{$:/language/Buttons/NewJournal/Caption}}",
"description": "{{$:/language/Buttons/NewJournal/Hint}}",
"text": "\\define journalButton()\n<$button tooltip={{$:/language/Buttons/NewJournal/Hint}} aria-label={{$:/language/Buttons/NewJournal/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-new-tiddler\" title=<<now \"$(journalTitleTemplate)$\">> tags=\"$(journalTags)$\" text=\"$(journalText)$\"/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/new-journal-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewJournal/Caption}}/></span>\n</$list>\n</$button>\n\\end\n<$set name=\"journalTitleTemplate\" value={{$:/config/NewJournal/Title}}>\n<$set name=\"journalTags\" value={{$:/config/NewJournal/Tags}}>\n<$set name=\"journalText\" value={{$:/config/NewJournal/Text}}>\n<<journalButton>>\n</$set></$set></$set>"
},
"$:/core/ui/Buttons/new-tiddler": {
"title": "$:/core/ui/Buttons/new-tiddler",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/new-button}} {{$:/language/Buttons/NewTiddler/Caption}}",
"description": "{{$:/language/Buttons/NewTiddler/Hint}}",
"text": "<$button message=\"tm-new-tiddler\" tooltip={{$:/language/Buttons/NewTiddler/Hint}} aria-label={{$:/language/Buttons/NewTiddler/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/new-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewTiddler/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/palette": {
"title": "$:/core/ui/Buttons/palette",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/palette}} {{$:/language/Buttons/Palette/Caption}}",
"description": "{{$:/language/Buttons/Palette/Hint}}",
"text": "<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/palette\">> tooltip={{$:/language/Buttons/Palette/Hint}} aria-label={{$:/language/Buttons/Palette/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/palette}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Palette/Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/palette\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\" style=\"font-size:0.7em;\">\n{{$:/snippets/paletteswitcher}}\n</div>\n</$reveal>"
},
"$:/core/ui/Buttons/print": {
"title": "$:/core/ui/Buttons/print",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/print-button}} {{$:/language/Buttons/Print/Caption}}",
"description": "{{$:/language/Buttons/Print/Hint}}",
"text": "<$button message=\"tm-print\" tooltip={{$:/language/Buttons/Print/Hint}} aria-label={{$:/language/Buttons/Print/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/print-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Print/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/refresh": {
"title": "$:/core/ui/Buttons/refresh",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/refresh-button}} {{$:/language/Buttons/Refresh/Caption}}",
"description": "{{$:/language/Buttons/Refresh/Hint}}",
"text": "<$button message=\"tm-browser-refresh\" tooltip={{$:/language/Buttons/Refresh/Hint}} aria-label={{$:/language/Buttons/Refresh/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/refresh-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Refresh/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/save-wiki": {
"title": "$:/core/ui/Buttons/save-wiki",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/save-button}} {{$:/language/Buttons/SaveWiki/Caption}}",
"description": "{{$:/language/Buttons/SaveWiki/Hint}}",
"text": "<$button message=\"tm-save-wiki\" param={{$:/config/SaveWikiButton/Template}} tooltip={{$:/language/Buttons/SaveWiki/Hint}} aria-label={{$:/language/Buttons/SaveWiki/Caption}} class=<<tv-config-toolbar-class>>>\n<span class=\"tc-dirty-indicator\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/save-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/SaveWiki/Caption}}/></span>\n</$list>\n</span>\n</$button>"
},
"$:/core/ui/Buttons/storyview": {
"title": "$:/core/ui/Buttons/storyview",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/storyview-classic}} {{$:/language/Buttons/StoryView/Caption}}",
"description": "{{$:/language/Buttons/StoryView/Hint}}",
"text": "\\define icon()\n$:/core/images/storyview-$(storyview)$\n\\end\n<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/storyview\">> tooltip={{$:/language/Buttons/StoryView/Hint}} aria-label={{$:/language/Buttons/StoryView/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n<$set name=\"storyview\" value={{$:/view}}>\n<$transclude tiddler=<<icon>>/>\n</$set>\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/StoryView/Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/storyview\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n<$linkcatcher to=\"$:/view\">\n<$list filter=\"[storyviews[]]\" variable=\"storyview\">\n<$link to=<<storyview>>>\n<span class=\"tc-drop-down-bullet\">\n<$reveal type=\"match\" state=\"$:/view\" text=<<storyview>>>\n•\n</$reveal>\n<$reveal type=\"nomatch\" state=\"$:/view\" text=<<storyview>>>\n \n</$reveal>\n</span>\n<$transclude tiddler=<<icon>>/>\n<$text text=<<storyview>>/></$link>\n</$list>\n</$linkcatcher>\n</div>\n</$reveal>"
},
"$:/core/ui/Buttons/tag-manager": {
"title": "$:/core/ui/Buttons/tag-manager",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/tag-button}} {{$:/language/Buttons/TagManager/Caption}}",
"description": "{{$:/language/Buttons/TagManager/Hint}}",
"text": "\\define control-panel-button(class)\n<$button to=\"$:/TagManager\" tooltip={{$:/language/Buttons/TagManager/Hint}} aria-label={{$:/language/Buttons/TagManager/Caption}} class=\"\"\"$(tv-config-toolbar-class)$ $class$\"\"\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/tag-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/TagManager/Caption}}/></span>\n</$list>\n</$button>\n\\end\n\n<$list filter=\"[list[$:/StoryList]] +[field:title[$:/TagManager]]\" emptyMessage=<<control-panel-button>>>\n<<control-panel-button \"tc-selected\">>\n</$list>\n"
},
"$:/core/ui/Buttons/theme": {
"title": "$:/core/ui/Buttons/theme",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/theme-button}} {{$:/language/Buttons/Theme/Caption}}",
"description": "{{$:/language/Buttons/Theme/Hint}}",
"text": "<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/theme\">> tooltip={{$:/language/Buttons/Theme/Hint}} aria-label={{$:/language/Buttons/Theme/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/theme-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Theme/Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/theme\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n<$linkcatcher to=\"$:/theme\">\n<$list filter=\"[plugin-type[theme]sort[title]]\" variable=\"themeTitle\">\n<$link to=<<themeTitle>>>\n<span class=\"tc-drop-down-bullet\">\n<$reveal type=\"match\" state=\"$:/theme\" text=<<themeTitle>>>\n•\n</$reveal>\n<$reveal type=\"nomatch\" state=\"$:/theme\" text=<<themeTitle>>>\n \n</$reveal>\n</span>\n<$view tiddler=<<themeTitle>> field=\"name\"/>\n</$link>\n</$list>\n</$linkcatcher>\n</div>\n</$reveal>"
},
"$:/core/ui/Buttons/timestamp": {
"title": "$:/core/ui/Buttons/timestamp",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/timestamp-on}} {{$:/language/Buttons/Timestamp/Caption}}",
"description": "{{$:/language/Buttons/Timestamp/Hint}}",
"text": "<$reveal type=\"nomatch\" state=\"$:/config/TimestampDisable\" text=\"yes\">\n<$button tooltip={{$:/language/Buttons/Timestamp/On/Hint}} aria-label={{$:/language/Buttons/Timestamp/On/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-setfield $tiddler=\"$:/config/TimestampDisable\" $value=\"yes\"/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/timestamp-on}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Timestamp/On/Caption}}/></span>\n</$list>\n</$button>\n</$reveal>\n<$reveal type=\"match\" state=\"$:/config/TimestampDisable\" text=\"yes\">\n<$button tooltip={{$:/language/Buttons/Timestamp/Off/Hint}} aria-label={{$:/language/Buttons/Timestamp/Off/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-setfield $tiddler=\"$:/config/TimestampDisable\" $value=\"no\"/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/timestamp-off}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Timestamp/Off/Caption}}/></span>\n</$list>\n</$button>\n</$reveal>"
},
"$:/core/ui/Buttons/unfold-all": {
"title": "$:/core/ui/Buttons/unfold-all",
"tags": "$:/tags/PageControls",
"caption": "{{$:/core/images/unfold-all-button}} {{$:/language/Buttons/UnfoldAll/Caption}}",
"description": "{{$:/language/Buttons/UnfoldAll/Hint}}",
"text": "<$button tooltip={{$:/language/Buttons/UnfoldAll/Hint}} aria-label={{$:/language/Buttons/UnfoldAll/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-unfold-all-tiddlers\" $param=<<currentTiddler>> foldedStatePrefix=\"$:/state/folded/\"/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\" variable=\"listItem\">\n{{$:/core/images/unfold-all-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/UnfoldAll/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/PageTemplate/pagecontrols": {
"title": "$:/core/ui/PageTemplate/pagecontrols",
"text": "\\define config-title()\n$:/config/PageControlButtons/Visibility/$(listItem)$\n\\end\n<div class=\"tc-page-controls\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/PageControls]!has[draft.of]]\" variable=\"listItem\">\n<$reveal type=\"nomatch\" state=<<config-title>> text=\"hide\">\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n</$reveal>\n</$list>\n</div>\n\n"
},
"$:/core/ui/PageStylesheet": {
"title": "$:/core/ui/PageStylesheet",
"text": "<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\">\n\n<$set name=\"currentTiddler\" value={{$:/language}}>\n\n<$set name=\"languageTitle\" value={{!!name}}>\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Stylesheet]!has[draft.of]]\">\n<$transclude mode=\"block\"/>\n</$list>\n\n</$set>\n\n</$set>\n\n</$importvariables>\n"
},
"$:/core/ui/PageTemplate/alerts": {
"title": "$:/core/ui/PageTemplate/alerts",
"tags": "$:/tags/PageTemplate",
"text": "<div class=\"tc-alerts\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Alert]!has[draft.of]]\" template=\"$:/core/ui/AlertTemplate\" storyview=\"pop\"/>\n\n</div>\n"
},
"$:/core/ui/PageTemplate/pluginreloadwarning": {
"title": "$:/core/ui/PageTemplate/pluginreloadwarning",
"tags": "$:/tags/PageTemplate",
"text": "\\define lingo-base() $:/language/\n\n<$list filter=\"[has[plugin-type]haschanged[]!plugin-type[import]limit[1]]\">\n\n<$reveal type=\"nomatch\" state=\"$:/temp/HidePluginWarning\" text=\"yes\">\n\n<div class=\"tc-plugin-reload-warning\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"\">\n\n<<lingo PluginReloadWarning>> <$button set=\"$:/temp/HidePluginWarning\" setTo=\"yes\" class=\"tc-btn-invisible\">{{$:/core/images/close-button}}</$button>\n\n</$set>\n\n</div>\n\n</$reveal>\n\n</$list>\n"
},
"$:/core/ui/PageTemplate/sidebar": {
"title": "$:/core/ui/PageTemplate/sidebar",
"tags": "$:/tags/PageTemplate",
"text": "<$scrollable fallthrough=\"no\" class=\"tc-sidebar-scrollable\">\n\n<div class=\"tc-sidebar-header\">\n\n<$reveal state=\"$:/state/sidebar\" type=\"match\" text=\"yes\" default=\"yes\" retain=\"yes\" animate=\"yes\">\n\n<h1 class=\"tc-site-title\">\n\n<$transclude tiddler=\"$:/SiteTitle\" mode=\"inline\"/>\n\n</h1>\n\n<div class=\"tc-site-subtitle\">\n\n<$transclude tiddler=\"$:/SiteSubtitle\" mode=\"inline\"/>\n\n</div>\n\n{{||$:/core/ui/PageTemplate/pagecontrols}}\n\n<$transclude tiddler=\"$:/core/ui/SideBarLists\" mode=\"inline\"/>\n\n</$reveal>\n\n</div>\n\n</$scrollable>"
},
"$:/core/ui/PageTemplate/story": {
"title": "$:/core/ui/PageTemplate/story",
"tags": "$:/tags/PageTemplate",
"text": "<section class=\"tc-story-river\">\n\n<section class=\"story-backdrop\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/AboveStory]!has[draft.of]]\">\n\n<$transclude/>\n\n</$list>\n\n</section>\n\n<$list filter=\"[list[$:/StoryList]]\" history=\"$:/HistoryList\" template=\"$:/core/ui/ViewTemplate\" editTemplate=\"$:/core/ui/EditTemplate\" storyview={{$:/view}} emptyMessage={{$:/config/EmptyStoryMessage}}/>\n\n<section class=\"story-frontdrop\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/BelowStory]!has[draft.of]]\">\n\n<$transclude/>\n\n</$list>\n\n</section>\n\n</section>\n"
},
"$:/core/ui/PageTemplate/topleftbar": {
"title": "$:/core/ui/PageTemplate/topleftbar",
"tags": "$:/tags/PageTemplate",
"text": "<span class=\"tc-topbar tc-topbar-left\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TopLeftBar]!has[draft.of]]\" variable=\"listItem\">\n\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n\n</$list>\n\n</span>\n"
},
"$:/core/ui/PageTemplate/toprightbar": {
"title": "$:/core/ui/PageTemplate/toprightbar",
"tags": "$:/tags/PageTemplate",
"text": "<span class=\"tc-topbar tc-topbar-right\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TopRightBar]!has[draft.of]]\" variable=\"listItem\">\n\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n\n</$list>\n\n</span>\n"
},
"$:/core/ui/PageTemplate": {
"title": "$:/core/ui/PageTemplate",
"text": "\\define containerClasses()\ntc-page-container tc-page-view-$(themeTitle)$ tc-language-$(languageTitle)$\n\\end\n\n<$importvariables filter=\"[[$:/core/ui/PageMacros]] [all[shadows+tiddlers]tag[$:/tags/Macro]!has[draft.of]]\">\n\n<$set name=\"tv-config-toolbar-icons\" value={{$:/config/Toolbar/Icons}}>\n\n<$set name=\"tv-config-toolbar-text\" value={{$:/config/Toolbar/Text}}>\n\n<$set name=\"tv-config-toolbar-class\" value={{$:/config/Toolbar/ButtonClass}}>\n\n<$set name=\"themeTitle\" value={{$:/view}}>\n\n<$set name=\"currentTiddler\" value={{$:/language}}>\n\n<$set name=\"languageTitle\" value={{!!name}}>\n\n<$set name=\"currentTiddler\" value=\"\">\n\n<div class=<<containerClasses>>>\n\n<$navigator story=\"$:/StoryList\" history=\"$:/HistoryList\" openLinkFromInsideRiver={{$:/config/Navigation/openLinkFromInsideRiver}} openLinkFromOutsideRiver={{$:/config/Navigation/openLinkFromOutsideRiver}} relinkOnRename={{$:/config/RelinkOnRename}}>\n\n<$dropzone>\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/PageTemplate]!has[draft.of]]\" variable=\"listItem\">\n\n<$transclude tiddler=<<listItem>>/>\n\n</$list>\n\n</$dropzone>\n\n</$navigator>\n\n</div>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</$importvariables>\n"
},
"$:/core/ui/PluginInfo": {
"title": "$:/core/ui/PluginInfo",
"text": "\\define localised-info-tiddler-title()\n$(currentTiddler)$/$(languageTitle)$/$(currentTab)$\n\\end\n\\define info-tiddler-title()\n$(currentTiddler)$/$(currentTab)$\n\\end\n\\define default-tiddler-title()\n$:/core/ui/PluginInfo/Default/$(currentTab)$\n\\end\n<$transclude tiddler=<<localised-info-tiddler-title>> mode=\"block\">\n<$transclude tiddler=<<currentTiddler>> subtiddler=<<localised-info-tiddler-title>> mode=\"block\">\n<$transclude tiddler=<<currentTiddler>> subtiddler=<<info-tiddler-title>> mode=\"block\">\n<$transclude tiddler=<<default-tiddler-title>> mode=\"block\">\n{{$:/language/ControlPanel/Plugin/NoInfoFound/Hint}}\n</$transclude>\n</$transclude>\n</$transclude>\n</$transclude>\n"
},
"$:/core/ui/PluginInfo/Default/contents": {
"title": "$:/core/ui/PluginInfo/Default/contents",
"text": "\\define lingo-base() $:/language/TiddlerInfo/Advanced/PluginInfo/\n<<lingo Hint>>\n<ul>\n<$list filter=\"[all[current]plugintiddlers[]sort[title]]\" emptyMessage=<<lingo Empty/Hint>>>\n<li>\n<$link to={{!!title}}>\n<$view field=\"title\"/>\n</$link>\n</li>\n</$list>\n</ul>\n"
},
"$:/core/ui/PluginListItemTemplate": {
"title": "$:/core/ui/PluginListItemTemplate",
"text": "<div class=\"tc-menu-list-item\">\n<$link to={{!!title}}>\n<$view field=\"description\">\n<$view field=\"title\"/>\n</$view>\n</$link>\n</div>"
},
"$:/core/ui/SearchResults": {
"title": "$:/core/ui/SearchResults",
"text": "<div class=\"tc-search-results\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]butfirst[]limit[1]]\" emptyMessage=\"\"\"\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]]\">\n<$transclude mode=\"block\"/>\n</$list>\n\"\"\">\n\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/SearchResults]!has[draft.of]]\" default={{$:/config/SearchResults/Default}}/>\n\n</$list>\n\n</div>\n"
},
"$:/core/ui/SideBar/More": {
"title": "$:/core/ui/SideBar/More",
"tags": "$:/tags/SideBar",
"caption": "{{$:/language/SideBar/More/Caption}}",
"text": "<div class=\"tc-more-sidebar\">\n<<tabs \"[all[shadows+tiddlers]tag[$:/tags/MoreSideBar]!has[draft.of]]\" \"$:/core/ui/MoreSideBar/Tags\" \"$:/state/tab/moresidebar\" \"tc-vertical\">>\n</div>\n"
},
"$:/core/ui/SideBar/Open": {
"title": "$:/core/ui/SideBar/Open",
"tags": "$:/tags/SideBar",
"caption": "{{$:/language/SideBar/Open/Caption}}",
"text": "\\define lingo-base() $:/language/CloseAll/\n\n\\define drop-actions()\n<$action-listops $tiddler=\"$:/StoryList\" $subfilter=\"+[insertbefore:currentTiddler<actionTiddler>]\"/>\n\\end\n\n<$list filter=\"[list[$:/StoryList]]\" history=\"$:/HistoryList\" storyview=\"pop\">\n<div style=\"position: relative;\">\n<$droppable actions=<<drop-actions>>>\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n<div>\n<$button message=\"tm-close-tiddler\" tooltip={{$:/language/Buttons/Close/Hint}} aria-label={{$:/language/Buttons/Close/Caption}} class=\"tc-btn-invisible tc-btn-mini\">×</$button> <$link to={{!!title}}><$view field=\"title\"/></$link>\n</div>\n</$droppable>\n</div>\n</$list>\n<$tiddler tiddler=\"\">\n<$droppable actions=<<drop-actions>>>\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n<$button message=\"tm-close-all-tiddlers\" class=\"tc-btn-invisible tc-btn-mini\"><<lingo Button>></$button>\n</$droppable>\n</$tiddler>\n"
},
"$:/core/ui/SideBar/Recent": {
"title": "$:/core/ui/SideBar/Recent",
"tags": "$:/tags/SideBar",
"caption": "{{$:/language/SideBar/Recent/Caption}}",
"text": "<$macrocall $name=\"timeline\" format={{$:/language/RecentChanges/DateFormat}}/>\n"
},
"$:/core/ui/SideBar/Tools": {
"title": "$:/core/ui/SideBar/Tools",
"tags": "$:/tags/SideBar",
"caption": "{{$:/language/SideBar/Tools/Caption}}",
"text": "\\define lingo-base() $:/language/ControlPanel/\n\\define config-title()\n$:/config/PageControlButtons/Visibility/$(listItem)$\n\\end\n\n<<lingo Basics/Version/Prompt>> <<version>>\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/PageControls]!has[draft.of]]\" variable=\"listItem\">\n\n<div style=\"position:relative;\">\n\n<$checkbox tiddler=<<config-title>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"show\"/> <$transclude tiddler=<<listItem>>/> <i class=\"tc-muted\"><$transclude tiddler=<<listItem>> field=\"description\"/></i>\n\n</div>\n\n</$list>\n\n</$set>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/SideBarLists": {
"title": "$:/core/ui/SideBarLists",
"text": "<div class=\"tc-sidebar-lists\">\n\n<$set name=\"searchTiddler\" value=\"$:/temp/search\">\n<div class=\"tc-search\">\n<$edit-text tiddler=\"$:/temp/search\" type=\"search\" tag=\"input\" focus={{$:/config/Search/AutoFocus}} focusPopup=<<qualify \"$:/state/popup/search-dropdown\">> class=\"tc-popup-handle\"/>\n<$reveal state=\"$:/temp/search\" type=\"nomatch\" text=\"\">\n<$button tooltip={{$:/language/Buttons/AdvancedSearch/Hint}} aria-label={{$:/language/Buttons/AdvancedSearch/Caption}} class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/advancedsearch\" text={{$:/temp/search}}/>\n<$action-setfield $tiddler=\"$:/temp/search\" text=\"\"/>\n<$action-navigate $to=\"$:/AdvancedSearch\"/>\n{{$:/core/images/advanced-search-button}}\n</$button>\n<$button class=\"tc-btn-invisible\">\n<$action-setfield $tiddler=\"$:/temp/search\" text=\"\" />\n{{$:/core/images/close-button}}\n</$button>\n<$button popup=<<qualify \"$:/state/popup/search-dropdown\">> class=\"tc-btn-invisible\">\n{{$:/core/images/down-arrow}}\n<$list filter=\"[{$:/temp/search}minlength{$:/config/Search/MinLength}limit[1]]\" variable=\"listItem\">\n<$set name=\"resultCount\" value=\"\"\"<$count filter=\"[!is[system]search{$(searchTiddler)$}]\"/>\"\"\">\n{{$:/language/Search/Matches}}\n</$set>\n</$list>\n</$button>\n</$reveal>\n<$reveal state=\"$:/temp/search\" type=\"match\" text=\"\">\n<$button to=\"$:/AdvancedSearch\" tooltip={{$:/language/Buttons/AdvancedSearch/Hint}} aria-label={{$:/language/Buttons/AdvancedSearch/Caption}} class=\"tc-btn-invisible\">\n{{$:/core/images/advanced-search-button}}\n</$button>\n</$reveal>\n</div>\n\n<$reveal tag=\"div\" class=\"tc-block-dropdown-wrapper\" state=\"$:/temp/search\" type=\"nomatch\" text=\"\">\n\n<$reveal tag=\"div\" class=\"tc-block-dropdown tc-search-drop-down tc-popup-handle\" state=<<qualify \"$:/state/popup/search-dropdown\">> type=\"nomatch\" text=\"\" default=\"\">\n\n<$list filter=\"[{$:/temp/search}minlength{$:/config/Search/MinLength}limit[1]]\" emptyMessage=\"\"\"<div class=\"tc-search-results\">{{$:/language/Search/Search/TooShort}}</div>\"\"\" variable=\"listItem\">\n\n{{$:/core/ui/SearchResults}}\n\n</$list>\n\n</$reveal>\n\n</$reveal>\n\n</$set>\n\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/SideBar]!has[draft.of]]\" default={{$:/config/DefaultSidebarTab}} state=\"$:/state/tab/sidebar\" />\n\n</div>\n"
},
"$:/TagManager": {
"title": "$:/TagManager",
"icon": "$:/core/images/tag-button",
"color": "#bbb",
"caption": "{{$:/language/TagManager/Caption}}",
"text": "\\define lingo-base() $:/language/TagManager/\n\\define iconEditorTab(type)\n<$list filter=\"[all[shadows+tiddlers]is[image]] [all[shadows+tiddlers]tag[$:/tags/Image]] -[type[application/pdf]] +[sort[title]] +[$type$is[system]]\">\n<$link to={{!!title}}>\n<$transclude/> <$view field=\"title\"/>\n</$link>\n</$list>\n\\end\n\\define iconEditor(title)\n<div class=\"tc-drop-down-wrapper\">\n<$button popup=<<qualify \"$:/state/popup/icon/$title$\">> class=\"tc-btn-invisible tc-btn-dropdown\">{{$:/core/images/down-arrow}}</$button>\n<$reveal state=<<qualify \"$:/state/popup/icon/$title$\">> type=\"popup\" position=\"belowleft\" text=\"\" default=\"\">\n<div class=\"tc-drop-down\">\n<$linkcatcher to=\"$title$!!icon\">\n<<iconEditorTab type:\"!\">>\n<hr/>\n<<iconEditorTab type:\"\">>\n</$linkcatcher>\n</div>\n</$reveal>\n</div>\n\\end\n\\define qualifyTitle(title)\n$title$$(currentTiddler)$\n\\end\n\\define toggleButton(state)\n<$reveal state=\"$state$\" type=\"match\" text=\"closed\" default=\"closed\">\n<$button set=\"$state$\" setTo=\"open\" class=\"tc-btn-invisible tc-btn-dropdown\" selectedClass=\"tc-selected\">\n{{$:/core/images/info-button}}\n</$button>\n</$reveal>\n<$reveal state=\"$state$\" type=\"match\" text=\"open\" default=\"closed\">\n<$button set=\"$state$\" setTo=\"closed\" class=\"tc-btn-invisible tc-btn-dropdown\" selectedClass=\"tc-selected\">\n{{$:/core/images/info-button}}\n</$button>\n</$reveal>\n\\end\n<table class=\"tc-tag-manager-table\">\n<tbody>\n<tr>\n<th><<lingo Colour/Heading>></th>\n<th class=\"tc-tag-manager-tag\"><<lingo Tag/Heading>></th>\n<th><<lingo Count/Heading>></th>\n<th><<lingo Icon/Heading>></th>\n<th><<lingo Info/Heading>></th>\n</tr>\n<$list filter=\"[tags[]!is[system]sort[title]]\">\n<tr>\n<td><$edit-text field=\"color\" tag=\"input\" type=\"color\"/></td>\n<td><$macrocall $name=\"tag\" tag=<<currentTiddler>>/></td>\n<td><$count filter=\"[all[current]tagging[]]\"/></td>\n<td>\n<$macrocall $name=\"iconEditor\" title={{!!title}}/>\n</td>\n<td>\n<$macrocall $name=\"toggleButton\" state=<<qualifyTitle \"$:/state/tag-manager/\">> /> \n</td>\n</tr>\n<tr>\n<td></td>\n<td colspan=\"4\">\n<$reveal state=<<qualifyTitle \"$:/state/tag-manager/\">> type=\"match\" text=\"open\" default=\"\">\n<table>\n<tbody>\n<tr><td><<lingo Colour/Heading>></td><td><$edit-text field=\"color\" tag=\"input\" type=\"text\" size=\"9\"/></td></tr>\n<tr><td><<lingo Icon/Heading>></td><td><$edit-text field=\"icon\" tag=\"input\" size=\"45\"/></td></tr>\n</tbody>\n</table>\n</$reveal>\n</td>\n</tr>\n</$list>\n<tr>\n<td></td>\n<td>\n{{$:/core/ui/UntaggedTemplate}}\n</td>\n<td>\n<small class=\"tc-menu-list-count\"><$count filter=\"[untagged[]!is[system]] -[tags[]]\"/></small>\n</td>\n<td></td>\n<td></td>\n</tr>\n</tbody>\n</table>\n"
},
"$:/core/ui/TagTemplate": {
"title": "$:/core/ui/TagTemplate",
"text": "<span class=\"tc-tag-list-item\">\n<$set name=\"transclusion\" value=<<currentTiddler>>>\n<$macrocall $name=\"tag-pill-body\" tag=<<currentTiddler>> icon={{!!icon}} colour={{!!color}} palette={{$:/palette}} element-tag=\"\"\"$button\"\"\" element-attributes=\"\"\"popup=<<qualify \"$:/state/popup/tag\">> dragFilter='[all[current]tagging[]]' tag='span'\"\"\"/>\n<$reveal state=<<qualify \"$:/state/popup/tag\">> type=\"popup\" position=\"below\" animate=\"yes\" class=\"tc-drop-down\">\n<$transclude tiddler=\"$:/core/ui/ListItemTemplate\"/>\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TagDropdown]!has[draft.of]]\" variable=\"listItem\"> \n<$transclude tiddler=<<listItem>>/> \n</$list>\n<hr>\n<$macrocall $name=\"list-tagged-draggable\" tag=<<currentTiddler>>/>\n</$reveal>\n</$set>\n</span>\n"
},
"$:/core/ui/TiddlerFieldTemplate": {
"title": "$:/core/ui/TiddlerFieldTemplate",
"text": "<tr class=\"tc-view-field\">\n<td class=\"tc-view-field-name\">\n<$text text=<<listItem>>/>\n</td>\n<td class=\"tc-view-field-value\">\n<$view field=<<listItem>>/>\n</td>\n</tr>"
},
"$:/core/ui/TiddlerFields": {
"title": "$:/core/ui/TiddlerFields",
"text": "<table class=\"tc-view-field-table\">\n<tbody>\n<$list filter=\"[all[current]fields[]sort[title]] -text\" template=\"$:/core/ui/TiddlerFieldTemplate\" variable=\"listItem\"/>\n</tbody>\n</table>\n"
},
"$:/core/ui/TiddlerInfo/Advanced/PluginInfo": {
"title": "$:/core/ui/TiddlerInfo/Advanced/PluginInfo",
"tags": "$:/tags/TiddlerInfo/Advanced",
"text": "\\define lingo-base() $:/language/TiddlerInfo/Advanced/PluginInfo/\n<$list filter=\"[all[current]has[plugin-type]]\">\n\n! <<lingo Heading>>\n\n<<lingo Hint>>\n<ul>\n<$list filter=\"[all[current]plugintiddlers[]sort[title]]\" emptyMessage=<<lingo Empty/Hint>>>\n<li>\n<$link to={{!!title}}>\n<$view field=\"title\"/>\n</$link>\n</li>\n</$list>\n</ul>\n\n</$list>\n"
},
"$:/core/ui/TiddlerInfo/Advanced/ShadowInfo": {
"title": "$:/core/ui/TiddlerInfo/Advanced/ShadowInfo",
"tags": "$:/tags/TiddlerInfo/Advanced",
"text": "\\define lingo-base() $:/language/TiddlerInfo/Advanced/ShadowInfo/\n<$set name=\"infoTiddler\" value=<<currentTiddler>>>\n\n''<<lingo Heading>>''\n\n<$list filter=\"[all[current]!is[shadow]]\">\n\n<<lingo NotShadow/Hint>>\n\n</$list>\n\n<$list filter=\"[all[current]is[shadow]]\">\n\n<<lingo Shadow/Hint>>\n\n<$list filter=\"[all[current]shadowsource[]]\">\n\n<$set name=\"pluginTiddler\" value=<<currentTiddler>>>\n<<lingo Shadow/Source>>\n</$set>\n\n</$list>\n\n<$list filter=\"[all[current]is[shadow]is[tiddler]]\">\n\n<<lingo OverriddenShadow/Hint>>\n\n</$list>\n\n\n</$list>\n</$set>\n"
},
"$:/core/ui/TiddlerInfo/Advanced": {
"title": "$:/core/ui/TiddlerInfo/Advanced",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Advanced/Caption}}",
"text": "<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/TiddlerInfo/Advanced]!has[draft.of]]\" variable=\"listItem\">\n<$transclude tiddler=<<listItem>>/>\n\n</$list>\n"
},
"$:/core/ui/TiddlerInfo/Fields": {
"title": "$:/core/ui/TiddlerInfo/Fields",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Fields/Caption}}",
"text": "<$transclude tiddler=\"$:/core/ui/TiddlerFields\"/>\n"
},
"$:/core/ui/TiddlerInfo/List": {
"title": "$:/core/ui/TiddlerInfo/List",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/List/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n<$list filter=\"[list{!!title}]\" emptyMessage=<<lingo List/Empty>> template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/TiddlerInfo/Listed": {
"title": "$:/core/ui/TiddlerInfo/Listed",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Listed/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n<$list filter=\"[all[current]listed[]!is[system]]\" emptyMessage=<<lingo Listed/Empty>> template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/TiddlerInfo/References": {
"title": "$:/core/ui/TiddlerInfo/References",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/References/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n<$list filter=\"[all[current]backlinks[]sort[title]]\" emptyMessage=<<lingo References/Empty>> template=\"$:/core/ui/ListItemTemplate\">\n</$list>\n"
},
"$:/core/ui/TiddlerInfo/Tagging": {
"title": "$:/core/ui/TiddlerInfo/Tagging",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Tagging/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n<$list filter=\"[all[current]tagging[]]\" emptyMessage=<<lingo Tagging/Empty>> template=\"$:/core/ui/ListItemTemplate\"/>\n"
},
"$:/core/ui/TiddlerInfo/Tools": {
"title": "$:/core/ui/TiddlerInfo/Tools",
"tags": "$:/tags/TiddlerInfo",
"caption": "{{$:/language/TiddlerInfo/Tools/Caption}}",
"text": "\\define lingo-base() $:/language/TiddlerInfo/\n\\define config-title()\n$:/config/ViewToolbarButtons/Visibility/$(listItem)$\n\\end\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ViewToolbar]!has[draft.of]]\" variable=\"listItem\">\n\n<$checkbox tiddler=<<config-title>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"show\"/> <$transclude tiddler=<<listItem>>/> <i class=\"tc-muted\"><$transclude tiddler=<<listItem>> field=\"description\"/></i>\n\n</$list>\n\n</$set>\n\n</$set>\n\n</$set>\n"
},
"$:/core/ui/TiddlerInfo": {
"title": "$:/core/ui/TiddlerInfo",
"text": "<div style=\"position:relative;\">\n<div class=\"tc-tiddler-controls\" style=\"position:absolute;right:0;\">\n<$reveal state=\"$:/config/TiddlerInfo/Mode\" type=\"match\" text=\"sticky\">\n<$button set=<<tiddlerInfoState>> setTo=\"\" tooltip={{$:/language/Buttons/Info/Hint}} aria-label={{$:/language/Buttons/Info/Caption}} class=\"tc-btn-invisible\">\n{{$:/core/images/close-button}}\n</$button>\n</$reveal>\n</div>\n</div>\n\n<$macrocall $name=\"tabs\" tabsList=\"[all[shadows+tiddlers]tag[$:/tags/TiddlerInfo]!has[draft.of]]\" default={{$:/config/TiddlerInfo/Default}}/>"
},
"$:/core/ui/TopBar/menu": {
"title": "$:/core/ui/TopBar/menu",
"tags": "$:/tags/TopRightBar",
"text": "<$reveal state=\"$:/state/sidebar\" type=\"nomatch\" text=\"no\">\n<$button set=\"$:/state/sidebar\" setTo=\"no\" tooltip={{$:/language/Buttons/HideSideBar/Hint}} aria-label={{$:/language/Buttons/HideSideBar/Caption}} class=\"tc-btn-invisible\">{{$:/core/images/chevron-right}}</$button>\n</$reveal>\n<$reveal state=\"$:/state/sidebar\" type=\"match\" text=\"no\">\n<$button set=\"$:/state/sidebar\" setTo=\"yes\" tooltip={{$:/language/Buttons/ShowSideBar/Hint}} aria-label={{$:/language/Buttons/ShowSideBar/Caption}} class=\"tc-btn-invisible\">{{$:/core/images/chevron-left}}</$button>\n</$reveal>\n"
},
"$:/core/ui/UntaggedTemplate": {
"title": "$:/core/ui/UntaggedTemplate",
"text": "\\define lingo-base() $:/language/SideBar/\n<$button popup=<<qualify \"$:/state/popup/tag\">> class=\"tc-btn-invisible tc-untagged-label tc-tag-label\">\n<<lingo Tags/Untagged/Caption>>\n</$button>\n<$reveal state=<<qualify \"$:/state/popup/tag\">> type=\"popup\" position=\"below\">\n<div class=\"tc-drop-down\">\n<$list filter=\"[untagged[]!is[system]] -[tags[]] +[sort[title]]\" template=\"$:/core/ui/ListItemTemplate\"/>\n</div>\n</$reveal>\n"
},
"$:/core/ui/ViewTemplate/body": {
"title": "$:/core/ui/ViewTemplate/body",
"tags": "$:/tags/ViewTemplate",
"text": "<$reveal tag=\"div\" class=\"tc-tiddler-body\" type=\"nomatch\" state=<<folded-state>> text=\"hide\" retain=\"yes\" animate=\"yes\">\n\n<$list filter=\"[all[current]!has[plugin-type]!field:hide-body[yes]]\">\n\n<$transclude>\n\n<$transclude tiddler=\"$:/language/MissingTiddler/Hint\"/>\n\n</$transclude>\n\n</$list>\n\n</$reveal>"
},
"$:/core/ui/ViewTemplate/classic": {
"title": "$:/core/ui/ViewTemplate/classic",
"tags": "$:/tags/ViewTemplate $:/tags/EditTemplate",
"text": "\\define lingo-base() $:/language/ClassicWarning/\n<$list filter=\"[all[current]type[text/x-tiddlywiki]]\">\n<div class=\"tc-message-box\">\n\n<<lingo Hint>>\n\n<$button set=\"!!type\" setTo=\"text/vnd.tiddlywiki\"><<lingo Upgrade/Caption>></$button>\n\n</div>\n</$list>\n"
},
"$:/core/ui/ViewTemplate/import": {
"title": "$:/core/ui/ViewTemplate/import",
"tags": "$:/tags/ViewTemplate",
"text": "\\define lingo-base() $:/language/Import/\n\n<$list filter=\"[all[current]field:plugin-type[import]]\">\n\n<div class=\"tc-import\">\n\n<<lingo Listing/Hint>>\n\n<$button message=\"tm-delete-tiddler\" param=<<currentTiddler>>><<lingo Listing/Cancel/Caption>></$button>\n<$button message=\"tm-perform-import\" param=<<currentTiddler>>><<lingo Listing/Import/Caption>></$button>\n\n{{||$:/core/ui/ImportListing}}\n\n<$button message=\"tm-delete-tiddler\" param=<<currentTiddler>>><<lingo Listing/Cancel/Caption>></$button>\n<$button message=\"tm-perform-import\" param=<<currentTiddler>>><<lingo Listing/Import/Caption>></$button>\n\n</div>\n\n</$list>\n"
},
"$:/core/ui/ViewTemplate/plugin": {
"title": "$:/core/ui/ViewTemplate/plugin",
"tags": "$:/tags/ViewTemplate",
"text": "<$list filter=\"[all[current]has[plugin-type]] -[all[current]field:plugin-type[import]]\">\n<$set name=\"plugin-type\" value={{!!plugin-type}}>\n<$set name=\"default-popup-state\" value=\"yes\">\n<$set name=\"qualified-state\" value=<<qualify \"$:/state/plugin-info\">>>\n{{||$:/core/ui/Components/plugin-info}}\n</$set>\n</$set>\n</$set>\n</$list>\n"
},
"$:/core/ui/ViewTemplate/subtitle": {
"title": "$:/core/ui/ViewTemplate/subtitle",
"tags": "$:/tags/ViewTemplate",
"text": "<$reveal type=\"nomatch\" state=<<folded-state>> text=\"hide\" tag=\"div\" retain=\"yes\" animate=\"yes\">\n<div class=\"tc-subtitle\">\n<$link to={{!!modifier}}>\n<$view field=\"modifier\"/>\n</$link> <$view field=\"modified\" format=\"date\" template={{$:/language/Tiddler/DateFormat}}/>\n</div>\n</$reveal>\n"
},
"$:/core/ui/ViewTemplate/tags": {
"title": "$:/core/ui/ViewTemplate/tags",
"tags": "$:/tags/ViewTemplate",
"text": "<$reveal type=\"nomatch\" state=<<folded-state>> text=\"hide\" tag=\"div\" retain=\"yes\" animate=\"yes\">\n<div class=\"tc-tags-wrapper\"><$list filter=\"[all[current]tags[]sort[title]]\" template=\"$:/core/ui/TagTemplate\" storyview=\"pop\"/></div>\n</$reveal>"
},
"$:/core/ui/ViewTemplate/title": {
"title": "$:/core/ui/ViewTemplate/title",
"tags": "$:/tags/ViewTemplate",
"text": "\\define title-styles()\nfill:$(foregroundColor)$;\n\\end\n\\define config-title()\n$:/config/ViewToolbarButtons/Visibility/$(listItem)$\n\\end\n<div class=\"tc-tiddler-title\">\n<div class=\"tc-titlebar\">\n<span class=\"tc-tiddler-controls\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ViewToolbar]!has[draft.of]]\" variable=\"listItem\"><$reveal type=\"nomatch\" state=<<config-title>> text=\"hide\"><$transclude tiddler=<<listItem>>/></$reveal></$list>\n</span>\n<$set name=\"tv-wikilinks\" value={{$:/config/Tiddlers/TitleLinks}}>\n<$link>\n<$set name=\"foregroundColor\" value={{!!color}}>\n<span class=\"tc-tiddler-title-icon\" style=<<title-styles>>>\n<$transclude tiddler={{!!icon}}/>\n</span>\n</$set>\n<$list filter=\"[all[current]removeprefix[$:/]]\">\n<h2 class=\"tc-title\" title={{$:/language/SystemTiddler/Tooltip}}>\n<span class=\"tc-system-title-prefix\">$:/</span><$text text=<<currentTiddler>>/>\n</h2>\n</$list>\n<$list filter=\"[all[current]!prefix[$:/]]\">\n<h2 class=\"tc-title\">\n<$view field=\"title\"/>\n</h2>\n</$list>\n</$link>\n</$set>\n</div>\n\n<$reveal type=\"nomatch\" text=\"\" default=\"\" state=<<tiddlerInfoState>> class=\"tc-tiddler-info tc-popup-handle\" animate=\"yes\" retain=\"yes\">\n\n<$transclude tiddler=\"$:/core/ui/TiddlerInfo\"/>\n\n</$reveal>\n</div>"
},
"$:/core/ui/ViewTemplate/unfold": {
"title": "$:/core/ui/ViewTemplate/unfold",
"tags": "$:/tags/ViewTemplate",
"text": "<$reveal tag=\"div\" type=\"nomatch\" state=\"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-bar\" text=\"hide\">\n<$reveal tag=\"div\" type=\"nomatch\" state=<<folded-state>> text=\"hide\" default=\"show\" retain=\"yes\" animate=\"yes\">\n<$button tooltip={{$:/language/Buttons/Fold/Hint}} aria-label={{$:/language/Buttons/Fold/Caption}} class=\"tc-fold-banner\">\n<$action-sendmessage $message=\"tm-fold-tiddler\" $param=<<currentTiddler>> foldedState=<<folded-state>>/>\n{{$:/core/images/chevron-up}}\n</$button>\n</$reveal>\n<$reveal tag=\"div\" type=\"nomatch\" state=<<folded-state>> text=\"show\" default=\"show\" retain=\"yes\" animate=\"yes\">\n<$button tooltip={{$:/language/Buttons/Unfold/Hint}} aria-label={{$:/language/Buttons/Unfold/Caption}} class=\"tc-unfold-banner\">\n<$action-sendmessage $message=\"tm-fold-tiddler\" $param=<<currentTiddler>> foldedState=<<folded-state>>/>\n{{$:/core/images/chevron-down}}\n</$button>\n</$reveal>\n</$reveal>\n"
},
"$:/core/ui/ViewTemplate": {
"title": "$:/core/ui/ViewTemplate",
"text": "\\define frame-classes()\ntc-tiddler-frame tc-tiddler-view-frame $(missingTiddlerClass)$ $(shadowTiddlerClass)$ $(systemTiddlerClass)$ $(tiddlerTagClasses)$\n\\end\n\\define folded-state()\n$:/state/folded/$(currentTiddler)$\n\\end\n<$set name=\"storyTiddler\" value=<<currentTiddler>>><$set name=\"tiddlerInfoState\" value=<<qualify \"$:/state/popup/tiddler-info\">>><$tiddler tiddler=<<currentTiddler>>><div class=<<frame-classes>>><$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ViewTemplate]!has[draft.of]]\" variable=\"listItem\"><$transclude tiddler=<<listItem>>/></$list>\n</div>\n</$tiddler></$set></$set>\n"
},
"$:/core/ui/Buttons/clone": {
"title": "$:/core/ui/Buttons/clone",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/clone-button}} {{$:/language/Buttons/Clone/Caption}}",
"description": "{{$:/language/Buttons/Clone/Hint}}",
"text": "<$button message=\"tm-new-tiddler\" param=<<currentTiddler>> tooltip={{$:/language/Buttons/Clone/Hint}} aria-label={{$:/language/Buttons/Clone/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/clone-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Clone/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/close-others": {
"title": "$:/core/ui/Buttons/close-others",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/close-others-button}} {{$:/language/Buttons/CloseOthers/Caption}}",
"description": "{{$:/language/Buttons/CloseOthers/Hint}}",
"text": "<$button message=\"tm-close-other-tiddlers\" param=<<currentTiddler>> tooltip={{$:/language/Buttons/CloseOthers/Hint}} aria-label={{$:/language/Buttons/CloseOthers/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/close-others-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/CloseOthers/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/close": {
"title": "$:/core/ui/Buttons/close",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/close-button}} {{$:/language/Buttons/Close/Caption}}",
"description": "{{$:/language/Buttons/Close/Hint}}",
"text": "<$button message=\"tm-close-tiddler\" tooltip={{$:/language/Buttons/Close/Hint}} aria-label={{$:/language/Buttons/Close/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/close-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Close/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/edit": {
"title": "$:/core/ui/Buttons/edit",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/edit-button}} {{$:/language/Buttons/Edit/Caption}}",
"description": "{{$:/language/Buttons/Edit/Hint}}",
"text": "<$button message=\"tm-edit-tiddler\" tooltip={{$:/language/Buttons/Edit/Hint}} aria-label={{$:/language/Buttons/Edit/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/edit-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Edit/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/export-tiddler": {
"title": "$:/core/ui/Buttons/export-tiddler",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/export-button}} {{$:/language/Buttons/ExportTiddler/Caption}}",
"description": "{{$:/language/Buttons/ExportTiddler/Hint}}",
"text": "\\define makeExportFilter()\n[[$(currentTiddler)$]]\n\\end\n<$macrocall $name=\"exportButton\" exportFilter=<<makeExportFilter>> lingoBase=\"$:/language/Buttons/ExportTiddler/\" baseFilename=<<currentTiddler>>/>"
},
"$:/core/ui/Buttons/fold-bar": {
"title": "$:/core/ui/Buttons/fold-bar",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/chevron-up}} {{$:/language/Buttons/Fold/FoldBar/Caption}}",
"description": "{{$:/language/Buttons/Fold/FoldBar/Hint}}",
"text": "<!-- This dummy toolbar button is here to allow visibility of the fold-bar to be controlled as if it were a toolbar button -->"
},
"$:/core/ui/Buttons/fold-others": {
"title": "$:/core/ui/Buttons/fold-others",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/fold-others-button}} {{$:/language/Buttons/FoldOthers/Caption}}",
"description": "{{$:/language/Buttons/FoldOthers/Hint}}",
"text": "<$button tooltip={{$:/language/Buttons/FoldOthers/Hint}} aria-label={{$:/language/Buttons/FoldOthers/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-fold-other-tiddlers\" $param=<<currentTiddler>> foldedStatePrefix=\"$:/state/folded/\"/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\" variable=\"listItem\">\n{{$:/core/images/fold-others-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/FoldOthers/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/fold": {
"title": "$:/core/ui/Buttons/fold",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/fold-button}} {{$:/language/Buttons/Fold/Caption}}",
"description": "{{$:/language/Buttons/Fold/Hint}}",
"text": "<$reveal type=\"nomatch\" state=<<folded-state>> text=\"hide\" default=\"show\"><$button tooltip={{$:/language/Buttons/Fold/Hint}} aria-label={{$:/language/Buttons/Fold/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-fold-tiddler\" $param=<<currentTiddler>> foldedState=<<folded-state>>/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\" variable=\"listItem\">\n{{$:/core/images/fold-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text={{$:/language/Buttons/Fold/Caption}}/>\n</span>\n</$list>\n</$button></$reveal><$reveal type=\"match\" state=<<folded-state>> text=\"hide\" default=\"show\"><$button tooltip={{$:/language/Buttons/Unfold/Hint}} aria-label={{$:/language/Buttons/Unfold/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-fold-tiddler\" $param=<<currentTiddler>> foldedState=<<folded-state>>/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\" variable=\"listItem\">\n{{$:/core/images/unfold-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\">\n<$text text={{$:/language/Buttons/Unfold/Caption}}/>\n</span>\n</$list>\n</$button></$reveal>"
},
"$:/core/ui/Buttons/info": {
"title": "$:/core/ui/Buttons/info",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/info-button}} {{$:/language/Buttons/Info/Caption}}",
"description": "{{$:/language/Buttons/Info/Hint}}",
"text": "\\define button-content()\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/info-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Info/Caption}}/></span>\n</$list>\n\\end\n<$reveal state=\"$:/config/TiddlerInfo/Mode\" type=\"match\" text=\"popup\">\n<$button popup=<<tiddlerInfoState>> tooltip={{$:/language/Buttons/Info/Hint}} aria-label={{$:/language/Buttons/Info/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$macrocall $name=\"button-content\" mode=\"inline\"/>\n</$button>\n</$reveal>\n<$reveal state=\"$:/config/TiddlerInfo/Mode\" type=\"match\" text=\"sticky\">\n<$reveal state=<<tiddlerInfoState>> type=\"match\" text=\"\" default=\"\">\n<$button set=<<tiddlerInfoState>> setTo=\"yes\" tooltip={{$:/language/Buttons/Info/Hint}} aria-label={{$:/language/Buttons/Info/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$macrocall $name=\"button-content\" mode=\"inline\"/>\n</$button>\n</$reveal>\n<$reveal state=<<tiddlerInfoState>> type=\"nomatch\" text=\"\" default=\"\">\n<$button set=<<tiddlerInfoState>> setTo=\"\" tooltip={{$:/language/Buttons/Info/Hint}} aria-label={{$:/language/Buttons/Info/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$macrocall $name=\"button-content\" mode=\"inline\"/>\n</$button>\n</$reveal>\n</$reveal>"
},
"$:/core/ui/Buttons/more-tiddler-actions": {
"title": "$:/core/ui/Buttons/more-tiddler-actions",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/down-arrow}} {{$:/language/Buttons/More/Caption}}",
"description": "{{$:/language/Buttons/More/Hint}}",
"text": "\\define config-title()\n$:/config/ViewToolbarButtons/Visibility/$(listItem)$\n\\end\n<$button popup=<<qualify \"$:/state/popup/more\">> tooltip={{$:/language/Buttons/More/Hint}} aria-label={{$:/language/Buttons/More/Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/down-arrow}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/More/Caption}}/></span>\n</$list>\n</$button><$reveal state=<<qualify \"$:/state/popup/more\">> type=\"popup\" position=\"below\" animate=\"yes\">\n\n<div class=\"tc-drop-down\">\n\n<$set name=\"tv-config-toolbar-icons\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-text\" value=\"yes\">\n\n<$set name=\"tv-config-toolbar-class\" value=\"tc-btn-invisible\">\n\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/ViewToolbar]!has[draft.of]] -[[$:/core/ui/Buttons/more-tiddler-actions]]\" variable=\"listItem\">\n\n<$reveal type=\"match\" state=<<config-title>> text=\"hide\">\n\n<$transclude tiddler=<<listItem>> mode=\"inline\"/>\n\n</$reveal>\n\n</$list>\n\n</$set>\n\n</$set>\n\n</$set>\n\n</div>\n\n</$reveal>"
},
"$:/core/ui/Buttons/new-here": {
"title": "$:/core/ui/Buttons/new-here",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/new-here-button}} {{$:/language/Buttons/NewHere/Caption}}",
"description": "{{$:/language/Buttons/NewHere/Hint}}",
"text": "\\define newHereButtonTags()\n[[$(currentTiddler)$]]\n\\end\n\\define newHereButton()\n<$button tooltip={{$:/language/Buttons/NewHere/Hint}} aria-label={{$:/language/Buttons/NewHere/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-new-tiddler\" tags=<<newHereButtonTags>>/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/new-here-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewHere/Caption}}/></span>\n</$list>\n</$button>\n\\end\n<<newHereButton>>"
},
"$:/core/ui/Buttons/new-journal-here": {
"title": "$:/core/ui/Buttons/new-journal-here",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/new-journal-button}} {{$:/language/Buttons/NewJournalHere/Caption}}",
"description": "{{$:/language/Buttons/NewJournalHere/Hint}}",
"text": "\\define journalButtonTags()\n[[$(currentTiddlerTag)$]] $(journalTags)$\n\\end\n\\define journalButton()\n<$button tooltip={{$:/language/Buttons/NewJournalHere/Hint}} aria-label={{$:/language/Buttons/NewJournalHere/Caption}} class=<<tv-config-toolbar-class>>>\n<$action-sendmessage $message=\"tm-new-tiddler\" title=<<now \"$(journalTitleTemplate)$\">> tags=<<journalButtonTags>>/>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/new-journal-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/NewJournalHere/Caption}}/></span>\n</$list>\n</$button>\n\\end\n<$set name=\"journalTitleTemplate\" value={{$:/config/NewJournal/Title}}>\n<$set name=\"journalTags\" value={{$:/config/NewJournal/Tags}}>\n<$set name=\"currentTiddlerTag\" value=<<currentTiddler>>>\n<<journalButton>>\n</$set></$set></$set>"
},
"$:/core/ui/Buttons/open-window": {
"title": "$:/core/ui/Buttons/open-window",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/open-window}} {{$:/language/Buttons/OpenWindow/Caption}}",
"description": "{{$:/language/Buttons/OpenWindow/Hint}}",
"text": "<$button message=\"tm-open-window\" tooltip={{$:/language/Buttons/OpenWindow/Hint}} aria-label={{$:/language/Buttons/OpenWindow/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/open-window}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/OpenWindow/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/permalink": {
"title": "$:/core/ui/Buttons/permalink",
"tags": "$:/tags/ViewToolbar",
"caption": "{{$:/core/images/permalink-button}} {{$:/language/Buttons/Permalink/Caption}}",
"description": "{{$:/language/Buttons/Permalink/Hint}}",
"text": "<$button message=\"tm-permalink\" tooltip={{$:/language/Buttons/Permalink/Hint}} aria-label={{$:/language/Buttons/Permalink/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/permalink-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Permalink/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/core/ui/Buttons/permaview": {
"title": "$:/core/ui/Buttons/permaview",
"tags": "$:/tags/ViewToolbar $:/tags/PageControls",
"caption": "{{$:/core/images/permaview-button}} {{$:/language/Buttons/Permaview/Caption}}",
"description": "{{$:/language/Buttons/Permaview/Hint}}",
"text": "<$button message=\"tm-permaview\" tooltip={{$:/language/Buttons/Permaview/Hint}} aria-label={{$:/language/Buttons/Permaview/Caption}} class=<<tv-config-toolbar-class>>>\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/permaview-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$:/language/Buttons/Permaview/Caption}}/></span>\n</$list>\n</$button>"
},
"$:/DefaultTiddlers": {
"title": "$:/DefaultTiddlers",
"text": "GettingStarted\n"
},
"$:/temp/advancedsearch": {
"title": "$:/temp/advancedsearch",
"text": ""
},
"$:/snippets/allfields": {
"title": "$:/snippets/allfields",
"text": "\\define renderfield(title)\n<tr class=\"tc-view-field\"><td class=\"tc-view-field-name\">''$title$'':</td><td class=\"tc-view-field-value\">//{{$:/language/Docs/Fields/$title$}}//</td></tr>\n\\end\n<table class=\"tc-view-field-table\"><tbody><$list filter=\"[fields[]sort[title]]\" variable=\"listItem\"><$macrocall $name=\"renderfield\" title=<<listItem>>/></$list>\n</tbody></table>\n"
},
"$:/config/AnimationDuration": {
"title": "$:/config/AnimationDuration",
"text": "400"
},
"$:/config/AutoSave": {
"title": "$:/config/AutoSave",
"text": "yes"
},
"$:/config/BitmapEditor/Colour": {
"title": "$:/config/BitmapEditor/Colour",
"text": "#444"
},
"$:/config/BitmapEditor/ImageSizes": {
"title": "$:/config/BitmapEditor/ImageSizes",
"text": "[[62px 100px]] [[100px 62px]] [[124px 200px]] [[200px 124px]] [[248px 400px]] [[371px 600px]] [[400px 248px]] [[556px 900px]] [[600px 371px]] [[742px 1200px]] [[900px 556px]] [[1200px 742px]]"
},
"$:/config/BitmapEditor/LineWidth": {
"title": "$:/config/BitmapEditor/LineWidth",
"text": "3px"
},
"$:/config/BitmapEditor/LineWidths": {
"title": "$:/config/BitmapEditor/LineWidths",
"text": "0.25px 0.5px 1px 2px 3px 4px 6px 8px 10px 16px 20px 28px 40px 56px 80px"
},
"$:/config/BitmapEditor/Opacities": {
"title": "$:/config/BitmapEditor/Opacities",
"text": "0.01 0.025 0.05 0.075 0.1 0.15 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0"
},
"$:/config/BitmapEditor/Opacity": {
"title": "$:/config/BitmapEditor/Opacity",
"text": "1.0"
},
"$:/config/DefaultSidebarTab": {
"title": "$:/config/DefaultSidebarTab",
"text": "$:/core/ui/SideBar/Open"
},
"$:/config/DownloadSaver/AutoSave": {
"title": "$:/config/DownloadSaver/AutoSave",
"text": "no"
},
"$:/config/Drafts/TypingTimeout": {
"title": "$:/config/Drafts/TypingTimeout",
"text": "400"
},
"$:/config/EditTemplateFields/Visibility/title": {
"title": "$:/config/EditTemplateFields/Visibility/title",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/tags": {
"title": "$:/config/EditTemplateFields/Visibility/tags",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/text": {
"title": "$:/config/EditTemplateFields/Visibility/text",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/creator": {
"title": "$:/config/EditTemplateFields/Visibility/creator",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/created": {
"title": "$:/config/EditTemplateFields/Visibility/created",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/modified": {
"title": "$:/config/EditTemplateFields/Visibility/modified",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/modifier": {
"title": "$:/config/EditTemplateFields/Visibility/modifier",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/type": {
"title": "$:/config/EditTemplateFields/Visibility/type",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/draft.title": {
"title": "$:/config/EditTemplateFields/Visibility/draft.title",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/draft.of": {
"title": "$:/config/EditTemplateFields/Visibility/draft.of",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/revision": {
"title": "$:/config/EditTemplateFields/Visibility/revision",
"text": "hide"
},
"$:/config/EditTemplateFields/Visibility/bag": {
"title": "$:/config/EditTemplateFields/Visibility/bag",
"text": "hide"
},
"$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-4": {
"title": "$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-4",
"text": "hide"
},
"$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-5": {
"title": "$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-5",
"text": "hide"
},
"$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-6": {
"title": "$:/config/EditorToolbarButtons/Visibility/$:/core/ui/EditorToolbar/heading-6",
"text": "hide"
},
"$:/config/EditorTypeMappings/image/gif": {
"title": "$:/config/EditorTypeMappings/image/gif",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/jpeg": {
"title": "$:/config/EditorTypeMappings/image/jpeg",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/jpg": {
"title": "$:/config/EditorTypeMappings/image/jpg",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/png": {
"title": "$:/config/EditorTypeMappings/image/png",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/image/x-icon": {
"title": "$:/config/EditorTypeMappings/image/x-icon",
"text": "bitmap"
},
"$:/config/EditorTypeMappings/text/vnd.tiddlywiki": {
"title": "$:/config/EditorTypeMappings/text/vnd.tiddlywiki",
"text": "text"
},
"$:/config/Manager/Show": {
"title": "$:/config/Manager/Show",
"text": "tiddlers"
},
"$:/config/Manager/Filter": {
"title": "$:/config/Manager/Filter",
"text": ""
},
"$:/config/Manager/Order": {
"title": "$:/config/Manager/Order",
"text": "forward"
},
"$:/config/Manager/Sort": {
"title": "$:/config/Manager/Sort",
"text": "title"
},
"$:/config/Manager/System": {
"title": "$:/config/Manager/System",
"text": "system"
},
"$:/config/Manager/Tag": {
"title": "$:/config/Manager/Tag",
"text": ""
},
"$:/state/popup/manager/item/$:/Manager/ItemMain/RawText": {
"title": "$:/state/popup/manager/item/$:/Manager/ItemMain/RawText",
"text": "hide"
},
"$:/config/MissingLinks": {
"title": "$:/config/MissingLinks",
"text": "yes"
},
"$:/config/Navigation/UpdateAddressBar": {
"title": "$:/config/Navigation/UpdateAddressBar",
"text": "no"
},
"$:/config/Navigation/UpdateHistory": {
"title": "$:/config/Navigation/UpdateHistory",
"text": "no"
},
"$:/config/OfficialPluginLibrary": {
"title": "$:/config/OfficialPluginLibrary",
"tags": "$:/tags/PluginLibrary",
"url": "http://tiddlywiki.com/library/v5.1.14/index.html",
"caption": "{{$:/language/OfficialPluginLibrary}}",
"text": "{{$:/language/OfficialPluginLibrary/Hint}}\n"
},
"$:/config/Navigation/openLinkFromInsideRiver": {
"title": "$:/config/Navigation/openLinkFromInsideRiver",
"text": "below"
},
"$:/config/Navigation/openLinkFromOutsideRiver": {
"title": "$:/config/Navigation/openLinkFromOutsideRiver",
"text": "top"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/advanced-search": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/advanced-search",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/close-all": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/close-all",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/encryption": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/encryption",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/export-page": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/export-page",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/fold-all": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/fold-all",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/full-screen": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/full-screen",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/home": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/home",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/refresh": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/refresh",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/import": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/import",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/language": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/language",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/tag-manager": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/tag-manager",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/manager": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/manager",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/more-page-actions": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/more-page-actions",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/new-journal": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/new-journal",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/new-image": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/new-image",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/palette": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/palette",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/permaview": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/permaview",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/print": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/print",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/storyview": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/storyview",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/timestamp": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/timestamp",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/theme": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/theme",
"text": "hide"
},
"$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/unfold-all": {
"title": "$:/config/PageControlButtons/Visibility/$:/core/ui/Buttons/unfold-all",
"text": "hide"
},
"$:/config/Performance/Instrumentation": {
"title": "$:/config/Performance/Instrumentation",
"text": "no"
},
"$:/config/SaveWikiButton/Template": {
"title": "$:/config/SaveWikiButton/Template",
"text": "$:/core/save/all"
},
"$:/config/SaverFilter": {
"title": "$:/config/SaverFilter",
"text": "[all[]] -[[$:/HistoryList]] -[[$:/StoryList]] -[[$:/Import]] -[[$:/isEncrypted]] -[[$:/UploadName]] -[prefix[$:/state/]] -[prefix[$:/temp/]]"
},
"$:/config/Search/AutoFocus": {
"title": "$:/config/Search/AutoFocus",
"text": "true"
},
"$:/config/Search/MinLength": {
"title": "$:/config/Search/MinLength",
"text": "3"
},
"$:/config/SearchResults/Default": {
"title": "$:/config/SearchResults/Default",
"text": "$:/core/ui/DefaultSearchResultList"
},
"$:/config/ShortcutInfo/bold": {
"title": "$:/config/ShortcutInfo/bold",
"text": "{{$:/language/Buttons/Bold/Hint}}"
},
"$:/config/ShortcutInfo/cancel-edit-tiddler": {
"title": "$:/config/ShortcutInfo/cancel-edit-tiddler",
"text": "{{$:/language/Buttons/Cancel/Hint}}"
},
"$:/config/ShortcutInfo/excise": {
"title": "$:/config/ShortcutInfo/excise",
"text": "{{$:/language/Buttons/Excise/Hint}}"
},
"$:/config/ShortcutInfo/heading-1": {
"title": "$:/config/ShortcutInfo/heading-1",
"text": "{{$:/language/Buttons/Heading1/Hint}}"
},
"$:/config/ShortcutInfo/heading-2": {
"title": "$:/config/ShortcutInfo/heading-2",
"text": "{{$:/language/Buttons/Heading2/Hint}}"
},
"$:/config/ShortcutInfo/heading-3": {
"title": "$:/config/ShortcutInfo/heading-3",
"text": "{{$:/language/Buttons/Heading3/Hint}}"
},
"$:/config/ShortcutInfo/heading-4": {
"title": "$:/config/ShortcutInfo/heading-4",
"text": "{{$:/language/Buttons/Heading4/Hint}}"
},
"$:/config/ShortcutInfo/heading-5": {
"title": "$:/config/ShortcutInfo/heading-5",
"text": "{{$:/language/Buttons/Heading5/Hint}}"
},
"$:/config/ShortcutInfo/heading-6": {
"title": "$:/config/ShortcutInfo/heading-6",
"text": "{{$:/language/Buttons/Heading6/Hint}}"
},
"$:/config/ShortcutInfo/italic": {
"title": "$:/config/ShortcutInfo/italic",
"text": "{{$:/language/Buttons/Italic/Hint}}"
},
"$:/config/ShortcutInfo/link": {
"title": "$:/config/ShortcutInfo/link",
"text": "{{$:/language/Buttons/Link/Hint}}"
},
"$:/config/ShortcutInfo/list-bullet": {
"title": "$:/config/ShortcutInfo/list-bullet",
"text": "{{$:/language/Buttons/ListBullet/Hint}}"
},
"$:/config/ShortcutInfo/list-number": {
"title": "$:/config/ShortcutInfo/list-number",
"text": "{{$:/language/Buttons/ListNumber/Hint}}"
},
"$:/config/ShortcutInfo/mono-block": {
"title": "$:/config/ShortcutInfo/mono-block",
"text": "{{$:/language/Buttons/MonoBlock/Hint}}"
},
"$:/config/ShortcutInfo/mono-line": {
"title": "$:/config/ShortcutInfo/mono-line",
"text": "{{$:/language/Buttons/MonoLine/Hint}}"
},
"$:/config/ShortcutInfo/picture": {
"title": "$:/config/ShortcutInfo/picture",
"text": "{{$:/language/Buttons/Picture/Hint}}"
},
"$:/config/ShortcutInfo/preview": {
"title": "$:/config/ShortcutInfo/preview",
"text": "{{$:/language/Buttons/Preview/Hint}}"
},
"$:/config/ShortcutInfo/quote": {
"title": "$:/config/ShortcutInfo/quote",
"text": "{{$:/language/Buttons/Quote/Hint}}"
},
"$:/config/ShortcutInfo/save-tiddler": {
"title": "$:/config/ShortcutInfo/save-tiddler",
"text": "{{$:/language/Buttons/Save/Hint}}"
},
"$:/config/ShortcutInfo/stamp": {
"title": "$:/config/ShortcutInfo/stamp",
"text": "{{$:/language/Buttons/Stamp/Hint}}"
},
"$:/config/ShortcutInfo/strikethrough": {
"title": "$:/config/ShortcutInfo/strikethrough",
"text": "{{$:/language/Buttons/Strikethrough/Hint}}"
},
"$:/config/ShortcutInfo/subscript": {
"title": "$:/config/ShortcutInfo/subscript",
"text": "{{$:/language/Buttons/Subscript/Hint}}"
},
"$:/config/ShortcutInfo/superscript": {
"title": "$:/config/ShortcutInfo/superscript",
"text": "{{$:/language/Buttons/Superscript/Hint}}"
},
"$:/config/ShortcutInfo/underline": {
"title": "$:/config/ShortcutInfo/underline",
"text": "{{$:/language/Buttons/Underline/Hint}}"
},
"$:/config/SyncFilter": {
"title": "$:/config/SyncFilter",
"text": "[is[tiddler]] -[[$:/HistoryList]] -[[$:/Import]] -[[$:/isEncrypted]] -[prefix[$:/status/]] -[prefix[$:/state/]] -[prefix[$:/temp/]]"
},
"$:/config/TextEditor/EditorHeight/Height": {
"title": "$:/config/TextEditor/EditorHeight/Height",
"text": "400px"
},
"$:/config/TextEditor/EditorHeight/Mode": {
"title": "$:/config/TextEditor/EditorHeight/Mode",
"text": "auto"
},
"$:/config/TiddlerInfo/Default": {
"title": "$:/config/TiddlerInfo/Default",
"text": "$:/core/ui/TiddlerInfo/Fields"
},
"$:/config/TiddlerInfo/Mode": {
"title": "$:/config/TiddlerInfo/Mode",
"text": "popup"
},
"$:/config/Tiddlers/TitleLinks": {
"title": "$:/config/Tiddlers/TitleLinks",
"text": "no"
},
"$:/config/Toolbar/ButtonClass": {
"title": "$:/config/Toolbar/ButtonClass",
"text": "tc-btn-invisible"
},
"$:/config/Toolbar/Icons": {
"title": "$:/config/Toolbar/Icons",
"text": "yes"
},
"$:/config/Toolbar/Text": {
"title": "$:/config/Toolbar/Text",
"text": "no"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/clone": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/clone",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/close-others": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/close-others",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/export-tiddler": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/export-tiddler",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/info": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/info",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/more-tiddler-actions": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/more-tiddler-actions",
"text": "show"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/new-here": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/new-here",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/new-journal-here": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/new-journal-here",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/open-window": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/open-window",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/permalink": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/permalink",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/permaview": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/permaview",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/delete": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/delete",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-bar": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-bar",
"text": "hide"
},
"$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-others": {
"title": "$:/config/ViewToolbarButtons/Visibility/$:/core/ui/Buttons/fold-others",
"text": "hide"
},
"$:/config/shortcuts-mac/bold": {
"title": "$:/config/shortcuts-mac/bold",
"text": "meta-B"
},
"$:/config/shortcuts-mac/italic": {
"title": "$:/config/shortcuts-mac/italic",
"text": "meta-I"
},
"$:/config/shortcuts-mac/underline": {
"title": "$:/config/shortcuts-mac/underline",
"text": "meta-U"
},
"$:/config/shortcuts-not-mac/bold": {
"title": "$:/config/shortcuts-not-mac/bold",
"text": "ctrl-B"
},
"$:/config/shortcuts-not-mac/italic": {
"title": "$:/config/shortcuts-not-mac/italic",
"text": "ctrl-I"
},
"$:/config/shortcuts-not-mac/underline": {
"title": "$:/config/shortcuts-not-mac/underline",
"text": "ctrl-U"
},
"$:/config/shortcuts/cancel-edit-tiddler": {
"title": "$:/config/shortcuts/cancel-edit-tiddler",
"text": "escape"
},
"$:/config/shortcuts/excise": {
"title": "$:/config/shortcuts/excise",
"text": "ctrl-E"
},
"$:/config/shortcuts/heading-1": {
"title": "$:/config/shortcuts/heading-1",
"text": "ctrl-1"
},
"$:/config/shortcuts/heading-2": {
"title": "$:/config/shortcuts/heading-2",
"text": "ctrl-2"
},
"$:/config/shortcuts/heading-3": {
"title": "$:/config/shortcuts/heading-3",
"text": "ctrl-3"
},
"$:/config/shortcuts/heading-4": {
"title": "$:/config/shortcuts/heading-4",
"text": "ctrl-4"
},
"$:/config/shortcuts/heading-5": {
"title": "$:/config/shortcuts/heading-5",
"text": "ctrl-5"
},
"$:/config/shortcuts/heading-6": {
"title": "$:/config/shortcuts/heading-6",
"text": "ctrl-6"
},
"$:/config/shortcuts/link": {
"title": "$:/config/shortcuts/link",
"text": "ctrl-L"
},
"$:/config/shortcuts/list-bullet": {
"title": "$:/config/shortcuts/list-bullet",
"text": "ctrl-shift-L"
},
"$:/config/shortcuts/list-number": {
"title": "$:/config/shortcuts/list-number",
"text": "ctrl-shift-N"
},
"$:/config/shortcuts/mono-block": {
"title": "$:/config/shortcuts/mono-block",
"text": "ctrl-shift-M"
},
"$:/config/shortcuts/mono-line": {
"title": "$:/config/shortcuts/mono-line",
"text": "ctrl-M"
},
"$:/config/shortcuts/picture": {
"title": "$:/config/shortcuts/picture",
"text": "ctrl-shift-I"
},
"$:/config/shortcuts/preview": {
"title": "$:/config/shortcuts/preview",
"text": "alt-P"
},
"$:/config/shortcuts/quote": {
"title": "$:/config/shortcuts/quote",
"text": "ctrl-Q"
},
"$:/config/shortcuts/save-tiddler": {
"title": "$:/config/shortcuts/save-tiddler",
"text": "ctrl+enter"
},
"$:/config/shortcuts/stamp": {
"title": "$:/config/shortcuts/stamp",
"text": "ctrl-S"
},
"$:/config/shortcuts/strikethrough": {
"title": "$:/config/shortcuts/strikethrough",
"text": "ctrl-T"
},
"$:/config/shortcuts/subscript": {
"title": "$:/config/shortcuts/subscript",
"text": "ctrl-shift-B"
},
"$:/config/shortcuts/superscript": {
"title": "$:/config/shortcuts/superscript",
"text": "ctrl-shift-P"
},
"$:/config/WikiParserRules/Inline/wikilink": {
"title": "$:/config/WikiParserRules/Inline/wikilink",
"text": "enable"
},
"$:/snippets/currpalettepreview": {
"title": "$:/snippets/currpalettepreview",
"text": "\\define swatchStyle()\nbackground-color: $(swatchColour)$;\n\\end\n\\define swatch(colour)\n<$set name=\"swatchColour\" value={{##$colour$}}>\n<div class=\"tc-swatch\" style=<<swatchStyle>>/>\n</$set>\n\\end\n<div class=\"tc-swatches-horiz\">\n<<swatch foreground>>\n<<swatch background>>\n<<swatch muted-foreground>>\n<<swatch primary>>\n<<swatch page-background>>\n<<swatch tab-background>>\n<<swatch tiddler-info-background>>\n</div>\n"
},
"$:/snippets/download-wiki-button": {
"title": "$:/snippets/download-wiki-button",
"text": "\\define lingo-base() $:/language/ControlPanel/Tools/Download/\n<$button class=\"tc-btn-big-green\">\n<$action-sendmessage $message=\"tm-download-file\" $param=\"$:/core/save/all\" filename=\"index.html\"/>\n<<lingo Full/Caption>> {{$:/core/images/save-button}}\n</$button>"
},
"$:/language": {
"title": "$:/language",
"text": "$:/languages/en-GB"
},
"$:/snippets/languageswitcher": {
"title": "$:/snippets/languageswitcher",
"text": "{{$:/language/ControlPanel/Basics/Language/Prompt}} <$select tiddler=\"$:/language\">\n<$list filter=\"[[$:/languages/en-GB]] [plugin-type[language]sort[description]]\">\n<option value=<<currentTiddler>>><$view field=\"description\"><$view field=\"name\"><$view field=\"title\"/></$view></$view></option>\n</$list>\n</$select>"
},
"$:/core/macros/CSS": {
"title": "$:/core/macros/CSS",
"tags": "$:/tags/Macro",
"text": "\\define colour(name)\n<$transclude tiddler={{$:/palette}} index=\"$name$\"><$transclude tiddler=\"$:/palettes/Vanilla\" index=\"$name$\"/></$transclude>\n\\end\n\n\\define color(name)\n<<colour $name$>>\n\\end\n\n\\define box-shadow(shadow)\n``\n -webkit-box-shadow: $shadow$;\n -moz-box-shadow: $shadow$;\n box-shadow: $shadow$;\n``\n\\end\n\n\\define filter(filter)\n``\n -webkit-filter: $filter$;\n -moz-filter: $filter$;\n filter: $filter$;\n``\n\\end\n\n\\define transition(transition)\n``\n -webkit-transition: $transition$;\n -moz-transition: $transition$;\n transition: $transition$;\n``\n\\end\n\n\\define transform-origin(origin)\n``\n -webkit-transform-origin: $origin$;\n -moz-transform-origin: $origin$;\n transform-origin: $origin$;\n``\n\\end\n\n\\define background-linear-gradient(gradient)\n``\nbackground-image: linear-gradient($gradient$);\nbackground-image: -o-linear-gradient($gradient$);\nbackground-image: -moz-linear-gradient($gradient$);\nbackground-image: -webkit-linear-gradient($gradient$);\nbackground-image: -ms-linear-gradient($gradient$);\n``\n\\end\n\n\\define column-count(columns)\n``\n-moz-column-count: $columns$;\n-webkit-column-count: $columns$;\ncolumn-count: $columns$;\n``\n\\end\n\n\\define datauri(title)\n<$macrocall $name=\"makedatauri\" type={{$title$!!type}} text={{$title$}}/>\n\\end\n\n\\define if-sidebar(text)\n<$reveal state=\"$:/state/sidebar\" type=\"match\" text=\"yes\" default=\"yes\">$text$</$reveal>\n\\end\n\n\\define if-no-sidebar(text)\n<$reveal state=\"$:/state/sidebar\" type=\"nomatch\" text=\"yes\" default=\"yes\">$text$</$reveal>\n\\end\n"
},
"$:/core/macros/colour-picker": {
"title": "$:/core/macros/colour-picker",
"tags": "$:/tags/Macro",
"text": "\\define colour-picker-update-recent()\n<$action-listops\n\t$tiddler=\"$:/config/ColourPicker/Recent\"\n\t$subfilter=\"$(colour-picker-value)$ [list[$:/config/ColourPicker/Recent]remove[$(colour-picker-value)$]] +[limit[8]]\"\n/>\n\\end\n\n\\define colour-picker-inner(actions)\n<$button tag=\"a\" tooltip=\"\"\"$(colour-picker-value)$\"\"\">\n\n$(colour-picker-update-recent)$\n\n$actions$\n\n<div style=\"background-color: $(colour-picker-value)$; width: 100%; height: 100%; border-radius: 50%;\"/>\n\n</$button>\n\\end\n\n\\define colour-picker-recent-inner(actions)\n<$set name=\"colour-picker-value\" value=\"$(recentColour)$\">\n<$macrocall $name=\"colour-picker-inner\" actions=\"\"\"$actions$\"\"\"/>\n</$set>\n\\end\n\n\\define colour-picker-recent(actions)\n{{$:/language/ColourPicker/Recent}} <$list filter=\"[list[$:/config/ColourPicker/Recent]]\" variable=\"recentColour\">\n<$macrocall $name=\"colour-picker-recent-inner\" actions=\"\"\"$actions$\"\"\"/></$list>\n\\end\n\n\\define colour-picker(actions)\n<div class=\"tc-colour-chooser\">\n\n<$macrocall $name=\"colour-picker-recent\" actions=\"\"\"$actions$\"\"\"/>\n\n---\n\n<$list filter=\"LightPink Pink Crimson LavenderBlush PaleVioletRed HotPink DeepPink MediumVioletRed Orchid Thistle Plum Violet Magenta Fuchsia DarkMagenta Purple MediumOrchid DarkViolet DarkOrchid Indigo BlueViolet MediumPurple MediumSlateBlue SlateBlue DarkSlateBlue Lavender GhostWhite Blue MediumBlue MidnightBlue DarkBlue Navy RoyalBlue CornflowerBlue LightSteelBlue LightSlateGrey SlateGrey DodgerBlue AliceBlue SteelBlue LightSkyBlue SkyBlue DeepSkyBlue LightBlue PowderBlue CadetBlue Azure LightCyan PaleTurquoise Cyan Aqua DarkTurquoise DarkSlateGrey DarkCyan Teal MediumTurquoise LightSeaGreen Turquoise Aquamarine MediumAquamarine MediumSpringGreen MintCream SpringGreen MediumSeaGreen SeaGreen Honeydew LightGreen PaleGreen DarkSeaGreen LimeGreen Lime ForestGreen Green DarkGreen Chartreuse LawnGreen GreenYellow DarkOliveGreen YellowGreen OliveDrab Beige LightGoldenrodYellow Ivory LightYellow Yellow Olive DarkKhaki LemonChiffon PaleGoldenrod Khaki Gold Cornsilk Goldenrod DarkGoldenrod FloralWhite OldLace Wheat Moccasin Orange PapayaWhip BlanchedAlmond NavajoWhite AntiqueWhite Tan BurlyWood Bisque DarkOrange Linen Peru PeachPuff SandyBrown Chocolate SaddleBrown Seashell Sienna LightSalmon Coral OrangeRed DarkSalmon Tomato MistyRose Salmon Snow LightCoral RosyBrown IndianRed Red Brown FireBrick DarkRed Maroon White WhiteSmoke Gainsboro LightGrey Silver DarkGrey Grey DimGrey Black\" variable=\"colour-picker-value\">\n<$macrocall $name=\"colour-picker-inner\" actions=\"\"\"$actions$\"\"\"/>\n</$list>\n\n---\n\n<$edit-text tiddler=\"$:/config/ColourPicker/New\" tag=\"input\" default=\"\" placeholder=\"\"/> \n<$edit-text tiddler=\"$:/config/ColourPicker/New\" type=\"color\" tag=\"input\"/>\n<$set name=\"colour-picker-value\" value={{$:/config/ColourPicker/New}}>\n<$macrocall $name=\"colour-picker-inner\" actions=\"\"\"$actions$\"\"\"/>\n</$set>\n\n</div>\n\n\\end\n"
},
"$:/core/macros/export": {
"title": "$:/core/macros/export",
"tags": "$:/tags/Macro",
"text": "\\define exportButtonFilename(baseFilename)\n$baseFilename$$(extension)$\n\\end\n\n\\define exportButton(exportFilter:\"[!is[system]sort[title]]\",lingoBase,baseFilename:\"tiddlers\")\n<span class=\"tc-popup-keep\">\n<$button popup=<<qualify \"$:/state/popup/export\">> tooltip={{$lingoBase$Hint}} aria-label={{$lingoBase$Caption}} class=<<tv-config-toolbar-class>> selectedClass=\"tc-selected\">\n<$list filter=\"[<tv-config-toolbar-icons>prefix[yes]]\">\n{{$:/core/images/export-button}}\n</$list>\n<$list filter=\"[<tv-config-toolbar-text>prefix[yes]]\">\n<span class=\"tc-btn-text\"><$text text={{$lingoBase$Caption}}/></span>\n</$list>\n</$button>\n</span>\n<$reveal state=<<qualify \"$:/state/popup/export\">> type=\"popup\" position=\"below\" animate=\"yes\">\n<div class=\"tc-drop-down\">\n<$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Exporter]]\">\n<$set name=\"extension\" value={{!!extension}}>\n<$button class=\"tc-btn-invisible\">\n<$action-sendmessage $message=\"tm-download-file\" $param=<<currentTiddler>> exportFilter=\"\"\"$exportFilter$\"\"\" filename=<<exportButtonFilename \"\"\"$baseFilename$\"\"\">>/>\n<$action-deletetiddler $tiddler=<<qualify \"$:/state/popup/export\">>/>\n<$transclude field=\"description\"/>\n</$button>\n</$set>\n</$list>\n</div>\n</$reveal>\n\\end\n"
},
"$:/core/macros/image-picker": {
"title": "$:/core/macros/image-picker",
"tags": "$:/tags/Macro",
"text": "\\define image-picker-thumbnail(actions)\n<$button tag=\"a\" tooltip=\"\"\"$(imageTitle)$\"\"\">\n$actions$\n<$transclude tiddler=<<imageTitle>>/>\n</$button>\n\\end\n\n\\define image-picker-list(filter,actions)\n<$list filter=\"\"\"$filter$\"\"\" variable=\"imageTitle\">\n<$macrocall $name=\"image-picker-thumbnail\" actions=\"\"\"$actions$\"\"\"/>\n</$list>\n\\end\n\n\\define image-picker(actions,filter:\"[all[shadows+tiddlers]is[image]] -[type[application/pdf]] +[!has[draft.of]sort[title]]\")\n<div class=\"tc-image-chooser\">\n<$vars state-system=<<qualify \"$:/state/image-picker/system\">>>\n<$checkbox tiddler=<<state-system>> field=\"text\" checked=\"show\" unchecked=\"hide\" default=\"hide\">\n{{$:/language/SystemTiddlers/Include/Prompt}}\n</$checkbox>\n<$reveal state=<<state-system>> type=\"match\" text=\"hide\" default=\"hide\" tag=\"div\">\n<$macrocall $name=\"image-picker-list\" filter=\"\"\"$filter$ +[!is[system]]\"\"\" actions=\"\"\"$actions$\"\"\"/>\n</$reveal>\n<$reveal state=<<state-system>> type=\"nomatch\" text=\"hide\" default=\"hide\" tag=\"div\">\n<$macrocall $name=\"image-picker-list\" filter=\"\"\"$filter$\"\"\" actions=\"\"\"$actions$\"\"\"/>\n</$reveal>\n</$vars>\n</div>\n\\end\n\n\\define image-picker-include-tagged-images(actions)\n<$macrocall $name=\"image-picker\" filter=\"[all[shadows+tiddlers]is[image]] [all[shadows+tiddlers]tag[$:/tags/Image]] -[type[application/pdf]] +[!has[draft.of]sort[title]]\" actions=\"\"\"$actions$\"\"\"/>\n\\end\n"
},
"$:/core/macros/lingo": {
"title": "$:/core/macros/lingo",
"tags": "$:/tags/Macro",
"text": "\\define lingo-base()\n$:/language/\n\\end\n\n\\define lingo(title)\n{{$(lingo-base)$$title$}}\n\\end\n"
},
"$:/core/macros/list": {
"title": "$:/core/macros/list",
"tags": "$:/tags/Macro",
"text": "\\define list-links(filter,type:\"ul\",subtype:\"li\",class:\"\")\n<$type$ class=\"$class$\">\n<$list filter=\"$filter$\">\n<$subtype$>\n<$link to={{!!title}}>\n<$transclude field=\"caption\">\n<$view field=\"title\"/>\n</$transclude>\n</$link>\n</$subtype$>\n</$list>\n</$type$>\n\\end\n\n\\define list-links-draggable-drop-actions()\n<$action-listops $tiddler=<<targetTiddler>> $field=<<targetField>> $subfilter=\"+[insertbefore:currentTiddler<actionTiddler>]\"/>\n\\end\n\n\\define list-links-draggable(tiddler,field:\"list\",type:\"ul\",subtype:\"li\",class:\"\",itemTemplate)\n<$vars targetTiddler=\"\"\"$tiddler$\"\"\" targetField=\"\"\"$field$\"\"\">\n<$type$ class=\"$class$\">\n<$list filter=\"[list[$tiddler$!!$field$]]\">\n<$droppable actions=<<list-links-draggable-drop-actions>> tag=\"\"\"$subtype$\"\"\">\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n<div>\n<$link to={{!!title}}>\n<$transclude tiddler=\"\"\"$itemTemplate$\"\"\">\n<$transclude field=\"caption\">\n<$view field=\"title\"/>\n</$transclude>\n</$transclude>\n</$link>\n</div>\n</$droppable>\n</$list>\n<$tiddler tiddler=\"\">\n<$droppable actions=<<list-links-draggable-drop-actions>> tag=\"\"\"$subtype$\"\"\">\n<div class=\"tc-droppable-placeholder\">\n \n</div>\n<div>\n \n</div>\n</$droppable>\n</$tiddler>\n</$type$>\n</$vars>\n\\end\n\n\\define list-tagged-draggable-drop-actions()\n<!-- Save the current ordering of the tiddlers with this tag -->\n<$set name=\"order\" filter=\"[<tag>tagging[]]\">\n<!-- Remove any list-after or list-before fields from the tiddlers with this tag -->\n<$list filter=\"[<tag>tagging[]]\">\n<$action-deletefield $field=\"list-before\"/>\n<$action-deletefield $field=\"list-after\"/>\n</$list>\n<!-- Assign the list field of the tag with the current ordering -->\n<$action-setfield $tiddler=<<tag>> $field=\"list\" $value=<<order>>/>\n<!-- Add the newly inserted item to the list -->\n<$action-listops $tiddler=<<tag>> $field=\"list\" $subfilter=\"+[insertbefore:currentTiddler<actionTiddler>]\"/>\n<!-- Make sure the newly added item has the right tag -->\n<$action-listops $tiddler=<<actionTiddler>> $tags=\"[<tag>]\"/>\n</$set>\n\\end\n\n\\define list-tagged-draggable(tag,itemTemplate,elementTag:\"div\")\n<$set name=\"tag\" value=\"\"\"$tag$\"\"\">\n<$list filter=\"[<tag>tagging[]]\">\n<$elementTag$ class=\"tc-menu-list-item\">\n<$droppable actions=<<list-tagged-draggable-drop-actions>>>\n<$elementTag$ class=\"tc-droppable-placeholder\">\n \n</$elementTag$>\n<$elementTag$>\n<$transclude tiddler=\"\"\"$itemTemplate$\"\"\">\n<$link to={{!!title}}>\n<$view field=\"title\"/>\n</$link>\n</$transclude>\n</$elementTag$>\n</$droppable>\n</$elementTag$>\n</$list>\n<$tiddler tiddler=\"\">\n<$droppable actions=<<list-tagged-draggable-drop-actions>>>\n<$elementTag$ class=\"tc-droppable-placeholder\">\n \n</$elementTag$>\n<$elementTag$ style=\"height:0.5em;\">\n</$elementTag$>\n</$droppable>\n</$tiddler>\n</$set>\n\\end\n"
},
"$:/core/macros/tabs": {
"title": "$:/core/macros/tabs",
"tags": "$:/tags/Macro",
"text": "\\define tabs(tabsList,default,state:\"$:/state/tab\",class,template)\n<div class=\"tc-tab-set $class$\">\n<div class=\"tc-tab-buttons $class$\">\n<$list filter=\"$tabsList$\" variable=\"currentTab\"><$set name=\"save-currentTiddler\" value=<<currentTiddler>>><$tiddler tiddler=<<currentTab>>><$button set=<<qualify \"$state$\">> setTo=<<currentTab>> default=\"$default$\" selectedClass=\"tc-tab-selected\" tooltip={{!!tooltip}}>\n<$tiddler tiddler=<<save-currentTiddler>>>\n<$set name=\"tv-wikilinks\" value=\"no\">\n<$transclude tiddler=<<currentTab>> field=\"caption\">\n<$macrocall $name=\"currentTab\" $type=\"text/plain\" $output=\"text/plain\"/>\n</$transclude>\n</$set></$tiddler></$button></$tiddler></$set></$list>\n</div>\n<div class=\"tc-tab-divider $class$\"/>\n<div class=\"tc-tab-content $class$\">\n<$list filter=\"$tabsList$\" variable=\"currentTab\">\n\n<$reveal type=\"match\" state=<<qualify \"$state$\">> text=<<currentTab>> default=\"$default$\">\n\n<$transclude tiddler=\"$template$\" mode=\"block\">\n\n<$transclude tiddler=<<currentTab>> mode=\"block\"/>\n\n</$transclude>\n\n</$reveal>\n\n</$list>\n</div>\n</div>\n\\end\n"
},
"$:/core/macros/tag-picker": {
"title": "$:/core/macros/tag-picker",
"tags": "$:/tags/Macro",
"text": "\\define add-tag-actions()\n<$action-sendmessage $message=\"tm-add-tag\" $param={{$:/temp/NewTagName}}/>\n<$action-deletetiddler $tiddler=\"$:/temp/NewTagName\"/>\n\\end\n\n\\define tag-button()\n<$button class=\"tc-btn-invisible\" tag=\"a\">\n$(actions)$\n<$action-deletetiddler $tiddler=\"$:/temp/NewTagName\"/>\n<$macrocall $name=\"tag-pill\" tag=<<tag>>/>\n</$button>\n\\end\n\n\\define tag-picker(actions)\n<$set name=\"actions\" value=\"\"\"$actions$\"\"\">\n<div class=\"tc-edit-add-tag\">\n<span class=\"tc-add-tag-name\">\n<$keyboard key=\"ENTER\" actions=<<add-tag-actions>>>\n<$edit-text tiddler=\"$:/temp/NewTagName\" tag=\"input\" default=\"\" placeholder={{$:/language/EditTemplate/Tags/Add/Placeholder}} focusPopup=<<qualify \"$:/state/popup/tags-auto-complete\">> class=\"tc-edit-texteditor tc-popup-handle\"/>\n</$keyboard>\n</span> <$button popup=<<qualify \"$:/state/popup/tags-auto-complete\">> class=\"tc-btn-invisible\" tooltip={{$:/language/EditTemplate/Tags/Dropdown/Hint}} aria-label={{$:/language/EditTemplate/Tags/Dropdown/Caption}}>{{$:/core/images/down-arrow}}</$button> <span class=\"tc-add-tag-button\">\n<$set name=\"tag\" value={{$:/temp/NewTagName}}>\n<$button set=\"$:/temp/NewTagName\" setTo=\"\" class=\"\">\n$actions$\n<$action-deletetiddler $tiddler=\"$:/temp/NewTagName\"/>\n{{$:/language/EditTemplate/Tags/Add/Button}}\n</$button>\n</$set>\n</span>\n</div>\n<div class=\"tc-block-dropdown-wrapper\">\n<$reveal state=<<qualify \"$:/state/popup/tags-auto-complete\">> type=\"nomatch\" text=\"\" default=\"\">\n<div class=\"tc-block-dropdown\">\n<$list filter=\"[tags[]!is[system]search:title{$:/temp/NewTagName}sort[]]\" variable=\"tag\">\n<<tag-button>>\n</$list>\n<hr>\n<$list filter=\"[tags[]is[system]search:title{$:/temp/NewTagName}sort[]]\" variable=\"tag\">\n<<tag-button>>\n</$list>\n</div>\n</$reveal>\n</div>\n</$set>\n\\end\n"
},
"$:/core/macros/tag": {
"title": "$:/core/macros/tag",
"tags": "$:/tags/Macro",
"text": "\\define tag-pill-styles()\nbackground-color:$(backgroundColor)$;\nfill:$(foregroundColor)$;\ncolor:$(foregroundColor)$;\n\\end\n\n\\define tag-pill-inner(tag,icon,colour,fallbackTarget,colourA,colourB,element-tag,element-attributes,actions)\n<$vars foregroundColor=<<contrastcolour target:\"\"\"$colour$\"\"\" fallbackTarget:\"\"\"$fallbackTarget$\"\"\" colourA:\"\"\"$colourA$\"\"\" colourB:\"\"\"$colourB$\"\"\">> backgroundColor=\"\"\"$colour$\"\"\">\n<$element-tag$ $element-attributes$ class=\"tc-tag-label tc-btn-invisible\" style=<<tag-pill-styles>>>\n$actions$<$transclude tiddler=\"\"\"$icon$\"\"\"/> <$view tiddler=\"\"\"$tag$\"\"\" field=\"title\" format=\"text\" />\n</$element-tag$>\n</$vars>\n\\end\n\n\\define tag-pill-body(tag,icon,colour,palette,element-tag,element-attributes,actions)\n<$macrocall $name=\"tag-pill-inner\" tag=\"\"\"$tag$\"\"\" icon=\"\"\"$icon$\"\"\" colour=\"\"\"$colour$\"\"\" fallbackTarget={{$palette$##tag-background}} colourA={{$palette$##foreground}} colourB={{$palette$##background}} element-tag=\"\"\"$element-tag$\"\"\" element-attributes=\"\"\"$element-attributes$\"\"\" actions=\"\"\"$actions$\"\"\"/>\n\\end\n\n\\define tag-pill(tag,element-tag:\"span\",element-attributes:\"\",actions:\"\")\n<span class=\"tc-tag-list-item\">\n<$macrocall $name=\"tag-pill-body\" tag=\"\"\"$tag$\"\"\" icon={{$tag$!!icon}} colour={{$tag$!!color}} palette={{$:/palette}} element-tag=\"\"\"$element-tag$\"\"\" element-attributes=\"\"\"$element-attributes$\"\"\" actions=\"\"\"$actions$\"\"\"/>\n</span>\n\\end\n\n\\define tag(tag)\n{{$tag$||$:/core/ui/TagTemplate}}\n\\end\n"
},
"$:/core/macros/thumbnails": {
"title": "$:/core/macros/thumbnails",
"tags": "$:/tags/Macro",
"text": "\\define thumbnail(link,icon,color,background-color,image,caption,width:\"280\",height:\"157\")\n<$link to=\"\"\"$link$\"\"\"><div class=\"tc-thumbnail-wrapper\">\n<div class=\"tc-thumbnail-image\" style=\"width:$width$px;height:$height$px;\"><$reveal type=\"nomatch\" text=\"\" default=\"\"\"$image$\"\"\" tag=\"div\" style=\"width:$width$px;height:$height$px;\">\n[img[$image$]]\n</$reveal><$reveal type=\"match\" text=\"\" default=\"\"\"$image$\"\"\" tag=\"div\" class=\"tc-thumbnail-background\" style=\"width:$width$px;height:$height$px;background-color:$background-color$;\"></$reveal></div><div class=\"tc-thumbnail-icon\" style=\"fill:$color$;color:$color$;\">\n$icon$\n</div><div class=\"tc-thumbnail-caption\">\n$caption$\n</div>\n</div></$link>\n\\end\n\n\\define thumbnail-right(link,icon,color,background-color,image,caption,width:\"280\",height:\"157\")\n<div class=\"tc-thumbnail-right-wrapper\"><<thumbnail \"\"\"$link$\"\"\" \"\"\"$icon$\"\"\" \"\"\"$color$\"\"\" \"\"\"$background-color$\"\"\" \"\"\"$image$\"\"\" \"\"\"$caption$\"\"\" \"\"\"$width$\"\"\" \"\"\"$height$\"\"\">></div>\n\\end\n\n\\define list-thumbnails(filter,width:\"280\",height:\"157\")\n<$list filter=\"\"\"$filter$\"\"\"><$macrocall $name=\"thumbnail\" link={{!!link}} icon={{!!icon}} color={{!!color}} background-color={{!!background-color}} image={{!!image}} caption={{!!caption}} width=\"\"\"$width$\"\"\" height=\"\"\"$height$\"\"\"/></$list>\n\\end\n"
},
"$:/core/macros/timeline": {
"created": "20141212105914482",
"modified": "20141212110330815",
"tags": "$:/tags/Macro",
"title": "$:/core/macros/timeline",
"type": "text/vnd.tiddlywiki",
"text": "\\define timeline-title()\n<!-- Override this macro with a global macro \n of the same name if you need to change \n how titles are displayed on the timeline \n -->\n<$view field=\"title\"/>\n\\end\n\\define timeline(limit:\"100\",format:\"DDth MMM YYYY\",subfilter:\"\",dateField:\"modified\")\n<div class=\"tc-timeline\">\n<$list filter=\"[!is[system]$subfilter$has[$dateField$]!sort[$dateField$]limit[$limit$]eachday[$dateField$]]\">\n<div class=\"tc-menu-list-item\">\n<$view field=\"$dateField$\" format=\"date\" template=\"$format$\"/>\n<$list filter=\"[sameday:$dateField${!!$dateField$}!is[system]$subfilter$!sort[$dateField$]]\">\n<div class=\"tc-menu-list-subitem\">\n<$link to={{!!title}}>\n<<timeline-title>>\n</$link>\n</div>\n</$list>\n</div>\n</$list>\n</div>\n\\end\n"
},
"$:/core/macros/toc": {
"title": "$:/core/macros/toc",
"tags": "$:/tags/Macro",
"text": "\\define toc-caption()\n<$set name=\"tv-wikilinks\" value=\"no\">\n <$transclude field=\"caption\">\n <$view field=\"title\"/>\n </$transclude>\n</$set>\n\\end\n\n\\define toc-body(tag,sort:\"\",itemClassFilter,exclude,path)\n<ol class=\"tc-toc\">\n <$list filter=\"\"\"[all[shadows+tiddlers]tag[$tag$]!has[draft.of]$sort$] $exclude$\"\"\">\n <$vars item=<<currentTiddler>> path=\"\"\"$path$/$tag$\"\"\" excluded=\"\"\"$exclude$ -[[$tag$]]\"\"\">\n <$set name=\"toc-item-class\" filter=\"\"\"$itemClassFilter$\"\"\" emptyValue=\"toc-item\" value=\"toc-item-selected\">\n <li class=<<toc-item-class>>>\n <$list filter=\"[all[current]toc-link[no]]\" emptyMessage=\"<$link><$view field='caption'><$view field='title'/></$view></$link>\">\n <<toc-caption>>\n </$list>\n <$macrocall $name=\"toc-body\" tag=<<item>> sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"$itemClassFilter$\"\"\" exclude=<<excluded>> path=<<path>>/>\n </li>\n </$set>\n </$vars>\n </$list>\n</ol>\n\\end\n\n\\define toc(tag,sort:\"\",itemClassFilter:\" \")\n<<toc-body tag:\"\"\"$tag$\"\"\" sort:\"\"\"$sort$\"\"\" itemClassFilter:\"\"\"$itemClassFilter$\"\"\">>\n\\end\n\n\\define toc-linked-expandable-body(tag,sort:\"\",itemClassFilter,exclude,path)\n<!-- helper function -->\n<$set name=\"toc-state\" value=<<qualify \"\"\"$:/state/toc$path$-$(currentTiddler)$\"\"\">>>\n <$set name=\"toc-item-class\" filter=\"\"\"$itemClassFilter$\"\"\" emptyValue=\"toc-item\" value=\"toc-item-selected\">\n <li class=<<toc-item-class>>>\n <$link>\n <$reveal type=\"nomatch\" state=<<toc-state>> text=\"open\">\n <$button set=<<toc-state>> setTo=\"open\" class=\"tc-btn-invisible\">\n {{$:/core/images/right-arrow}}\n </$button>\n </$reveal>\n <$reveal type=\"match\" state=<<toc-state>> text=\"open\">\n <$button set=<<toc-state>> setTo=\"close\" class=\"tc-btn-invisible\">\n {{$:/core/images/down-arrow}}\n </$button>\n </$reveal>\n <<toc-caption>>\n </$link>\n <$reveal type=\"match\" state=<<toc-state>> text=\"open\">\n <$macrocall $name=\"toc-expandable\" tag=<<currentTiddler>> sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"$itemClassFilter$\"\"\" exclude=\"\"\"$exclude$\"\"\" path=\"\"\"$path$\"\"\"/>\n </$reveal>\n </li>\n </$set>\n</$set>\n\\end\n\n\\define toc-unlinked-expandable-body(tag,sort:\"\",itemClassFilter:\" \",exclude,path)\n<!-- helper function -->\n<$set name=\"toc-state\" value=<<qualify \"\"\"$:/state/toc$path$-$(currentTiddler)$\"\"\">>>\n <$set name=\"toc-item-class\" filter=\"\"\"$itemClassFilter$\"\"\" emptyValue=\"toc-item\" value=\"toc-item-selected\">\n <li class=<<toc-item-class>>>\n <$reveal type=\"nomatch\" state=<<toc-state>> text=\"open\">\n <$button set=<<toc-state>> setTo=\"open\" class=\"tc-btn-invisible\">\n {{$:/core/images/right-arrow}}\n <<toc-caption>>\n </$button>\n </$reveal>\n <$reveal type=\"match\" state=<<toc-state>> text=\"open\">\n <$button set=<<toc-state>> setTo=\"close\" class=\"tc-btn-invisible\">\n {{$:/core/images/down-arrow}}\n <<toc-caption>>\n </$button>\n </$reveal>\n <$reveal type=\"match\" state=<<toc-state>> text=\"open\">\n <$macrocall $name=\"toc-expandable\" tag=<<currentTiddler>> sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"$itemClassFilter$\"\"\" exclude=\"\"\"$exclude$\"\"\" path=\"\"\"$path$\"\"\"/>\n </$reveal>\n </li>\n </$set>\n</$set>\n\\end\n\n\\define toc-expandable-empty-message()\n<<toc-linked-expandable-body tag:\"\"\"$(tag)$\"\"\" sort:\"\"\"$(sort)$\"\"\" itemClassFilter:\"\"\"$(itemClassFilter)$\"\"\" exclude:\"\"\"$(excluded)$\"\"\" path:\"\"\"$(path)$\"\"\">>\n\\end\n\n\\define toc-expandable(tag,sort:\"\",itemClassFilter:\" \",exclude,path)\n<$vars tag=\"\"\"$tag$\"\"\" sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"$itemClassFilter$\"\"\" excluded=\"\"\"$exclude$ -[[$tag$]]\"\"\" path=\"\"\"$path$/$tag$\"\"\">\n <ol class=\"tc-toc toc-expandable\">\n <$list filter=\"\"\"[all[shadows+tiddlers]tag[$tag$]!has[draft.of]$sort$] $exclude$\"\"\">\n <$list filter=\"[all[current]toc-link[no]]\" emptyMessage=<<toc-expandable-empty-message>> >\n <$macrocall $name=\"toc-unlinked-expandable-body\" tag=\"\"\"$tag$\"\"\" sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"itemClassFilter\"\"\" exclude=<<excluded>> path=<<path>> />\n </$list>\n </$list>\n </ol>\n</$vars>\n\\end\n\n\\define toc-linked-selective-expandable-body(tag,sort:\"\",itemClassFilter:\" \",exclude,path)\n<$set name=\"toc-state\" value=<<qualify \"\"\"$:/state/toc$path$-$(currentTiddler)$\"\"\">>>\n <$set name=\"toc-item-class\" filter=\"\"\"$itemClassFilter$\"\"\" emptyValue=\"toc-item\" value=\"toc-item-selected\" >\n <li class=<<toc-item-class>>>\n <$link>\n <$list filter=\"[all[current]tagging[]limit[1]]\" variable=\"ignore\" emptyMessage=\"<$button class='tc-btn-invisible'>{{$:/core/images/blank}}</$button>\">\n <$reveal type=\"nomatch\" state=<<toc-state>> text=\"open\">\n <$button set=<<toc-state>> setTo=\"open\" class=\"tc-btn-invisible\">\n {{$:/core/images/right-arrow}}\n </$button>\n </$reveal>\n <$reveal type=\"match\" state=<<toc-state>> text=\"open\">\n <$button set=<<toc-state>> setTo=\"close\" class=\"tc-btn-invisible\">\n {{$:/core/images/down-arrow}}\n </$button>\n </$reveal>\n </$list>\n <<toc-caption>>\n </$link>\n <$reveal type=\"match\" state=<<toc-state>> text=\"open\">\n <$macrocall $name=\"toc-selective-expandable\" tag=<<currentTiddler>> sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"$itemClassFilter$\"\"\" exclude=\"\"\"$exclude$\"\"\" path=\"\"\"$path$\"\"\"/>\n </$reveal>\n </li>\n </$set>\n</$set>\n\\end\n\n\\define toc-unlinked-selective-expandable-body(tag,sort:\"\",itemClassFilter:\" \",exclude,path)\n<$set name=\"toc-state\" value=<<qualify \"\"\"$:/state/toc$path$-$(currentTiddler)$\"\"\">>>\n <$set name=\"toc-item-class\" filter=\"\"\"$itemClassFilter$\"\"\" emptyValue=\"toc-item\" value=\"toc-item-selected\">\n <li class=<<toc-item-class>>>\n <$list filter=\"[all[current]tagging[]limit[1]]\" variable=\"ignore\" emptyMessage=\"<$button class='tc-btn-invisible'>{{$:/core/images/blank}}</$button> <$view field='caption'><$view field='title'/></$view>\">\n <$reveal type=\"nomatch\" state=<<toc-state>> text=\"open\">\n <$button set=<<toc-state>> setTo=\"open\" class=\"tc-btn-invisible\">\n {{$:/core/images/right-arrow}}\n <<toc-caption>>\n </$button>\n </$reveal>\n <$reveal type=\"match\" state=<<toc-state>> text=\"open\">\n <$button set=<<toc-state>> setTo=\"close\" class=\"tc-btn-invisible\">\n {{$:/core/images/down-arrow}}\n <<toc-caption>>\n </$button>\n </$reveal>\n </$list>\n <$reveal type=\"match\" state=<<toc-state>> text=\"open\">\n <$macrocall $name=\"\"\"toc-selective-expandable\"\"\" tag=<<currentTiddler>> sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"$itemClassFilter$\"\"\" exclude=\"\"\"$exclude$\"\"\" path=\"\"\"$path$\"\"\"/>\n </$reveal>\n </li>\n </$set>\n</$set>\n\\end\n\n\\define toc-selective-expandable-empty-message()\n<<toc-linked-selective-expandable-body tag:\"\"\"$(tag)$\"\"\" sort:\"\"\"$(sort)$\"\"\" itemClassFilter:\"\"\"$(itemClassFilter)$\"\"\" exclude:\"\"\"$(excluded)$\"\"\" path:\"\"\"$(path)$\"\"\">>\n\\end\n\n\\define toc-selective-expandable(tag,sort:\"\",itemClassFilter,exclude,path)\n<$vars tag=\"\"\"$tag$\"\"\" sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"$itemClassFilter$\"\"\" excluded=\"\"\"$exclude$ -[[$tag$]]\"\"\" path=\"\"\"$path$/$tag$\"\"\">\n <ol class=\"tc-toc toc-selective-expandable\">\n <$list filter=\"\"\"[all[shadows+tiddlers]tag[$tag$]!has[draft.of]$sort$] $exclude$\"\"\">\n <$list filter=\"[all[current]toc-link[no]]\" variable=\"ignore\" emptyMessage=<<toc-selective-expandable-empty-message>> >\n <$macrocall $name=toc-unlinked-selective-expandable-body tag=\"\"\"$tag$\"\"\" sort=\"\"\"$sort$\"\"\" itemClassFilter=\"\"\"$itemClassFilter$\"\"\" exclude=<<excluded>> path=<<path>> >\n </$list>\n </$list>\n </ol>\n</$vars>\n\\end\n\n\\define toc-tabbed-selected-item-filter(selectedTiddler)\n[all[current]field:title{$selectedTiddler$}]\n\\end\n\n\\define toc-tabbed-external-nav(tag,sort:\"\",selectedTiddler:\"$:/temp/toc/selectedTiddler\",unselectedText,missingText,template:\"\")\n<$tiddler tiddler={{$selectedTiddler$}}>\n <div class=\"tc-tabbed-table-of-contents\">\n <$linkcatcher to=\"$selectedTiddler$\">\n <div class=\"tc-table-of-contents\">\n <$macrocall $name=\"toc-selective-expandable\" tag=\"\"\"$tag$\"\"\" sort=\"\"\"$sort$\"\"\" itemClassFilter=<<toc-tabbed-selected-item-filter selectedTiddler:\"\"\"$selectedTiddler$\"\"\">>/>\n </div>\n </$linkcatcher>\n <div class=\"tc-tabbed-table-of-contents-content\">\n <$reveal state=\"\"\"$selectedTiddler$\"\"\" type=\"nomatch\" text=\"\">\n <$transclude mode=\"block\" tiddler=\"$template$\">\n <h1><<toc-caption>></h1>\n <$transclude mode=\"block\">$missingText$</$transclude>\n </$transclude>\n </$reveal>\n <$reveal state=\"\"\"$selectedTiddler$\"\"\" type=\"match\" text=\"\">\n $unselectedText$\n </$reveal>\n </div>\n </div>\n</$tiddler>\n\\end\n\n\\define toc-tabbed-internal-nav(tag,sort:\"\",selectedTiddler:\"$:/temp/toc/selectedTiddler\",unselectedText,missingText,template:\"\")\n<$linkcatcher to=\"\"\"$selectedTiddler$\"\"\">\n <$macrocall $name=\"toc-tabbed-external-nav\" tag=\"\"\"$tag$\"\"\" sort=\"\"\"$sort$\"\"\" selectedTiddler=\"\"\"$selectedTiddler$\"\"\" unselectedText=\"\"\"$unselectedText$\"\"\" missingText=\"\"\"$missingText$\"\"\" template=\"\"\"$template$\"\"\"/>\n</$linkcatcher>\n\\end\n\n"
},
"$:/core/macros/translink": {
"title": "$:/core/macros/translink",
"tags": "$:/tags/Macro",
"text": "\\define translink(title,mode:\"block\")\n<div style=\"border:1px solid #ccc; padding: 0.5em; background: black; foreground; white;\">\n<$link to=\"\"\"$title$\"\"\">\n<$text text=\"\"\"$title$\"\"\"/>\n</$link>\n<div style=\"border:1px solid #ccc; padding: 0.5em; background: white; foreground; black;\">\n<$transclude tiddler=\"\"\"$title$\"\"\" mode=\"$mode$\">\n\"<$text text=\"\"\"$title$\"\"\"/>\" is missing\n</$transclude>\n</div>\n</div>\n\\end\n"
},
"$:/snippets/minilanguageswitcher": {
"title": "$:/snippets/minilanguageswitcher",
"text": "<$select tiddler=\"$:/language\">\n<$list filter=\"[[$:/languages/en-GB]] [plugin-type[language]sort[title]]\">\n<option value=<<currentTiddler>>><$view field=\"description\"><$view field=\"name\"><$view field=\"title\"/></$view></$view></option>\n</$list>\n</$select>"
},
"$:/snippets/minithemeswitcher": {
"title": "$:/snippets/minithemeswitcher",
"text": "\\define lingo-base() $:/language/ControlPanel/Theme/\n<<lingo Prompt>> <$select tiddler=\"$:/theme\">\n<$list filter=\"[plugin-type[theme]sort[title]]\">\n<option value=<<currentTiddler>>><$view field=\"name\"><$view field=\"title\"/></$view></option>\n</$list>\n</$select>"
},
"$:/snippets/modules": {
"title": "$:/snippets/modules",
"text": "\\define describeModuleType(type)\n{{$:/language/Docs/ModuleTypes/$type$}}\n\\end\n<$list filter=\"[moduletypes[]]\">\n\n!! <$macrocall $name=\"currentTiddler\" $type=\"text/plain\" $output=\"text/plain\"/>\n\n<$macrocall $name=\"describeModuleType\" type=<<currentTiddler>>/>\n\n<ul><$list filter=\"[all[current]modules[]]\"><li><$link><<currentTiddler>></$link>\n</li>\n</$list>\n</ul>\n</$list>\n"
},
"$:/palette": {
"title": "$:/palette",
"text": "$:/palettes/Vanilla"
},
"$:/snippets/paletteeditor": {
"title": "$:/snippets/paletteeditor",
"text": "\\define lingo-base() $:/language/ControlPanel/Palette/Editor/\n\\define describePaletteColour(colour)\n<$transclude tiddler=\"$:/language/Docs/PaletteColours/$colour$\"><$text text=\"$colour$\"/></$transclude>\n\\end\n<$set name=\"currentTiddler\" value={{$:/palette}}>\n\n<<lingo Prompt>> <$link to={{$:/palette}}><$macrocall $name=\"currentTiddler\" $output=\"text/plain\"/></$link>\n\n<$list filter=\"[all[current]is[shadow]is[tiddler]]\" variable=\"listItem\">\n<<lingo Prompt/Modified>>\n<$button message=\"tm-delete-tiddler\" param={{$:/palette}}><<lingo Reset/Caption>></$button>\n</$list>\n\n<$list filter=\"[all[current]is[shadow]!is[tiddler]]\" variable=\"listItem\">\n<<lingo Clone/Prompt>>\n</$list>\n\n<$button message=\"tm-new-tiddler\" param={{$:/palette}}><<lingo Clone/Caption>></$button>\n\n<table>\n<tbody>\n<$list filter=\"[all[current]indexes[]]\" variable=\"colourName\">\n<tr>\n<td>\n''<$macrocall $name=\"describePaletteColour\" colour=<<colourName>>/>''<br/>\n<$macrocall $name=\"colourName\" $output=\"text/plain\"/>\n</td>\n<td>\n<$edit-text index=<<colourName>> tag=\"input\"/>\n<br>\n<$edit-text index=<<colourName>> type=\"color\" tag=\"input\"/>\n</td>\n</tr>\n</$list>\n</tbody>\n</table>\n</$set>\n"
},
"$:/snippets/palettepreview": {
"title": "$:/snippets/palettepreview",
"text": "<$set name=\"currentTiddler\" value={{$:/palette}}>\n<$transclude tiddler=\"$:/snippets/currpalettepreview\"/>\n</$set>\n"
},
"$:/snippets/paletteswitcher": {
"title": "$:/snippets/paletteswitcher",
"text": "\\define lingo-base() $:/language/ControlPanel/Palette/\n<div class=\"tc-prompt\">\n<<lingo Prompt>> <$view tiddler={{$:/palette}} field=\"name\"/>\n</div>\n\n<$linkcatcher to=\"$:/palette\">\n<div class=\"tc-chooser\"><$list filter=\"[all[shadows+tiddlers]tag[$:/tags/Palette]sort[description]]\"><div class=\"tc-chooser-item\"><$link to={{!!title}}><div><$reveal state=\"$:/palette\" type=\"match\" text={{!!title}}>•</$reveal><$reveal state=\"$:/palette\" type=\"nomatch\" text={{!!title}}> </$reveal> ''<$view field=\"name\" format=\"text\"/>'' - <$view field=\"description\" format=\"text\"/></div><$transclude tiddler=\"$:/snippets/currpalettepreview\"/></$link></div>\n</$list>\n</div>\n</$linkcatcher>"
},
"$:/temp/search": {
"title": "$:/temp/search",
"text": ""
},
"$:/tags/AdvancedSearch": {
"title": "$:/tags/AdvancedSearch",
"list": "[[$:/core/ui/AdvancedSearch/Standard]] [[$:/core/ui/AdvancedSearch/System]] [[$:/core/ui/AdvancedSearch/Shadows]] [[$:/core/ui/AdvancedSearch/Filter]]"
},
"$:/tags/AdvancedSearch/FilterButton": {
"title": "$:/tags/AdvancedSearch/FilterButton",
"list": "$:/core/ui/AdvancedSearch/Filter/FilterButtons/dropdown $:/core/ui/AdvancedSearch/Filter/FilterButtons/clear $:/core/ui/AdvancedSearch/Filter/FilterButtons/export $:/core/ui/AdvancedSearch/Filter/FilterButtons/delete"
},
"$:/tags/ControlPanel": {
"title": "$:/tags/ControlPanel",
"list": "$:/core/ui/ControlPanel/Info $:/core/ui/ControlPanel/Appearance $:/core/ui/ControlPanel/Settings $:/core/ui/ControlPanel/Saving $:/core/ui/ControlPanel/Plugins $:/core/ui/ControlPanel/Tools $:/core/ui/ControlPanel/Internals"
},
"$:/tags/ControlPanel/Info": {
"title": "$:/tags/ControlPanel/Info",
"list": "$:/core/ui/ControlPanel/Basics $:/core/ui/ControlPanel/Advanced"
},
"$:/tags/ControlPanel/Plugins": {
"title": "$:/tags/ControlPanel/Plugins",
"list": "[[$:/core/ui/ControlPanel/Plugins/Installed]] [[$:/core/ui/ControlPanel/Plugins/Add]]"
},
"$:/tags/EditTemplate": {
"title": "$:/tags/EditTemplate",
"list": "[[$:/core/ui/EditTemplate/controls]] [[$:/core/ui/EditTemplate/title]] [[$:/core/ui/EditTemplate/tags]] [[$:/core/ui/EditTemplate/shadow]] [[$:/core/ui/ViewTemplate/classic]] [[$:/core/ui/EditTemplate/body]] [[$:/core/ui/EditTemplate/type]] [[$:/core/ui/EditTemplate/fields]]"
},
"$:/tags/EditToolbar": {
"title": "$:/tags/EditToolbar",
"list": "[[$:/core/ui/Buttons/delete]] [[$:/core/ui/Buttons/cancel]] [[$:/core/ui/Buttons/save]]"
},
"$:/tags/EditorToolbar": {
"title": "$:/tags/EditorToolbar",
"list": "$:/core/ui/EditorToolbar/paint $:/core/ui/EditorToolbar/opacity $:/core/ui/EditorToolbar/line-width $:/core/ui/EditorToolbar/clear $:/core/ui/EditorToolbar/bold $:/core/ui/EditorToolbar/italic $:/core/ui/EditorToolbar/strikethrough $:/core/ui/EditorToolbar/underline $:/core/ui/EditorToolbar/superscript $:/core/ui/EditorToolbar/subscript $:/core/ui/EditorToolbar/mono-line $:/core/ui/EditorToolbar/mono-block $:/core/ui/EditorToolbar/quote $:/core/ui/EditorToolbar/list-bullet $:/core/ui/EditorToolbar/list-number $:/core/ui/EditorToolbar/heading-1 $:/core/ui/EditorToolbar/heading-2 $:/core/ui/EditorToolbar/heading-3 $:/core/ui/EditorToolbar/heading-4 $:/core/ui/EditorToolbar/heading-5 $:/core/ui/EditorToolbar/heading-6 $:/core/ui/EditorToolbar/link $:/core/ui/EditorToolbar/excise $:/core/ui/EditorToolbar/picture $:/core/ui/EditorToolbar/stamp $:/core/ui/EditorToolbar/size $:/core/ui/EditorToolbar/editor-height $:/core/ui/EditorToolbar/more $:/core/ui/EditorToolbar/preview $:/core/ui/EditorToolbar/preview-type"
},
"$:/tags/Manager/ItemMain": {
"title": "$:/tags/Manager/ItemMain",
"list": "$:/Manager/ItemMain/WikifiedText $:/Manager/ItemMain/RawText $:/Manager/ItemMain/Fields"
},
"$:/tags/Manager/ItemSidebar": {
"title": "$:/tags/Manager/ItemSidebar",
"list": "$:/Manager/ItemSidebar/Tags $:/Manager/ItemSidebar/Colour $:/Manager/ItemSidebar/Icon $:/Manager/ItemSidebar/Tools"
},
"$:/tags/MoreSideBar": {
"title": "$:/tags/MoreSideBar",
"list": "[[$:/core/ui/MoreSideBar/All]] [[$:/core/ui/MoreSideBar/Recent]] [[$:/core/ui/MoreSideBar/Tags]] [[$:/core/ui/MoreSideBar/Missing]] [[$:/core/ui/MoreSideBar/Drafts]] [[$:/core/ui/MoreSideBar/Orphans]] [[$:/core/ui/MoreSideBar/Types]] [[$:/core/ui/MoreSideBar/System]] [[$:/core/ui/MoreSideBar/Shadows]] [[$:/core/ui/MoreSideBar/Plugins]]",
"text": ""
},
"$:/tags/PageControls": {
"title": "$:/tags/PageControls",
"list": "[[$:/core/ui/Buttons/home]] [[$:/core/ui/Buttons/close-all]] [[$:/core/ui/Buttons/fold-all]] [[$:/core/ui/Buttons/unfold-all]] [[$:/core/ui/Buttons/permaview]] [[$:/core/ui/Buttons/new-tiddler]] [[$:/core/ui/Buttons/new-journal]] [[$:/core/ui/Buttons/new-image]] [[$:/core/ui/Buttons/import]] [[$:/core/ui/Buttons/export-page]] [[$:/core/ui/Buttons/control-panel]] [[$:/core/ui/Buttons/advanced-search]] [[$:/core/ui/Buttons/manager]] [[$:/core/ui/Buttons/tag-manager]] [[$:/core/ui/Buttons/language]] [[$:/core/ui/Buttons/palette]] [[$:/core/ui/Buttons/theme]] [[$:/core/ui/Buttons/storyview]] [[$:/core/ui/Buttons/encryption]] [[$:/core/ui/Buttons/timestamp]] [[$:/core/ui/Buttons/full-screen]] [[$:/core/ui/Buttons/print]] [[$:/core/ui/Buttons/save-wiki]] [[$:/core/ui/Buttons/refresh]] [[$:/core/ui/Buttons/more-page-actions]]"
},
"$:/tags/PageTemplate": {
"title": "$:/tags/PageTemplate",
"list": "[[$:/core/ui/PageTemplate/topleftbar]] [[$:/core/ui/PageTemplate/toprightbar]] [[$:/core/ui/PageTemplate/sidebar]] [[$:/core/ui/PageTemplate/story]] [[$:/core/ui/PageTemplate/alerts]]",
"text": ""
},
"$:/tags/SideBar": {
"title": "$:/tags/SideBar",
"list": "[[$:/core/ui/SideBar/Open]] [[$:/core/ui/SideBar/Recent]] [[$:/core/ui/SideBar/Tools]] [[$:/core/ui/SideBar/More]]",
"text": ""
},
"$:/tags/TiddlerInfo": {
"title": "$:/tags/TiddlerInfo",
"list": "[[$:/core/ui/TiddlerInfo/Tools]] [[$:/core/ui/TiddlerInfo/References]] [[$:/core/ui/TiddlerInfo/Tagging]] [[$:/core/ui/TiddlerInfo/List]] [[$:/core/ui/TiddlerInfo/Listed]] [[$:/core/ui/TiddlerInfo/Fields]]",
"text": ""
},
"$:/tags/TiddlerInfo/Advanced": {
"title": "$:/tags/TiddlerInfo/Advanced",
"list": "[[$:/core/ui/TiddlerInfo/Advanced/ShadowInfo]] [[$:/core/ui/TiddlerInfo/Advanced/PluginInfo]]"
},
"$:/tags/ViewTemplate": {
"title": "$:/tags/ViewTemplate",
"list": "[[$:/core/ui/ViewTemplate/title]] [[$:/core/ui/ViewTemplate/unfold]] [[$:/core/ui/ViewTemplate/subtitle]] [[$:/core/ui/ViewTemplate/tags]] [[$:/core/ui/ViewTemplate/classic]] [[$:/core/ui/ViewTemplate/body]]"
},
"$:/tags/ViewToolbar": {
"title": "$:/tags/ViewToolbar",
"list": "[[$:/core/ui/Buttons/more-tiddler-actions]] [[$:/core/ui/Buttons/info]] [[$:/core/ui/Buttons/new-here]] [[$:/core/ui/Buttons/new-journal-here]] [[$:/core/ui/Buttons/clone]] [[$:/core/ui/Buttons/export-tiddler]] [[$:/core/ui/Buttons/edit]] [[$:/core/ui/Buttons/delete]] [[$:/core/ui/Buttons/permalink]] [[$:/core/ui/Buttons/permaview]] [[$:/core/ui/Buttons/open-window]] [[$:/core/ui/Buttons/close-others]] [[$:/core/ui/Buttons/close]] [[$:/core/ui/Buttons/fold-others]] [[$:/core/ui/Buttons/fold]]"
},
"$:/snippets/themeswitcher": {
"title": "$:/snippets/themeswitcher",
"text": "\\define lingo-base() $:/language/ControlPanel/Theme/\n<<lingo Prompt>> <$view tiddler={{$:/theme}} field=\"name\"/>\n\n<$linkcatcher to=\"$:/theme\">\n<$list filter=\"[plugin-type[theme]sort[title]]\"><div><$reveal state=\"$:/theme\" type=\"match\" text={{!!title}}>•</$reveal><$reveal state=\"$:/theme\" type=\"nomatch\" text={{!!title}}> </$reveal> <$link to={{!!title}}>''<$view field=\"name\" format=\"text\"/>'' <$view field=\"description\" format=\"text\"/></$link></div>\n</$list>\n</$linkcatcher>"
},
"$:/core/wiki/title": {
"title": "$:/core/wiki/title",
"type": "text/vnd.tiddlywiki",
"text": "{{$:/SiteTitle}} --- {{$:/SiteSubtitle}}"
},
"$:/view": {
"title": "$:/view",
"text": "classic"
},
"$:/snippets/viewswitcher": {
"title": "$:/snippets/viewswitcher",
"text": "\\define lingo-base() $:/language/ControlPanel/StoryView/\n<<lingo Prompt>> <$select tiddler=\"$:/view\">\n<$list filter=\"[storyviews[]]\">\n<option><$view field=\"title\"/></option>\n</$list>\n</$select>"
}
}
}
[[Welcome Page]] [[Blog]] [[Paper Reading Group 2017]]
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$:/languages/en-GB
$:/palettes/Rocker
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{
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"modified": "20140520221208420",
"title": "$:/plugins/kpe/mathjax/mathjax.cdn.tid",
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"tags": "$:/tags/RawMarkup $:/core/wiki/rawmarkup",
"text": "<script type=\"text/x-mathjax-config\">\n(function(){\n MathJax.Hub.Config({\n jax: [\"input/TeX\",\"output/HTML-CSS\"],\n extensions: [\"tex2jax.js\",\"MathMenu.js\",\"MathZoom.js\"],\n tex2jax: {\n inlineMath: [\n ['$','$'],\n ['\\\\\\\\(','\\\\\\\\)']\n ],\n processEscapes: true\n },\n TeX: {\n extensions: [\"AMSsymbols.js\",\"AMSmath.js\"]\n }\n });\n})();\n</script>\n<script src=\"https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML\" type=\"text/javascript\"></script>"
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"title": "$:/plugins/kpe/mathjax/mathjax.js",
"type": "application/javascript",
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{
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"text": "codemirror"
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"$:/config/EditorTypeMappings/text/css": {
"title": "$:/config/EditorTypeMappings/text/css",
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"$:/config/CodeMirror": {
"title": "$:/config/CodeMirror",
"type": "application/json",
"text": "{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/addon/dialog/dialog.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/search/searchcursor.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/mode/multiplex.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/css/css.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/htmlembedded/htmlembedded.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/htmlmixed/htmlmixed.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/markdown/markdown.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/meta.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/tiddlywiki/tiddlywiki.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js\",\n \"$:/plugins/tiddlywiki/codemirror/keymap/vim.js\",\n \"$:/plugins/tiddlywiki/codemirror/keymap/emacs.js\"\n ],\n \"configuration\": {\n \"matchBrackets\": true,\n \"showCursorWhenSelecting\": true\n }\n}"
},
"$:/plugins/tiddlywiki/codemirror/edit-codemirror.js": {
"text": "/*\\\ntitle: $:/plugins/tiddlywiki/codemirror/edit-codemirror.js\ntype: application/javascript\nmodule-type: widget\n\nEdit-codemirror widget\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar editTextWidgetFactory = require(\"$:/core/modules/editor/factory.js\").editTextWidgetFactory,\n\tCodeMirrorEngine = require(\"$:/plugins/tiddlywiki/codemirror/engine.js\").CodeMirrorEngine;\n\nexports[\"edit-codemirror\"] = editTextWidgetFactory(CodeMirrorEngine,CodeMirrorEngine);\n\n})();\n",
"title": "$:/plugins/tiddlywiki/codemirror/edit-codemirror.js",
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"$:/plugins/tiddlywiki/codemirror/engine.js": {
"text": "/*\\\ntitle: $:/plugins/tiddlywiki/codemirror/engine.js\ntype: application/javascript\nmodule-type: library\n\nText editor engine based on a CodeMirror instance\n\n\\*/\n(function(){\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar CODEMIRROR_OPTIONS = \"$:/config/CodeMirror\",\n\tHEIGHT_VALUE_TITLE = \"$:/config/TextEditor/EditorHeight/Height\"\n\n// Install CodeMirror\nif($tw.browser && !window.CodeMirror) {\n\twindow.CodeMirror = require(\"$:/plugins/tiddlywiki/codemirror/lib/codemirror.js\");\n\t// Install required CodeMirror plugins\n\tvar configOptions = $tw.wiki.getTiddlerData(CODEMIRROR_OPTIONS,{}),\n\t\treq = configOptions.require;\n\tif(req) {\n\t\tif($tw.utils.isArray(req)) {\n\t\t\tfor(var index=0; index<req.length; index++) {\n\t\t\t\trequire(req[index]);\n\t\t\t}\n\t\t} else {\n\t\t\trequire(req);\n\t\t}\n\t}\n}\n\nfunction CodeMirrorEngine(options) {\n\t// Save our options\n\tvar self = this;\n\toptions = options || {};\n\tthis.widget = options.widget;\n\tthis.value = options.value;\n\tthis.parentNode = options.parentNode;\n\tthis.nextSibling = options.nextSibling;\n\t// Create the wrapper DIV\n\tthis.domNode = this.widget.document.createElement(\"div\");\n\tif(this.widget.editClass) {\n\t\tthis.domNode.className = this.widget.editClass;\n\t}\n\tthis.domNode.style.display = \"inline-block\";\n\tthis.parentNode.insertBefore(this.domNode,this.nextSibling);\n\tthis.widget.domNodes.push(this.domNode);\n\t// Get the configuration options for the CodeMirror object\n\tvar config = $tw.wiki.getTiddlerData(CODEMIRROR_OPTIONS,{}).configuration || {};\n\tif(!(\"lineWrapping\" in config)) {\n\t\tconfig.lineWrapping = true;\n\t}\n\tif(!(\"lineNumbers\" in config)) {\n\t\tconfig.lineNumbers = true;\n\t}\n\tconfig.mode = options.type;\n\tconfig.value = options.value;\n\t// Create the CodeMirror instance\n\tthis.cm = window.CodeMirror(function(cmDomNode) {\n\t\t// Note that this is a synchronous callback that is called before the constructor returns\n\t\tself.domNode.appendChild(cmDomNode);\n\t},config);\n\t// Set up a change event handler\n\tthis.cm.on(\"change\",function() {\n\t\tself.widget.saveChanges(self.getText());\n\t});\n\tthis.cm.on(\"drop\",function(cm,event) {\n\t\tevent.stopPropagation(); // Otherwise TW's dropzone widget sees the drop event\n\t\treturn false;\n\t});\n\tthis.cm.on(\"keydown\",function(cm,event) {\n\t\treturn self.widget.handleKeydownEvent.call(self.widget,event);\n\t});\n}\n\n/*\nSet the text of the engine if it doesn't currently have focus\n*/\nCodeMirrorEngine.prototype.setText = function(text,type) {\n\tthis.cm.setOption(\"mode\",type);\n\tif(!this.cm.hasFocus()) {\n\t\tthis.cm.setValue(text);\n\t}\n};\n\n/*\nGet the text of the engine\n*/\nCodeMirrorEngine.prototype.getText = function() {\n\treturn this.cm.getValue();\n};\n\n/*\nFix the height of textarea to fit content\n*/\nCodeMirrorEngine.prototype.fixHeight = function() {\n\tif(this.widget.editAutoHeight) {\n\t\t// Resize to fit\n\t\tthis.cm.setSize(null,null);\n\t} else {\n\t\tvar fixedHeight = parseInt(this.widget.wiki.getTiddlerText(HEIGHT_VALUE_TITLE,\"400px\"),10);\n\t\tfixedHeight = Math.max(fixedHeight,20);\n\t\tthis.cm.setSize(null,fixedHeight);\n\t}\n};\n\n/*\nFocus the engine node\n*/\nCodeMirrorEngine.prototype.focus = function() {\n\tthis.cm.focus();\n}\n\n/*\nCreate a blank structure representing a text operation\n*/\nCodeMirrorEngine.prototype.createTextOperation = function() {\n\tvar selections = this.cm.listSelections();\n\tif(selections.length > 0) {\n\t\tvar anchorPos = this.cm.indexFromPos(selections[0].anchor),\n\t\t\theadPos = this.cm.indexFromPos(selections[0].head);\n\t}\n\tvar operation = {\n\t\ttext: this.cm.getValue(),\n\t\tselStart: Math.min(anchorPos,headPos),\n\t\tselEnd: Math.max(anchorPos,headPos),\n\t\tcutStart: null,\n\t\tcutEnd: null,\n\t\treplacement: null,\n\t\tnewSelStart: null,\n\t\tnewSelEnd: null\n\t};\n\toperation.selection = operation.text.substring(operation.selStart,operation.selEnd);\n\treturn operation;\n};\n\n/*\nExecute a text operation\n*/\nCodeMirrorEngine.prototype.executeTextOperation = function(operation) {\n\t// Perform the required changes to the text area and the underlying tiddler\n\tvar newText = operation.text;\n\tif(operation.replacement !== null) {\n\t\tthis.cm.replaceRange(operation.replacement,this.cm.posFromIndex(operation.cutStart),this.cm.posFromIndex(operation.cutEnd));\n\t\tthis.cm.setSelection(this.cm.posFromIndex(operation.newSelStart),this.cm.posFromIndex(operation.newSelEnd));\n\t\tnewText = operation.text.substring(0,operation.cutStart) + operation.replacement + operation.text.substring(operation.cutEnd);\n\t}\n\tthis.cm.focus();\n\treturn newText;\n};\n\nexports.CodeMirrorEngine = CodeMirrorEngine;\n\n})();\n",
"title": "$:/plugins/tiddlywiki/codemirror/engine.js",
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},
"$:/plugins/tiddlywiki/codemirror/lib/codemirror.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n// This is CodeMirror (http://codemirror.net), a code editor\n// implemented in JavaScript on top of the browser's DOM.\n//\n// You can find some technical background for some of the code below\n// at http://marijnhaverbeke.nl/blog/#cm-internals .\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n module.exports = mod();\n else if (typeof define == \"function\" && define.amd) // AMD\n return define([], mod);\n else // Plain browser env\n (this || window).CodeMirror = mod();\n})(function() {\n \"use strict\";\n\n // BROWSER SNIFFING\n\n // Kludges for bugs and behavior differences that can't be feature\n // detected are enabled based on userAgent etc sniffing.\n var userAgent = navigator.userAgent;\n var platform = navigator.platform;\n\n var gecko = /gecko\\/\\d/i.test(userAgent);\n var ie_upto10 = /MSIE \\d/.test(userAgent);\n var ie_11up = /Trident\\/(?:[7-9]|\\d{2,})\\..*rv:(\\d+)/.exec(userAgent);\n var ie = ie_upto10 || ie_11up;\n var ie_version = ie && (ie_upto10 ? document.documentMode || 6 : ie_11up[1]);\n var webkit = /WebKit\\//.test(userAgent);\n var qtwebkit = webkit && /Qt\\/\\d+\\.\\d+/.test(userAgent);\n var chrome = /Chrome\\//.test(userAgent);\n var presto = /Opera\\//.test(userAgent);\n var safari = /Apple Computer/.test(navigator.vendor);\n var mac_geMountainLion = /Mac OS X 1\\d\\D([8-9]|\\d\\d)\\D/.test(userAgent);\n var phantom = /PhantomJS/.test(userAgent);\n\n var ios = /AppleWebKit/.test(userAgent) && /Mobile\\/\\w+/.test(userAgent);\n // This is woefully incomplete. Suggestions for alternative methods welcome.\n var mobile = ios || /Android|webOS|BlackBerry|Opera Mini|Opera Mobi|IEMobile/i.test(userAgent);\n var mac = ios || /Mac/.test(platform);\n var windows = /win/i.test(platform);\n\n var presto_version = presto && userAgent.match(/Version\\/(\\d*\\.\\d*)/);\n if (presto_version) presto_version = Number(presto_version[1]);\n if (presto_version && presto_version >= 15) { presto = false; webkit = true; }\n // Some browsers use the wrong event properties to signal cmd/ctrl on OS X\n var flipCtrlCmd = mac && (qtwebkit || presto && (presto_version == null || presto_version < 12.11));\n var captureRightClick = gecko || (ie && ie_version >= 9);\n\n // Optimize some code when these features are not used.\n var sawReadOnlySpans = false, sawCollapsedSpans = false;\n\n // EDITOR CONSTRUCTOR\n\n // A CodeMirror instance represents an editor. This is the object\n // that user code is usually dealing with.\n\n function CodeMirror(place, options) {\n if (!(this instanceof CodeMirror)) return new CodeMirror(place, options);\n\n this.options = options = options ? copyObj(options) : {};\n // Determine effective options based on given values and defaults.\n copyObj(defaults, options, false);\n setGuttersForLineNumbers(options);\n\n var doc = options.value;\n if (typeof doc == \"string\") doc = new Doc(doc, options.mode, null, options.lineSeparator);\n this.doc = doc;\n\n var input = new CodeMirror.inputStyles[options.inputStyle](this);\n var display = this.display = new Display(place, doc, input);\n display.wrapper.CodeMirror = this;\n updateGutters(this);\n themeChanged(this);\n if (options.lineWrapping)\n this.display.wrapper.className += \" CodeMirror-wrap\";\n if (options.autofocus && !mobile) display.input.focus();\n initScrollbars(this);\n\n this.state = {\n keyMaps: [], // stores maps added by addKeyMap\n overlays: [], // highlighting overlays, as added by addOverlay\n modeGen: 0, // bumped when mode/overlay changes, used to invalidate highlighting info\n overwrite: false,\n delayingBlurEvent: false,\n focused: false,\n suppressEdits: false, // used to disable editing during key handlers when in readOnly mode\n pasteIncoming: false, cutIncoming: false, // help recognize paste/cut edits in input.poll\n selectingText: false,\n draggingText: false,\n highlight: new Delayed(), // stores highlight worker timeout\n keySeq: null, // Unfinished key sequence\n specialChars: null\n };\n\n var cm = this;\n\n // Override magic textarea content restore that IE sometimes does\n // on our hidden textarea on reload\n if (ie && ie_version < 11) setTimeout(function() { cm.display.input.reset(true); }, 20);\n\n registerEventHandlers(this);\n ensureGlobalHandlers();\n\n startOperation(this);\n this.curOp.forceUpdate = true;\n attachDoc(this, doc);\n\n if ((options.autofocus && !mobile) || cm.hasFocus())\n setTimeout(bind(onFocus, this), 20);\n else\n onBlur(this);\n\n for (var opt in optionHandlers) if (optionHandlers.hasOwnProperty(opt))\n optionHandlers[opt](this, options[opt], Init);\n maybeUpdateLineNumberWidth(this);\n if (options.finishInit) options.finishInit(this);\n for (var i = 0; i < initHooks.length; ++i) initHooks[i](this);\n endOperation(this);\n // Suppress optimizelegibility in Webkit, since it breaks text\n // measuring on line wrapping boundaries.\n if (webkit && options.lineWrapping &&\n getComputedStyle(display.lineDiv).textRendering == \"optimizelegibility\")\n display.lineDiv.style.textRendering = \"auto\";\n }\n\n // DISPLAY CONSTRUCTOR\n\n // The display handles the DOM integration, both for input reading\n // and content drawing. It holds references to DOM nodes and\n // display-related state.\n\n function Display(place, doc, input) {\n var d = this;\n this.input = input;\n\n // Covers bottom-right square when both scrollbars are present.\n d.scrollbarFiller = elt(\"div\", null, \"CodeMirror-scrollbar-filler\");\n d.scrollbarFiller.setAttribute(\"cm-not-content\", \"true\");\n // Covers bottom of gutter when coverGutterNextToScrollbar is on\n // and h scrollbar is present.\n d.gutterFiller = elt(\"div\", null, \"CodeMirror-gutter-filler\");\n d.gutterFiller.setAttribute(\"cm-not-content\", \"true\");\n // Will contain the actual code, positioned to cover the viewport.\n d.lineDiv = elt(\"div\", null, \"CodeMirror-code\");\n // Elements are added to these to represent selection and cursors.\n d.selectionDiv = elt(\"div\", null, null, \"position: relative; z-index: 1\");\n d.cursorDiv = elt(\"div\", null, \"CodeMirror-cursors\");\n // A visibility: hidden element used to find the size of things.\n d.measure = elt(\"div\", null, \"CodeMirror-measure\");\n // When lines outside of the viewport are measured, they are drawn in this.\n d.lineMeasure = elt(\"div\", null, \"CodeMirror-measure\");\n // Wraps everything that needs to exist inside the vertically-padded coordinate system\n d.lineSpace = elt(\"div\", [d.measure, d.lineMeasure, d.selectionDiv, d.cursorDiv, d.lineDiv],\n null, \"position: relative; outline: none\");\n // Moved around its parent to cover visible view.\n d.mover = elt(\"div\", [elt(\"div\", [d.lineSpace], \"CodeMirror-lines\")], null, \"position: relative\");\n // Set to the height of the document, allowing scrolling.\n d.sizer = elt(\"div\", [d.mover], \"CodeMirror-sizer\");\n d.sizerWidth = null;\n // Behavior of elts with overflow: auto and padding is\n // inconsistent across browsers. This is used to ensure the\n // scrollable area is big enough.\n d.heightForcer = elt(\"div\", null, null, \"position: absolute; height: \" + scrollerGap + \"px; width: 1px;\");\n // Will contain the gutters, if any.\n d.gutters = elt(\"div\", null, \"CodeMirror-gutters\");\n d.lineGutter = null;\n // Actual scrollable element.\n d.scroller = elt(\"div\", [d.sizer, d.heightForcer, d.gutters], \"CodeMirror-scroll\");\n d.scroller.setAttribute(\"tabIndex\", \"-1\");\n // The element in which the editor lives.\n d.wrapper = elt(\"div\", [d.scrollbarFiller, d.gutterFiller, d.scroller], \"CodeMirror\");\n\n // Work around IE7 z-index bug (not perfect, hence IE7 not really being supported)\n if (ie && ie_version < 8) { d.gutters.style.zIndex = -1; d.scroller.style.paddingRight = 0; }\n if (!webkit && !(gecko && mobile)) d.scroller.draggable = true;\n\n if (place) {\n if (place.appendChild) place.appendChild(d.wrapper);\n else place(d.wrapper);\n }\n\n // Current rendered range (may be bigger than the view window).\n d.viewFrom = d.viewTo = doc.first;\n d.reportedViewFrom = d.reportedViewTo = doc.first;\n // Information about the rendered lines.\n d.view = [];\n d.renderedView = null;\n // Holds info about a single rendered line when it was rendered\n // for measurement, while not in view.\n d.externalMeasured = null;\n // Empty space (in pixels) above the view\n d.viewOffset = 0;\n d.lastWrapHeight = d.lastWrapWidth = 0;\n d.updateLineNumbers = null;\n\n d.nativeBarWidth = d.barHeight = d.barWidth = 0;\n d.scrollbarsClipped = false;\n\n // Used to only resize the line number gutter when necessary (when\n // the amount of lines crosses a boundary that makes its width change)\n d.lineNumWidth = d.lineNumInnerWidth = d.lineNumChars = null;\n // Set to true when a non-horizontal-scrolling line widget is\n // added. As an optimization, line widget aligning is skipped when\n // this is false.\n d.alignWidgets = false;\n\n d.cachedCharWidth = d.cachedTextHeight = d.cachedPaddingH = null;\n\n // Tracks the maximum line length so that the horizontal scrollbar\n // can be kept static when scrolling.\n d.maxLine = null;\n d.maxLineLength = 0;\n d.maxLineChanged = false;\n\n // Used for measuring wheel scrolling granularity\n d.wheelDX = d.wheelDY = d.wheelStartX = d.wheelStartY = null;\n\n // True when shift is held down.\n d.shift = false;\n\n // Used to track whether anything happened since the context menu\n // was opened.\n d.selForContextMenu = null;\n\n d.activeTouch = null;\n\n input.init(d);\n }\n\n // STATE UPDATES\n\n // Used to get the editor into a consistent state again when options change.\n\n function loadMode(cm) {\n cm.doc.mode = CodeMirror.getMode(cm.options, cm.doc.modeOption);\n resetModeState(cm);\n }\n\n function resetModeState(cm) {\n cm.doc.iter(function(line) {\n if (line.stateAfter) line.stateAfter = null;\n if (line.styles) line.styles = null;\n });\n cm.doc.frontier = cm.doc.first;\n startWorker(cm, 100);\n cm.state.modeGen++;\n if (cm.curOp) regChange(cm);\n }\n\n function wrappingChanged(cm) {\n if (cm.options.lineWrapping) {\n addClass(cm.display.wrapper, \"CodeMirror-wrap\");\n cm.display.sizer.style.minWidth = \"\";\n cm.display.sizerWidth = null;\n } else {\n rmClass(cm.display.wrapper, \"CodeMirror-wrap\");\n findMaxLine(cm);\n }\n estimateLineHeights(cm);\n regChange(cm);\n clearCaches(cm);\n setTimeout(function(){updateScrollbars(cm);}, 100);\n }\n\n // Returns a function that estimates the height of a line, to use as\n // first approximation until the line becomes visible (and is thus\n // properly measurable).\n function estimateHeight(cm) {\n var th = textHeight(cm.display), wrapping = cm.options.lineWrapping;\n var perLine = wrapping && Math.max(5, cm.display.scroller.clientWidth / charWidth(cm.display) - 3);\n return function(line) {\n if (lineIsHidden(cm.doc, line)) return 0;\n\n var widgetsHeight = 0;\n if (line.widgets) for (var i = 0; i < line.widgets.length; i++) {\n if (line.widgets[i].height) widgetsHeight += line.widgets[i].height;\n }\n\n if (wrapping)\n return widgetsHeight + (Math.ceil(line.text.length / perLine) || 1) * th;\n else\n return widgetsHeight + th;\n };\n }\n\n function estimateLineHeights(cm) {\n var doc = cm.doc, est = estimateHeight(cm);\n doc.iter(function(line) {\n var estHeight = est(line);\n if (estHeight != line.height) updateLineHeight(line, estHeight);\n });\n }\n\n function themeChanged(cm) {\n cm.display.wrapper.className = cm.display.wrapper.className.replace(/\\s*cm-s-\\S+/g, \"\") +\n cm.options.theme.replace(/(^|\\s)\\s*/g, \" cm-s-\");\n clearCaches(cm);\n }\n\n function guttersChanged(cm) {\n updateGutters(cm);\n regChange(cm);\n setTimeout(function(){alignHorizontally(cm);}, 20);\n }\n\n // Rebuild the gutter elements, ensure the margin to the left of the\n // code matches their width.\n function updateGutters(cm) {\n var gutters = cm.display.gutters, specs = cm.options.gutters;\n removeChildren(gutters);\n for (var i = 0; i < specs.length; ++i) {\n var gutterClass = specs[i];\n var gElt = gutters.appendChild(elt(\"div\", null, \"CodeMirror-gutter \" + gutterClass));\n if (gutterClass == \"CodeMirror-linenumbers\") {\n cm.display.lineGutter = gElt;\n gElt.style.width = (cm.display.lineNumWidth || 1) + \"px\";\n }\n }\n gutters.style.display = i ? \"\" : \"none\";\n updateGutterSpace(cm);\n }\n\n function updateGutterSpace(cm) {\n var width = cm.display.gutters.offsetWidth;\n cm.display.sizer.style.marginLeft = width + \"px\";\n }\n\n // Compute the character length of a line, taking into account\n // collapsed ranges (see markText) that might hide parts, and join\n // other lines onto it.\n function lineLength(line) {\n if (line.height == 0) return 0;\n var len = line.text.length, merged, cur = line;\n while (merged = collapsedSpanAtStart(cur)) {\n var found = merged.find(0, true);\n cur = found.from.line;\n len += found.from.ch - found.to.ch;\n }\n cur = line;\n while (merged = collapsedSpanAtEnd(cur)) {\n var found = merged.find(0, true);\n len -= cur.text.length - found.from.ch;\n cur = found.to.line;\n len += cur.text.length - found.to.ch;\n }\n return len;\n }\n\n // Find the longest line in the document.\n function findMaxLine(cm) {\n var d = cm.display, doc = cm.doc;\n d.maxLine = getLine(doc, doc.first);\n d.maxLineLength = lineLength(d.maxLine);\n d.maxLineChanged = true;\n doc.iter(function(line) {\n var len = lineLength(line);\n if (len > d.maxLineLength) {\n d.maxLineLength = len;\n d.maxLine = line;\n }\n });\n }\n\n // Make sure the gutters options contains the element\n // \"CodeMirror-linenumbers\" when the lineNumbers option is true.\n function setGuttersForLineNumbers(options) {\n var found = indexOf(options.gutters, \"CodeMirror-linenumbers\");\n if (found == -1 && options.lineNumbers) {\n options.gutters = options.gutters.concat([\"CodeMirror-linenumbers\"]);\n } else if (found > -1 && !options.lineNumbers) {\n options.gutters = options.gutters.slice(0);\n options.gutters.splice(found, 1);\n }\n }\n\n // SCROLLBARS\n\n // Prepare DOM reads needed to update the scrollbars. Done in one\n // shot to minimize update/measure roundtrips.\n function measureForScrollbars(cm) {\n var d = cm.display, gutterW = d.gutters.offsetWidth;\n var docH = Math.round(cm.doc.height + paddingVert(cm.display));\n return {\n clientHeight: d.scroller.clientHeight,\n viewHeight: d.wrapper.clientHeight,\n scrollWidth: d.scroller.scrollWidth, clientWidth: d.scroller.clientWidth,\n viewWidth: d.wrapper.clientWidth,\n barLeft: cm.options.fixedGutter ? gutterW : 0,\n docHeight: docH,\n scrollHeight: docH + scrollGap(cm) + d.barHeight,\n nativeBarWidth: d.nativeBarWidth,\n gutterWidth: gutterW\n };\n }\n\n function NativeScrollbars(place, scroll, cm) {\n this.cm = cm;\n var vert = this.vert = elt(\"div\", [elt(\"div\", null, null, \"min-width: 1px\")], \"CodeMirror-vscrollbar\");\n var horiz = this.horiz = elt(\"div\", [elt(\"div\", null, null, \"height: 100%; min-height: 1px\")], \"CodeMirror-hscrollbar\");\n place(vert); place(horiz);\n\n on(vert, \"scroll\", function() {\n if (vert.clientHeight) scroll(vert.scrollTop, \"vertical\");\n });\n on(horiz, \"scroll\", function() {\n if (horiz.clientWidth) scroll(horiz.scrollLeft, \"horizontal\");\n });\n\n this.checkedZeroWidth = false;\n // Need to set a minimum width to see the scrollbar on IE7 (but must not set it on IE8).\n if (ie && ie_version < 8) this.horiz.style.minHeight = this.vert.style.minWidth = \"18px\";\n }\n\n NativeScrollbars.prototype = copyObj({\n update: function(measure) {\n var needsH = measure.scrollWidth > measure.clientWidth + 1;\n var needsV = measure.scrollHeight > measure.clientHeight + 1;\n var sWidth = measure.nativeBarWidth;\n\n if (needsV) {\n this.vert.style.display = \"block\";\n this.vert.style.bottom = needsH ? sWidth + \"px\" : \"0\";\n var totalHeight = measure.viewHeight - (needsH ? sWidth : 0);\n // A bug in IE8 can cause this value to be negative, so guard it.\n this.vert.firstChild.style.height =\n Math.max(0, measure.scrollHeight - measure.clientHeight + totalHeight) + \"px\";\n } else {\n this.vert.style.display = \"\";\n this.vert.firstChild.style.height = \"0\";\n }\n\n if (needsH) {\n this.horiz.style.display = \"block\";\n this.horiz.style.right = needsV ? sWidth + \"px\" : \"0\";\n this.horiz.style.left = measure.barLeft + \"px\";\n var totalWidth = measure.viewWidth - measure.barLeft - (needsV ? sWidth : 0);\n this.horiz.firstChild.style.width =\n (measure.scrollWidth - measure.clientWidth + totalWidth) + \"px\";\n } else {\n this.horiz.style.display = \"\";\n this.horiz.firstChild.style.width = \"0\";\n }\n\n if (!this.checkedZeroWidth && measure.clientHeight > 0) {\n if (sWidth == 0) this.zeroWidthHack();\n this.checkedZeroWidth = true;\n }\n\n return {right: needsV ? sWidth : 0, bottom: needsH ? sWidth : 0};\n },\n setScrollLeft: function(pos) {\n if (this.horiz.scrollLeft != pos) this.horiz.scrollLeft = pos;\n if (this.disableHoriz) this.enableZeroWidthBar(this.horiz, this.disableHoriz);\n },\n setScrollTop: function(pos) {\n if (this.vert.scrollTop != pos) this.vert.scrollTop = pos;\n if (this.disableVert) this.enableZeroWidthBar(this.vert, this.disableVert);\n },\n zeroWidthHack: function() {\n var w = mac && !mac_geMountainLion ? \"12px\" : \"18px\";\n this.horiz.style.height = this.vert.style.width = w;\n this.horiz.style.pointerEvents = this.vert.style.pointerEvents = \"none\";\n this.disableHoriz = new Delayed;\n this.disableVert = new Delayed;\n },\n enableZeroWidthBar: function(bar, delay) {\n bar.style.pointerEvents = \"auto\";\n function maybeDisable() {\n // To find out whether the scrollbar is still visible, we\n // check whether the element under the pixel in the bottom\n // left corner of the scrollbar box is the scrollbar box\n // itself (when the bar is still visible) or its filler child\n // (when the bar is hidden). If it is still visible, we keep\n // it enabled, if it's hidden, we disable pointer events.\n var box = bar.getBoundingClientRect();\n var elt = document.elementFromPoint(box.left + 1, box.bottom - 1);\n if (elt != bar) bar.style.pointerEvents = \"none\";\n else delay.set(1000, maybeDisable);\n }\n delay.set(1000, maybeDisable);\n },\n clear: function() {\n var parent = this.horiz.parentNode;\n parent.removeChild(this.horiz);\n parent.removeChild(this.vert);\n }\n }, NativeScrollbars.prototype);\n\n function NullScrollbars() {}\n\n NullScrollbars.prototype = copyObj({\n update: function() { return {bottom: 0, right: 0}; },\n setScrollLeft: function() {},\n setScrollTop: function() {},\n clear: function() {}\n }, NullScrollbars.prototype);\n\n CodeMirror.scrollbarModel = {\"native\": NativeScrollbars, \"null\": NullScrollbars};\n\n function initScrollbars(cm) {\n if (cm.display.scrollbars) {\n cm.display.scrollbars.clear();\n if (cm.display.scrollbars.addClass)\n rmClass(cm.display.wrapper, cm.display.scrollbars.addClass);\n }\n\n cm.display.scrollbars = new CodeMirror.scrollbarModel[cm.options.scrollbarStyle](function(node) {\n cm.display.wrapper.insertBefore(node, cm.display.scrollbarFiller);\n // Prevent clicks in the scrollbars from killing focus\n on(node, \"mousedown\", function() {\n if (cm.state.focused) setTimeout(function() { cm.display.input.focus(); }, 0);\n });\n node.setAttribute(\"cm-not-content\", \"true\");\n }, function(pos, axis) {\n if (axis == \"horizontal\") setScrollLeft(cm, pos);\n else setScrollTop(cm, pos);\n }, cm);\n if (cm.display.scrollbars.addClass)\n addClass(cm.display.wrapper, cm.display.scrollbars.addClass);\n }\n\n function updateScrollbars(cm, measure) {\n if (!measure) measure = measureForScrollbars(cm);\n var startWidth = cm.display.barWidth, startHeight = cm.display.barHeight;\n updateScrollbarsInner(cm, measure);\n for (var i = 0; i < 4 && startWidth != cm.display.barWidth || startHeight != cm.display.barHeight; i++) {\n if (startWidth != cm.display.barWidth && cm.options.lineWrapping)\n updateHeightsInViewport(cm);\n updateScrollbarsInner(cm, measureForScrollbars(cm));\n startWidth = cm.display.barWidth; startHeight = cm.display.barHeight;\n }\n }\n\n // Re-synchronize the fake scrollbars with the actual size of the\n // content.\n function updateScrollbarsInner(cm, measure) {\n var d = cm.display;\n var sizes = d.scrollbars.update(measure);\n\n d.sizer.style.paddingRight = (d.barWidth = sizes.right) + \"px\";\n d.sizer.style.paddingBottom = (d.barHeight = sizes.bottom) + \"px\";\n d.heightForcer.style.borderBottom = sizes.bottom + \"px solid transparent\"\n\n if (sizes.right && sizes.bottom) {\n d.scrollbarFiller.style.display = \"block\";\n d.scrollbarFiller.style.height = sizes.bottom + \"px\";\n d.scrollbarFiller.style.width = sizes.right + \"px\";\n } else d.scrollbarFiller.style.display = \"\";\n if (sizes.bottom && cm.options.coverGutterNextToScrollbar && cm.options.fixedGutter) {\n d.gutterFiller.style.display = \"block\";\n d.gutterFiller.style.height = sizes.bottom + \"px\";\n d.gutterFiller.style.width = measure.gutterWidth + \"px\";\n } else d.gutterFiller.style.display = \"\";\n }\n\n // Compute the lines that are visible in a given viewport (defaults\n // the the current scroll position). viewport may contain top,\n // height, and ensure (see op.scrollToPos) properties.\n function visibleLines(display, doc, viewport) {\n var top = viewport && viewport.top != null ? Math.max(0, viewport.top) : display.scroller.scrollTop;\n top = Math.floor(top - paddingTop(display));\n var bottom = viewport && viewport.bottom != null ? viewport.bottom : top + display.wrapper.clientHeight;\n\n var from = lineAtHeight(doc, top), to = lineAtHeight(doc, bottom);\n // Ensure is a {from: {line, ch}, to: {line, ch}} object, and\n // forces those lines into the viewport (if possible).\n if (viewport && viewport.ensure) {\n var ensureFrom = viewport.ensure.from.line, ensureTo = viewport.ensure.to.line;\n if (ensureFrom < from) {\n from = ensureFrom;\n to = lineAtHeight(doc, heightAtLine(getLine(doc, ensureFrom)) + display.wrapper.clientHeight);\n } else if (Math.min(ensureTo, doc.lastLine()) >= to) {\n from = lineAtHeight(doc, heightAtLine(getLine(doc, ensureTo)) - display.wrapper.clientHeight);\n to = ensureTo;\n }\n }\n return {from: from, to: Math.max(to, from + 1)};\n }\n\n // LINE NUMBERS\n\n // Re-align line numbers and gutter marks to compensate for\n // horizontal scrolling.\n function alignHorizontally(cm) {\n var display = cm.display, view = display.view;\n if (!display.alignWidgets && (!display.gutters.firstChild || !cm.options.fixedGutter)) return;\n var comp = compensateForHScroll(display) - display.scroller.scrollLeft + cm.doc.scrollLeft;\n var gutterW = display.gutters.offsetWidth, left = comp + \"px\";\n for (var i = 0; i < view.length; i++) if (!view[i].hidden) {\n if (cm.options.fixedGutter && view[i].gutter)\n view[i].gutter.style.left = left;\n var align = view[i].alignable;\n if (align) for (var j = 0; j < align.length; j++)\n align[j].style.left = left;\n }\n if (cm.options.fixedGutter)\n display.gutters.style.left = (comp + gutterW) + \"px\";\n }\n\n // Used to ensure that the line number gutter is still the right\n // size for the current document size. Returns true when an update\n // is needed.\n function maybeUpdateLineNumberWidth(cm) {\n if (!cm.options.lineNumbers) return false;\n var doc = cm.doc, last = lineNumberFor(cm.options, doc.first + doc.size - 1), display = cm.display;\n if (last.length != display.lineNumChars) {\n var test = display.measure.appendChild(elt(\"div\", [elt(\"div\", last)],\n \"CodeMirror-linenumber CodeMirror-gutter-elt\"));\n var innerW = test.firstChild.offsetWidth, padding = test.offsetWidth - innerW;\n display.lineGutter.style.width = \"\";\n display.lineNumInnerWidth = Math.max(innerW, display.lineGutter.offsetWidth - padding) + 1;\n display.lineNumWidth = display.lineNumInnerWidth + padding;\n display.lineNumChars = display.lineNumInnerWidth ? last.length : -1;\n display.lineGutter.style.width = display.lineNumWidth + \"px\";\n updateGutterSpace(cm);\n return true;\n }\n return false;\n }\n\n function lineNumberFor(options, i) {\n return String(options.lineNumberFormatter(i + options.firstLineNumber));\n }\n\n // Computes display.scroller.scrollLeft + display.gutters.offsetWidth,\n // but using getBoundingClientRect to get a sub-pixel-accurate\n // result.\n function compensateForHScroll(display) {\n return display.scroller.getBoundingClientRect().left - display.sizer.getBoundingClientRect().left;\n }\n\n // DISPLAY DRAWING\n\n function DisplayUpdate(cm, viewport, force) {\n var display = cm.display;\n\n this.viewport = viewport;\n // Store some values that we'll need later (but don't want to force a relayout for)\n this.visible = visibleLines(display, cm.doc, viewport);\n this.editorIsHidden = !display.wrapper.offsetWidth;\n this.wrapperHeight = display.wrapper.clientHeight;\n this.wrapperWidth = display.wrapper.clientWidth;\n this.oldDisplayWidth = displayWidth(cm);\n this.force = force;\n this.dims = getDimensions(cm);\n this.events = [];\n }\n\n DisplayUpdate.prototype.signal = function(emitter, type) {\n if (hasHandler(emitter, type))\n this.events.push(arguments);\n };\n DisplayUpdate.prototype.finish = function() {\n for (var i = 0; i < this.events.length; i++)\n signal.apply(null, this.events[i]);\n };\n\n function maybeClipScrollbars(cm) {\n var display = cm.display;\n if (!display.scrollbarsClipped && display.scroller.offsetWidth) {\n display.nativeBarWidth = display.scroller.offsetWidth - display.scroller.clientWidth;\n display.heightForcer.style.height = scrollGap(cm) + \"px\";\n display.sizer.style.marginBottom = -display.nativeBarWidth + \"px\";\n display.sizer.style.borderRightWidth = scrollGap(cm) + \"px\";\n display.scrollbarsClipped = true;\n }\n }\n\n // Does the actual updating of the line display. Bails out\n // (returning false) when there is nothing to be done and forced is\n // false.\n function updateDisplayIfNeeded(cm, update) {\n var display = cm.display, doc = cm.doc;\n\n if (update.editorIsHidden) {\n resetView(cm);\n return false;\n }\n\n // Bail out if the visible area is already rendered and nothing changed.\n if (!update.force &&\n update.visible.from >= display.viewFrom && update.visible.to <= display.viewTo &&\n (display.updateLineNumbers == null || display.updateLineNumbers >= display.viewTo) &&\n display.renderedView == display.view && countDirtyView(cm) == 0)\n return false;\n\n if (maybeUpdateLineNumberWidth(cm)) {\n resetView(cm);\n update.dims = getDimensions(cm);\n }\n\n // Compute a suitable new viewport (from & to)\n var end = doc.first + doc.size;\n var from = Math.max(update.visible.from - cm.options.viewportMargin, doc.first);\n var to = Math.min(end, update.visible.to + cm.options.viewportMargin);\n if (display.viewFrom < from && from - display.viewFrom < 20) from = Math.max(doc.first, display.viewFrom);\n if (display.viewTo > to && display.viewTo - to < 20) to = Math.min(end, display.viewTo);\n if (sawCollapsedSpans) {\n from = visualLineNo(cm.doc, from);\n to = visualLineEndNo(cm.doc, to);\n }\n\n var different = from != display.viewFrom || to != display.viewTo ||\n display.lastWrapHeight != update.wrapperHeight || display.lastWrapWidth != update.wrapperWidth;\n adjustView(cm, from, to);\n\n display.viewOffset = heightAtLine(getLine(cm.doc, display.viewFrom));\n // Position the mover div to align with the current scroll position\n cm.display.mover.style.top = display.viewOffset + \"px\";\n\n var toUpdate = countDirtyView(cm);\n if (!different && toUpdate == 0 && !update.force && display.renderedView == display.view &&\n (display.updateLineNumbers == null || display.updateLineNumbers >= display.viewTo))\n return false;\n\n // For big changes, we hide the enclosing element during the\n // update, since that speeds up the operations on most browsers.\n var focused = activeElt();\n if (toUpdate > 4) display.lineDiv.style.display = \"none\";\n patchDisplay(cm, display.updateLineNumbers, update.dims);\n if (toUpdate > 4) display.lineDiv.style.display = \"\";\n display.renderedView = display.view;\n // There might have been a widget with a focused element that got\n // hidden or updated, if so re-focus it.\n if (focused && activeElt() != focused && focused.offsetHeight) focused.focus();\n\n // Prevent selection and cursors from interfering with the scroll\n // width and height.\n removeChildren(display.cursorDiv);\n removeChildren(display.selectionDiv);\n display.gutters.style.height = display.sizer.style.minHeight = 0;\n\n if (different) {\n display.lastWrapHeight = update.wrapperHeight;\n display.lastWrapWidth = update.wrapperWidth;\n startWorker(cm, 400);\n }\n\n display.updateLineNumbers = null;\n\n return true;\n }\n\n function postUpdateDisplay(cm, update) {\n var viewport = update.viewport;\n\n for (var first = true;; first = false) {\n if (!first || !cm.options.lineWrapping || update.oldDisplayWidth == displayWidth(cm)) {\n // Clip forced viewport to actual scrollable area.\n if (viewport && viewport.top != null)\n viewport = {top: Math.min(cm.doc.height + paddingVert(cm.display) - displayHeight(cm), viewport.top)};\n // Updated line heights might result in the drawn area not\n // actually covering the viewport. Keep looping until it does.\n update.visible = visibleLines(cm.display, cm.doc, viewport);\n if (update.visible.from >= cm.display.viewFrom && update.visible.to <= cm.display.viewTo)\n break;\n }\n if (!updateDisplayIfNeeded(cm, update)) break;\n updateHeightsInViewport(cm);\n var barMeasure = measureForScrollbars(cm);\n updateSelection(cm);\n updateScrollbars(cm, barMeasure);\n setDocumentHeight(cm, barMeasure);\n }\n\n update.signal(cm, \"update\", cm);\n if (cm.display.viewFrom != cm.display.reportedViewFrom || cm.display.viewTo != cm.display.reportedViewTo) {\n update.signal(cm, \"viewportChange\", cm, cm.display.viewFrom, cm.display.viewTo);\n cm.display.reportedViewFrom = cm.display.viewFrom; cm.display.reportedViewTo = cm.display.viewTo;\n }\n }\n\n function updateDisplaySimple(cm, viewport) {\n var update = new DisplayUpdate(cm, viewport);\n if (updateDisplayIfNeeded(cm, update)) {\n updateHeightsInViewport(cm);\n postUpdateDisplay(cm, update);\n var barMeasure = measureForScrollbars(cm);\n updateSelection(cm);\n updateScrollbars(cm, barMeasure);\n setDocumentHeight(cm, barMeasure);\n update.finish();\n }\n }\n\n function setDocumentHeight(cm, measure) {\n cm.display.sizer.style.minHeight = measure.docHeight + \"px\";\n cm.display.heightForcer.style.top = measure.docHeight + \"px\";\n cm.display.gutters.style.height = (measure.docHeight + cm.display.barHeight + scrollGap(cm)) + \"px\";\n }\n\n // Read the actual heights of the rendered lines, and update their\n // stored heights to match.\n function updateHeightsInViewport(cm) {\n var display = cm.display;\n var prevBottom = display.lineDiv.offsetTop;\n for (var i = 0; i < display.view.length; i++) {\n var cur = display.view[i], height;\n if (cur.hidden) continue;\n if (ie && ie_version < 8) {\n var bot = cur.node.offsetTop + cur.node.offsetHeight;\n height = bot - prevBottom;\n prevBottom = bot;\n } else {\n var box = cur.node.getBoundingClientRect();\n height = box.bottom - box.top;\n }\n var diff = cur.line.height - height;\n if (height < 2) height = textHeight(display);\n if (diff > .001 || diff < -.001) {\n updateLineHeight(cur.line, height);\n updateWidgetHeight(cur.line);\n if (cur.rest) for (var j = 0; j < cur.rest.length; j++)\n updateWidgetHeight(cur.rest[j]);\n }\n }\n }\n\n // Read and store the height of line widgets associated with the\n // given line.\n function updateWidgetHeight(line) {\n if (line.widgets) for (var i = 0; i < line.widgets.length; ++i)\n line.widgets[i].height = line.widgets[i].node.parentNode.offsetHeight;\n }\n\n // Do a bulk-read of the DOM positions and sizes needed to draw the\n // view, so that we don't interleave reading and writing to the DOM.\n function getDimensions(cm) {\n var d = cm.display, left = {}, width = {};\n var gutterLeft = d.gutters.clientLeft;\n for (var n = d.gutters.firstChild, i = 0; n; n = n.nextSibling, ++i) {\n left[cm.options.gutters[i]] = n.offsetLeft + n.clientLeft + gutterLeft;\n width[cm.options.gutters[i]] = n.clientWidth;\n }\n return {fixedPos: compensateForHScroll(d),\n gutterTotalWidth: d.gutters.offsetWidth,\n gutterLeft: left,\n gutterWidth: width,\n wrapperWidth: d.wrapper.clientWidth};\n }\n\n // Sync the actual display DOM structure with display.view, removing\n // nodes for lines that are no longer in view, and creating the ones\n // that are not there yet, and updating the ones that are out of\n // date.\n function patchDisplay(cm, updateNumbersFrom, dims) {\n var display = cm.display, lineNumbers = cm.options.lineNumbers;\n var container = display.lineDiv, cur = container.firstChild;\n\n function rm(node) {\n var next = node.nextSibling;\n // Works around a throw-scroll bug in OS X Webkit\n if (webkit && mac && cm.display.currentWheelTarget == node)\n node.style.display = \"none\";\n else\n node.parentNode.removeChild(node);\n return next;\n }\n\n var view = display.view, lineN = display.viewFrom;\n // Loop over the elements in the view, syncing cur (the DOM nodes\n // in display.lineDiv) with the view as we go.\n for (var i = 0; i < view.length; i++) {\n var lineView = view[i];\n if (lineView.hidden) {\n } else if (!lineView.node || lineView.node.parentNode != container) { // Not drawn yet\n var node = buildLineElement(cm, lineView, lineN, dims);\n container.insertBefore(node, cur);\n } else { // Already drawn\n while (cur != lineView.node) cur = rm(cur);\n var updateNumber = lineNumbers && updateNumbersFrom != null &&\n updateNumbersFrom <= lineN && lineView.lineNumber;\n if (lineView.changes) {\n if (indexOf(lineView.changes, \"gutter\") > -1) updateNumber = false;\n updateLineForChanges(cm, lineView, lineN, dims);\n }\n if (updateNumber) {\n removeChildren(lineView.lineNumber);\n lineView.lineNumber.appendChild(document.createTextNode(lineNumberFor(cm.options, lineN)));\n }\n cur = lineView.node.nextSibling;\n }\n lineN += lineView.size;\n }\n while (cur) cur = rm(cur);\n }\n\n // When an aspect of a line changes, a string is added to\n // lineView.changes. This updates the relevant part of the line's\n // DOM structure.\n function updateLineForChanges(cm, lineView, lineN, dims) {\n for (var j = 0; j < lineView.changes.length; j++) {\n var type = lineView.changes[j];\n if (type == \"text\") updateLineText(cm, lineView);\n else if (type == \"gutter\") updateLineGutter(cm, lineView, lineN, dims);\n else if (type == \"class\") updateLineClasses(lineView);\n else if (type == \"widget\") updateLineWidgets(cm, lineView, dims);\n }\n lineView.changes = null;\n }\n\n // Lines with gutter elements, widgets or a background class need to\n // be wrapped, and have the extra elements added to the wrapper div\n function ensureLineWrapped(lineView) {\n if (lineView.node == lineView.text) {\n lineView.node = elt(\"div\", null, null, \"position: relative\");\n if (lineView.text.parentNode)\n lineView.text.parentNode.replaceChild(lineView.node, lineView.text);\n lineView.node.appendChild(lineView.text);\n if (ie && ie_version < 8) lineView.node.style.zIndex = 2;\n }\n return lineView.node;\n }\n\n function updateLineBackground(lineView) {\n var cls = lineView.bgClass ? lineView.bgClass + \" \" + (lineView.line.bgClass || \"\") : lineView.line.bgClass;\n if (cls) cls += \" CodeMirror-linebackground\";\n if (lineView.background) {\n if (cls) lineView.background.className = cls;\n else { lineView.background.parentNode.removeChild(lineView.background); lineView.background = null; }\n } else if (cls) {\n var wrap = ensureLineWrapped(lineView);\n lineView.background = wrap.insertBefore(elt(\"div\", null, cls), wrap.firstChild);\n }\n }\n\n // Wrapper around buildLineContent which will reuse the structure\n // in display.externalMeasured when possible.\n function getLineContent(cm, lineView) {\n var ext = cm.display.externalMeasured;\n if (ext && ext.line == lineView.line) {\n cm.display.externalMeasured = null;\n lineView.measure = ext.measure;\n return ext.built;\n }\n return buildLineContent(cm, lineView);\n }\n\n // Redraw the line's text. Interacts with the background and text\n // classes because the mode may output tokens that influence these\n // classes.\n function updateLineText(cm, lineView) {\n var cls = lineView.text.className;\n var built = getLineContent(cm, lineView);\n if (lineView.text == lineView.node) lineView.node = built.pre;\n lineView.text.parentNode.replaceChild(built.pre, lineView.text);\n lineView.text = built.pre;\n if (built.bgClass != lineView.bgClass || built.textClass != lineView.textClass) {\n lineView.bgClass = built.bgClass;\n lineView.textClass = built.textClass;\n updateLineClasses(lineView);\n } else if (cls) {\n lineView.text.className = cls;\n }\n }\n\n function updateLineClasses(lineView) {\n updateLineBackground(lineView);\n if (lineView.line.wrapClass)\n ensureLineWrapped(lineView).className = lineView.line.wrapClass;\n else if (lineView.node != lineView.text)\n lineView.node.className = \"\";\n var textClass = lineView.textClass ? lineView.textClass + \" \" + (lineView.line.textClass || \"\") : lineView.line.textClass;\n lineView.text.className = textClass || \"\";\n }\n\n function updateLineGutter(cm, lineView, lineN, dims) {\n if (lineView.gutter) {\n lineView.node.removeChild(lineView.gutter);\n lineView.gutter = null;\n }\n if (lineView.gutterBackground) {\n lineView.node.removeChild(lineView.gutterBackground);\n lineView.gutterBackground = null;\n }\n if (lineView.line.gutterClass) {\n var wrap = ensureLineWrapped(lineView);\n lineView.gutterBackground = elt(\"div\", null, \"CodeMirror-gutter-background \" + lineView.line.gutterClass,\n \"left: \" + (cm.options.fixedGutter ? dims.fixedPos : -dims.gutterTotalWidth) +\n \"px; width: \" + dims.gutterTotalWidth + \"px\");\n wrap.insertBefore(lineView.gutterBackground, lineView.text);\n }\n var markers = lineView.line.gutterMarkers;\n if (cm.options.lineNumbers || markers) {\n var wrap = ensureLineWrapped(lineView);\n var gutterWrap = lineView.gutter = elt(\"div\", null, \"CodeMirror-gutter-wrapper\", \"left: \" +\n (cm.options.fixedGutter ? dims.fixedPos : -dims.gutterTotalWidth) + \"px\");\n cm.display.input.setUneditable(gutterWrap);\n wrap.insertBefore(gutterWrap, lineView.text);\n if (lineView.line.gutterClass)\n gutterWrap.className += \" \" + lineView.line.gutterClass;\n if (cm.options.lineNumbers && (!markers || !markers[\"CodeMirror-linenumbers\"]))\n lineView.lineNumber = gutterWrap.appendChild(\n elt(\"div\", lineNumberFor(cm.options, lineN),\n \"CodeMirror-linenumber CodeMirror-gutter-elt\",\n \"left: \" + dims.gutterLeft[\"CodeMirror-linenumbers\"] + \"px; width: \"\n + cm.display.lineNumInnerWidth + \"px\"));\n if (markers) for (var k = 0; k < cm.options.gutters.length; ++k) {\n var id = cm.options.gutters[k], found = markers.hasOwnProperty(id) && markers[id];\n if (found)\n gutterWrap.appendChild(elt(\"div\", [found], \"CodeMirror-gutter-elt\", \"left: \" +\n dims.gutterLeft[id] + \"px; width: \" + dims.gutterWidth[id] + \"px\"));\n }\n }\n }\n\n function updateLineWidgets(cm, lineView, dims) {\n if (lineView.alignable) lineView.alignable = null;\n for (var node = lineView.node.firstChild, next; node; node = next) {\n var next = node.nextSibling;\n if (node.className == \"CodeMirror-linewidget\")\n lineView.node.removeChild(node);\n }\n insertLineWidgets(cm, lineView, dims);\n }\n\n // Build a line's DOM representation from scratch\n function buildLineElement(cm, lineView, lineN, dims) {\n var built = getLineContent(cm, lineView);\n lineView.text = lineView.node = built.pre;\n if (built.bgClass) lineView.bgClass = built.bgClass;\n if (built.textClass) lineView.textClass = built.textClass;\n\n updateLineClasses(lineView);\n updateLineGutter(cm, lineView, lineN, dims);\n insertLineWidgets(cm, lineView, dims);\n return lineView.node;\n }\n\n // A lineView may contain multiple logical lines (when merged by\n // collapsed spans). The widgets for all of them need to be drawn.\n function insertLineWidgets(cm, lineView, dims) {\n insertLineWidgetsFor(cm, lineView.line, lineView, dims, true);\n if (lineView.rest) for (var i = 0; i < lineView.rest.length; i++)\n insertLineWidgetsFor(cm, lineView.rest[i], lineView, dims, false);\n }\n\n function insertLineWidgetsFor(cm, line, lineView, dims, allowAbove) {\n if (!line.widgets) return;\n var wrap = ensureLineWrapped(lineView);\n for (var i = 0, ws = line.widgets; i < ws.length; ++i) {\n var widget = ws[i], node = elt(\"div\", [widget.node], \"CodeMirror-linewidget\");\n if (!widget.handleMouseEvents) node.setAttribute(\"cm-ignore-events\", \"true\");\n positionLineWidget(widget, node, lineView, dims);\n cm.display.input.setUneditable(node);\n if (allowAbove && widget.above)\n wrap.insertBefore(node, lineView.gutter || lineView.text);\n else\n wrap.appendChild(node);\n signalLater(widget, \"redraw\");\n }\n }\n\n function positionLineWidget(widget, node, lineView, dims) {\n if (widget.noHScroll) {\n (lineView.alignable || (lineView.alignable = [])).push(node);\n var width = dims.wrapperWidth;\n node.style.left = dims.fixedPos + \"px\";\n if (!widget.coverGutter) {\n width -= dims.gutterTotalWidth;\n node.style.paddingLeft = dims.gutterTotalWidth + \"px\";\n }\n node.style.width = width + \"px\";\n }\n if (widget.coverGutter) {\n node.style.zIndex = 5;\n node.style.position = \"relative\";\n if (!widget.noHScroll) node.style.marginLeft = -dims.gutterTotalWidth + \"px\";\n }\n }\n\n // POSITION OBJECT\n\n // A Pos instance represents a position within the text.\n var Pos = CodeMirror.Pos = function(line, ch) {\n if (!(this instanceof Pos)) return new Pos(line, ch);\n this.line = line; this.ch = ch;\n };\n\n // Compare two positions, return 0 if they are the same, a negative\n // number when a is less, and a positive number otherwise.\n var cmp = CodeMirror.cmpPos = function(a, b) { return a.line - b.line || a.ch - b.ch; };\n\n function copyPos(x) {return Pos(x.line, x.ch);}\n function maxPos(a, b) { return cmp(a, b) < 0 ? b : a; }\n function minPos(a, b) { return cmp(a, b) < 0 ? a : b; }\n\n // INPUT HANDLING\n\n function ensureFocus(cm) {\n if (!cm.state.focused) { cm.display.input.focus(); onFocus(cm); }\n }\n\n // This will be set to an array of strings when copying, so that,\n // when pasting, we know what kind of selections the copied text\n // was made out of.\n var lastCopied = null;\n\n function applyTextInput(cm, inserted, deleted, sel, origin) {\n var doc = cm.doc;\n cm.display.shift = false;\n if (!sel) sel = doc.sel;\n\n var paste = cm.state.pasteIncoming || origin == \"paste\";\n var textLines = doc.splitLines(inserted), multiPaste = null;\n // When pasing N lines into N selections, insert one line per selection\n if (paste && sel.ranges.length > 1) {\n if (lastCopied && lastCopied.join(\"\\n\") == inserted) {\n if (sel.ranges.length % lastCopied.length == 0) {\n multiPaste = [];\n for (var i = 0; i < lastCopied.length; i++)\n multiPaste.push(doc.splitLines(lastCopied[i]));\n }\n } else if (textLines.length == sel.ranges.length) {\n multiPaste = map(textLines, function(l) { return [l]; });\n }\n }\n\n // Normal behavior is to insert the new text into every selection\n for (var i = sel.ranges.length - 1; i >= 0; i--) {\n var range = sel.ranges[i];\n var from = range.from(), to = range.to();\n if (range.empty()) {\n if (deleted && deleted > 0) // Handle deletion\n from = Pos(from.line, from.ch - deleted);\n else if (cm.state.overwrite && !paste) // Handle overwrite\n to = Pos(to.line, Math.min(getLine(doc, to.line).text.length, to.ch + lst(textLines).length));\n }\n var updateInput = cm.curOp.updateInput;\n var changeEvent = {from: from, to: to, text: multiPaste ? multiPaste[i % multiPaste.length] : textLines,\n origin: origin || (paste ? \"paste\" : cm.state.cutIncoming ? \"cut\" : \"+input\")};\n makeChange(cm.doc, changeEvent);\n signalLater(cm, \"inputRead\", cm, changeEvent);\n }\n if (inserted && !paste)\n triggerElectric(cm, inserted);\n\n ensureCursorVisible(cm);\n cm.curOp.updateInput = updateInput;\n cm.curOp.typing = true;\n cm.state.pasteIncoming = cm.state.cutIncoming = false;\n }\n\n function handlePaste(e, cm) {\n var pasted = e.clipboardData && e.clipboardData.getData(\"text/plain\");\n if (pasted) {\n e.preventDefault();\n if (!cm.isReadOnly() && !cm.options.disableInput)\n runInOp(cm, function() { applyTextInput(cm, pasted, 0, null, \"paste\"); });\n return true;\n }\n }\n\n function triggerElectric(cm, inserted) {\n // When an 'electric' character is inserted, immediately trigger a reindent\n if (!cm.options.electricChars || !cm.options.smartIndent) return;\n var sel = cm.doc.sel;\n\n for (var i = sel.ranges.length - 1; i >= 0; i--) {\n var range = sel.ranges[i];\n if (range.head.ch > 100 || (i && sel.ranges[i - 1].head.line == range.head.line)) continue;\n var mode = cm.getModeAt(range.head);\n var indented = false;\n if (mode.electricChars) {\n for (var j = 0; j < mode.electricChars.length; j++)\n if (inserted.indexOf(mode.electricChars.charAt(j)) > -1) {\n indented = indentLine(cm, range.head.line, \"smart\");\n break;\n }\n } else if (mode.electricInput) {\n if (mode.electricInput.test(getLine(cm.doc, range.head.line).text.slice(0, range.head.ch)))\n indented = indentLine(cm, range.head.line, \"smart\");\n }\n if (indented) signalLater(cm, \"electricInput\", cm, range.head.line);\n }\n }\n\n function copyableRanges(cm) {\n var text = [], ranges = [];\n for (var i = 0; i < cm.doc.sel.ranges.length; i++) {\n var line = cm.doc.sel.ranges[i].head.line;\n var lineRange = {anchor: Pos(line, 0), head: Pos(line + 1, 0)};\n ranges.push(lineRange);\n text.push(cm.getRange(lineRange.anchor, lineRange.head));\n }\n return {text: text, ranges: ranges};\n }\n\n function disableBrowserMagic(field) {\n field.setAttribute(\"autocorrect\", \"off\");\n field.setAttribute(\"autocapitalize\", \"off\");\n field.setAttribute(\"spellcheck\", \"false\");\n }\n\n // TEXTAREA INPUT STYLE\n\n function TextareaInput(cm) {\n this.cm = cm;\n // See input.poll and input.reset\n this.prevInput = \"\";\n\n // Flag that indicates whether we expect input to appear real soon\n // now (after some event like 'keypress' or 'input') and are\n // polling intensively.\n this.pollingFast = false;\n // Self-resetting timeout for the poller\n this.polling = new Delayed();\n // Tracks when input.reset has punted to just putting a short\n // string into the textarea instead of the full selection.\n this.inaccurateSelection = false;\n // Used to work around IE issue with selection being forgotten when focus moves away from textarea\n this.hasSelection = false;\n this.composing = null;\n };\n\n function hiddenTextarea() {\n var te = elt(\"textarea\", null, null, \"position: absolute; padding: 0; width: 1px; height: 1em; outline: none\");\n var div = elt(\"div\", [te], null, \"overflow: hidden; position: relative; width: 3px; height: 0px;\");\n // The textarea is kept positioned near the cursor to prevent the\n // fact that it'll be scrolled into view on input from scrolling\n // our fake cursor out of view. On webkit, when wrap=off, paste is\n // very slow. So make the area wide instead.\n if (webkit) te.style.width = \"1000px\";\n else te.setAttribute(\"wrap\", \"off\");\n // If border: 0; -- iOS fails to open keyboard (issue #1287)\n if (ios) te.style.border = \"1px solid black\";\n disableBrowserMagic(te);\n return div;\n }\n\n TextareaInput.prototype = copyObj({\n init: function(display) {\n var input = this, cm = this.cm;\n\n // Wraps and hides input textarea\n var div = this.wrapper = hiddenTextarea();\n // The semihidden textarea that is focused when the editor is\n // focused, and receives input.\n var te = this.textarea = div.firstChild;\n display.wrapper.insertBefore(div, display.wrapper.firstChild);\n\n // Needed to hide big blue blinking cursor on Mobile Safari (doesn't seem to work in iOS 8 anymore)\n if (ios) te.style.width = \"0px\";\n\n on(te, \"input\", function() {\n if (ie && ie_version >= 9 && input.hasSelection) input.hasSelection = null;\n input.poll();\n });\n\n on(te, \"paste\", function(e) {\n if (signalDOMEvent(cm, e) || handlePaste(e, cm)) return\n\n cm.state.pasteIncoming = true;\n input.fastPoll();\n });\n\n function prepareCopyCut(e) {\n if (signalDOMEvent(cm, e)) return\n if (cm.somethingSelected()) {\n lastCopied = cm.getSelections();\n if (input.inaccurateSelection) {\n input.prevInput = \"\";\n input.inaccurateSelection = false;\n te.value = lastCopied.join(\"\\n\");\n selectInput(te);\n }\n } else if (!cm.options.lineWiseCopyCut) {\n return;\n } else {\n var ranges = copyableRanges(cm);\n lastCopied = ranges.text;\n if (e.type == \"cut\") {\n cm.setSelections(ranges.ranges, null, sel_dontScroll);\n } else {\n input.prevInput = \"\";\n te.value = ranges.text.join(\"\\n\");\n selectInput(te);\n }\n }\n if (e.type == \"cut\") cm.state.cutIncoming = true;\n }\n on(te, \"cut\", prepareCopyCut);\n on(te, \"copy\", prepareCopyCut);\n\n on(display.scroller, \"paste\", function(e) {\n if (eventInWidget(display, e) || signalDOMEvent(cm, e)) return;\n cm.state.pasteIncoming = true;\n input.focus();\n });\n\n // Prevent normal selection in the editor (we handle our own)\n on(display.lineSpace, \"selectstart\", function(e) {\n if (!eventInWidget(display, e)) e_preventDefault(e);\n });\n\n on(te, \"compositionstart\", function() {\n var start = cm.getCursor(\"from\");\n if (input.composing) input.composing.range.clear()\n input.composing = {\n start: start,\n range: cm.markText(start, cm.getCursor(\"to\"), {className: \"CodeMirror-composing\"})\n };\n });\n on(te, \"compositionend\", function() {\n if (input.composing) {\n input.poll();\n input.composing.range.clear();\n input.composing = null;\n }\n });\n },\n\n prepareSelection: function() {\n // Redraw the selection and/or cursor\n var cm = this.cm, display = cm.display, doc = cm.doc;\n var result = prepareSelection(cm);\n\n // Move the hidden textarea near the cursor to prevent scrolling artifacts\n if (cm.options.moveInputWithCursor) {\n var headPos = cursorCoords(cm, doc.sel.primary().head, \"div\");\n var wrapOff = display.wrapper.getBoundingClientRect(), lineOff = display.lineDiv.getBoundingClientRect();\n result.teTop = Math.max(0, Math.min(display.wrapper.clientHeight - 10,\n headPos.top + lineOff.top - wrapOff.top));\n result.teLeft = Math.max(0, Math.min(display.wrapper.clientWidth - 10,\n headPos.left + lineOff.left - wrapOff.left));\n }\n\n return result;\n },\n\n showSelection: function(drawn) {\n var cm = this.cm, display = cm.display;\n removeChildrenAndAdd(display.cursorDiv, drawn.cursors);\n removeChildrenAndAdd(display.selectionDiv, drawn.selection);\n if (drawn.teTop != null) {\n this.wrapper.style.top = drawn.teTop + \"px\";\n this.wrapper.style.left = drawn.teLeft + \"px\";\n }\n },\n\n // Reset the input to correspond to the selection (or to be empty,\n // when not typing and nothing is selected)\n reset: function(typing) {\n if (this.contextMenuPending) return;\n var minimal, selected, cm = this.cm, doc = cm.doc;\n if (cm.somethingSelected()) {\n this.prevInput = \"\";\n var range = doc.sel.primary();\n minimal = hasCopyEvent &&\n (range.to().line - range.from().line > 100 || (selected = cm.getSelection()).length > 1000);\n var content = minimal ? \"-\" : selected || cm.getSelection();\n this.textarea.value = content;\n if (cm.state.focused) selectInput(this.textarea);\n if (ie && ie_version >= 9) this.hasSelection = content;\n } else if (!typing) {\n this.prevInput = this.textarea.value = \"\";\n if (ie && ie_version >= 9) this.hasSelection = null;\n }\n this.inaccurateSelection = minimal;\n },\n\n getField: function() { return this.textarea; },\n\n supportsTouch: function() { return false; },\n\n focus: function() {\n if (this.cm.options.readOnly != \"nocursor\" && (!mobile || activeElt() != this.textarea)) {\n try { this.textarea.focus(); }\n catch (e) {} // IE8 will throw if the textarea is display: none or not in DOM\n }\n },\n\n blur: function() { this.textarea.blur(); },\n\n resetPosition: function() {\n this.wrapper.style.top = this.wrapper.style.left = 0;\n },\n\n receivedFocus: function() { this.slowPoll(); },\n\n // Poll for input changes, using the normal rate of polling. This\n // runs as long as the editor is focused.\n slowPoll: function() {\n var input = this;\n if (input.pollingFast) return;\n input.polling.set(this.cm.options.pollInterval, function() {\n input.poll();\n if (input.cm.state.focused) input.slowPoll();\n });\n },\n\n // When an event has just come in that is likely to add or change\n // something in the input textarea, we poll faster, to ensure that\n // the change appears on the screen quickly.\n fastPoll: function() {\n var missed = false, input = this;\n input.pollingFast = true;\n function p() {\n var changed = input.poll();\n if (!changed && !missed) {missed = true; input.polling.set(60, p);}\n else {input.pollingFast = false; input.slowPoll();}\n }\n input.polling.set(20, p);\n },\n\n // Read input from the textarea, and update the document to match.\n // When something is selected, it is present in the textarea, and\n // selected (unless it is huge, in which case a placeholder is\n // used). When nothing is selected, the cursor sits after previously\n // seen text (can be empty), which is stored in prevInput (we must\n // not reset the textarea when typing, because that breaks IME).\n poll: function() {\n var cm = this.cm, input = this.textarea, prevInput = this.prevInput;\n // Since this is called a *lot*, try to bail out as cheaply as\n // possible when it is clear that nothing happened. hasSelection\n // will be the case when there is a lot of text in the textarea,\n // in which case reading its value would be expensive.\n if (this.contextMenuPending || !cm.state.focused ||\n (hasSelection(input) && !prevInput && !this.composing) ||\n cm.isReadOnly() || cm.options.disableInput || cm.state.keySeq)\n return false;\n\n var text = input.value;\n // If nothing changed, bail.\n if (text == prevInput && !cm.somethingSelected()) return false;\n // Work around nonsensical selection resetting in IE9/10, and\n // inexplicable appearance of private area unicode characters on\n // some key combos in Mac (#2689).\n if (ie && ie_version >= 9 && this.hasSelection === text ||\n mac && /[\\uf700-\\uf7ff]/.test(text)) {\n cm.display.input.reset();\n return false;\n }\n\n if (cm.doc.sel == cm.display.selForContextMenu) {\n var first = text.charCodeAt(0);\n if (first == 0x200b && !prevInput) prevInput = \"\\u200b\";\n if (first == 0x21da) { this.reset(); return this.cm.execCommand(\"undo\"); }\n }\n // Find the part of the input that is actually new\n var same = 0, l = Math.min(prevInput.length, text.length);\n while (same < l && prevInput.charCodeAt(same) == text.charCodeAt(same)) ++same;\n\n var self = this;\n runInOp(cm, function() {\n applyTextInput(cm, text.slice(same), prevInput.length - same,\n null, self.composing ? \"*compose\" : null);\n\n // Don't leave long text in the textarea, since it makes further polling slow\n if (text.length > 1000 || text.indexOf(\"\\n\") > -1) input.value = self.prevInput = \"\";\n else self.prevInput = text;\n\n if (self.composing) {\n self.composing.range.clear();\n self.composing.range = cm.markText(self.composing.start, cm.getCursor(\"to\"),\n {className: \"CodeMirror-composing\"});\n }\n });\n return true;\n },\n\n ensurePolled: function() {\n if (this.pollingFast && this.poll()) this.pollingFast = false;\n },\n\n onKeyPress: function() {\n if (ie && ie_version >= 9) this.hasSelection = null;\n this.fastPoll();\n },\n\n onContextMenu: function(e) {\n var input = this, cm = input.cm, display = cm.display, te = input.textarea;\n var pos = posFromMouse(cm, e), scrollPos = display.scroller.scrollTop;\n if (!pos || presto) return; // Opera is difficult.\n\n // Reset the current text selection only if the click is done outside of the selection\n // and 'resetSelectionOnContextMenu' option is true.\n var reset = cm.options.resetSelectionOnContextMenu;\n if (reset && cm.doc.sel.contains(pos) == -1)\n operation(cm, setSelection)(cm.doc, simpleSelection(pos), sel_dontScroll);\n\n var oldCSS = te.style.cssText, oldWrapperCSS = input.wrapper.style.cssText;\n input.wrapper.style.cssText = \"position: absolute\"\n var wrapperBox = input.wrapper.getBoundingClientRect()\n te.style.cssText = \"position: absolute; width: 30px; height: 30px; top: \" + (e.clientY - wrapperBox.top - 5) +\n \"px; left: \" + (e.clientX - wrapperBox.left - 5) + \"px; z-index: 1000; background: \" +\n (ie ? \"rgba(255, 255, 255, .05)\" : \"transparent\") +\n \"; outline: none; border-width: 0; outline: none; overflow: hidden; opacity: .05; filter: alpha(opacity=5);\";\n if (webkit) var oldScrollY = window.scrollY; // Work around Chrome issue (#2712)\n display.input.focus();\n if (webkit) window.scrollTo(null, oldScrollY);\n display.input.reset();\n // Adds \"Select all\" to context menu in FF\n if (!cm.somethingSelected()) te.value = input.prevInput = \" \";\n input.contextMenuPending = true;\n display.selForContextMenu = cm.doc.sel;\n clearTimeout(display.detectingSelectAll);\n\n // Select-all will be greyed out if there's nothing to select, so\n // this adds a zero-width space so that we can later check whether\n // it got selected.\n function prepareSelectAllHack() {\n if (te.selectionStart != null) {\n var selected = cm.somethingSelected();\n var extval = \"\\u200b\" + (selected ? te.value : \"\");\n te.value = \"\\u21da\"; // Used to catch context-menu undo\n te.value = extval;\n input.prevInput = selected ? \"\" : \"\\u200b\";\n te.selectionStart = 1; te.selectionEnd = extval.length;\n // Re-set this, in case some other handler touched the\n // selection in the meantime.\n display.selForContextMenu = cm.doc.sel;\n }\n }\n function rehide() {\n input.contextMenuPending = false;\n input.wrapper.style.cssText = oldWrapperCSS\n te.style.cssText = oldCSS;\n if (ie && ie_version < 9) display.scrollbars.setScrollTop(display.scroller.scrollTop = scrollPos);\n\n // Try to detect the user choosing select-all\n if (te.selectionStart != null) {\n if (!ie || (ie && ie_version < 9)) prepareSelectAllHack();\n var i = 0, poll = function() {\n if (display.selForContextMenu == cm.doc.sel && te.selectionStart == 0 &&\n te.selectionEnd > 0 && input.prevInput == \"\\u200b\")\n operation(cm, commands.selectAll)(cm);\n else if (i++ < 10) display.detectingSelectAll = setTimeout(poll, 500);\n else display.input.reset();\n };\n display.detectingSelectAll = setTimeout(poll, 200);\n }\n }\n\n if (ie && ie_version >= 9) prepareSelectAllHack();\n if (captureRightClick) {\n e_stop(e);\n var mouseup = function() {\n off(window, \"mouseup\", mouseup);\n setTimeout(rehide, 20);\n };\n on(window, \"mouseup\", mouseup);\n } else {\n setTimeout(rehide, 50);\n }\n },\n\n readOnlyChanged: function(val) {\n if (!val) this.reset();\n },\n\n setUneditable: nothing,\n\n needsContentAttribute: false\n }, TextareaInput.prototype);\n\n // CONTENTEDITABLE INPUT STYLE\n\n function ContentEditableInput(cm) {\n this.cm = cm;\n this.lastAnchorNode = this.lastAnchorOffset = this.lastFocusNode = this.lastFocusOffset = null;\n this.polling = new Delayed();\n this.gracePeriod = false;\n }\n\n ContentEditableInput.prototype = copyObj({\n init: function(display) {\n var input = this, cm = input.cm;\n var div = input.div = display.lineDiv;\n disableBrowserMagic(div);\n\n on(div, \"paste\", function(e) {\n if (!signalDOMEvent(cm, e)) handlePaste(e, cm);\n })\n\n on(div, \"compositionstart\", function(e) {\n var data = e.data;\n input.composing = {sel: cm.doc.sel, data: data, startData: data};\n if (!data) return;\n var prim = cm.doc.sel.primary();\n var line = cm.getLine(prim.head.line);\n var found = line.indexOf(data, Math.max(0, prim.head.ch - data.length));\n if (found > -1 && found <= prim.head.ch)\n input.composing.sel = simpleSelection(Pos(prim.head.line, found),\n Pos(prim.head.line, found + data.length));\n });\n on(div, \"compositionupdate\", function(e) {\n input.composing.data = e.data;\n });\n on(div, \"compositionend\", function(e) {\n var ours = input.composing;\n if (!ours) return;\n if (e.data != ours.startData && !/\\u200b/.test(e.data))\n ours.data = e.data;\n // Need a small delay to prevent other code (input event,\n // selection polling) from doing damage when fired right after\n // compositionend.\n setTimeout(function() {\n if (!ours.handled)\n input.applyComposition(ours);\n if (input.composing == ours)\n input.composing = null;\n }, 50);\n });\n\n on(div, \"touchstart\", function() {\n input.forceCompositionEnd();\n });\n\n on(div, \"input\", function() {\n if (input.composing) return;\n if (cm.isReadOnly() || !input.pollContent())\n runInOp(input.cm, function() {regChange(cm);});\n });\n\n function onCopyCut(e) {\n if (signalDOMEvent(cm, e)) return\n if (cm.somethingSelected()) {\n lastCopied = cm.getSelections();\n if (e.type == \"cut\") cm.replaceSelection(\"\", null, \"cut\");\n } else if (!cm.options.lineWiseCopyCut) {\n return;\n } else {\n var ranges = copyableRanges(cm);\n lastCopied = ranges.text;\n if (e.type == \"cut\") {\n cm.operation(function() {\n cm.setSelections(ranges.ranges, 0, sel_dontScroll);\n cm.replaceSelection(\"\", null, \"cut\");\n });\n }\n }\n // iOS exposes the clipboard API, but seems to discard content inserted into it\n if (e.clipboardData && !ios) {\n e.preventDefault();\n e.clipboardData.clearData();\n e.clipboardData.setData(\"text/plain\", lastCopied.join(\"\\n\"));\n } else {\n // Old-fashioned briefly-focus-a-textarea hack\n var kludge = hiddenTextarea(), te = kludge.firstChild;\n cm.display.lineSpace.insertBefore(kludge, cm.display.lineSpace.firstChild);\n te.value = lastCopied.join(\"\\n\");\n var hadFocus = document.activeElement;\n selectInput(te);\n setTimeout(function() {\n cm.display.lineSpace.removeChild(kludge);\n hadFocus.focus();\n }, 50);\n }\n }\n on(div, \"copy\", onCopyCut);\n on(div, \"cut\", onCopyCut);\n },\n\n prepareSelection: function() {\n var result = prepareSelection(this.cm, false);\n result.focus = this.cm.state.focused;\n return result;\n },\n\n showSelection: function(info) {\n if (!info || !this.cm.display.view.length) return;\n if (info.focus) this.showPrimarySelection();\n this.showMultipleSelections(info);\n },\n\n showPrimarySelection: function() {\n var sel = window.getSelection(), prim = this.cm.doc.sel.primary();\n var curAnchor = domToPos(this.cm, sel.anchorNode, sel.anchorOffset);\n var curFocus = domToPos(this.cm, sel.focusNode, sel.focusOffset);\n if (curAnchor && !curAnchor.bad && curFocus && !curFocus.bad &&\n cmp(minPos(curAnchor, curFocus), prim.from()) == 0 &&\n cmp(maxPos(curAnchor, curFocus), prim.to()) == 0)\n return;\n\n var start = posToDOM(this.cm, prim.from());\n var end = posToDOM(this.cm, prim.to());\n if (!start && !end) return;\n\n var view = this.cm.display.view;\n var old = sel.rangeCount && sel.getRangeAt(0);\n if (!start) {\n start = {node: view[0].measure.map[2], offset: 0};\n } else if (!end) { // FIXME dangerously hacky\n var measure = view[view.length - 1].measure;\n var map = measure.maps ? measure.maps[measure.maps.length - 1] : measure.map;\n end = {node: map[map.length - 1], offset: map[map.length - 2] - map[map.length - 3]};\n }\n\n try { var rng = range(start.node, start.offset, end.offset, end.node); }\n catch(e) {} // Our model of the DOM might be outdated, in which case the range we try to set can be impossible\n if (rng) {\n if (!gecko && this.cm.state.focused) {\n sel.collapse(start.node, start.offset);\n if (!rng.collapsed) sel.addRange(rng);\n } else {\n sel.removeAllRanges();\n sel.addRange(rng);\n }\n if (old && sel.anchorNode == null) sel.addRange(old);\n else if (gecko) this.startGracePeriod();\n }\n this.rememberSelection();\n },\n\n startGracePeriod: function() {\n var input = this;\n clearTimeout(this.gracePeriod);\n this.gracePeriod = setTimeout(function() {\n input.gracePeriod = false;\n if (input.selectionChanged())\n input.cm.operation(function() { input.cm.curOp.selectionChanged = true; });\n }, 20);\n },\n\n showMultipleSelections: function(info) {\n removeChildrenAndAdd(this.cm.display.cursorDiv, info.cursors);\n removeChildrenAndAdd(this.cm.display.selectionDiv, info.selection);\n },\n\n rememberSelection: function() {\n var sel = window.getSelection();\n this.lastAnchorNode = sel.anchorNode; this.lastAnchorOffset = sel.anchorOffset;\n this.lastFocusNode = sel.focusNode; this.lastFocusOffset = sel.focusOffset;\n },\n\n selectionInEditor: function() {\n var sel = window.getSelection();\n if (!sel.rangeCount) return false;\n var node = sel.getRangeAt(0).commonAncestorContainer;\n return contains(this.div, node);\n },\n\n focus: function() {\n if (this.cm.options.readOnly != \"nocursor\") this.div.focus();\n },\n blur: function() { this.div.blur(); },\n getField: function() { return this.div; },\n\n supportsTouch: function() { return true; },\n\n receivedFocus: function() {\n var input = this;\n if (this.selectionInEditor())\n this.pollSelection();\n else\n runInOp(this.cm, function() { input.cm.curOp.selectionChanged = true; });\n\n function poll() {\n if (input.cm.state.focused) {\n input.pollSelection();\n input.polling.set(input.cm.options.pollInterval, poll);\n }\n }\n this.polling.set(this.cm.options.pollInterval, poll);\n },\n\n selectionChanged: function() {\n var sel = window.getSelection();\n return sel.anchorNode != this.lastAnchorNode || sel.anchorOffset != this.lastAnchorOffset ||\n sel.focusNode != this.lastFocusNode || sel.focusOffset != this.lastFocusOffset;\n },\n\n pollSelection: function() {\n if (!this.composing && !this.gracePeriod && this.selectionChanged()) {\n var sel = window.getSelection(), cm = this.cm;\n this.rememberSelection();\n var anchor = domToPos(cm, sel.anchorNode, sel.anchorOffset);\n var head = domToPos(cm, sel.focusNode, sel.focusOffset);\n if (anchor && head) runInOp(cm, function() {\n setSelection(cm.doc, simpleSelection(anchor, head), sel_dontScroll);\n if (anchor.bad || head.bad) cm.curOp.selectionChanged = true;\n });\n }\n },\n\n pollContent: function() {\n var cm = this.cm, display = cm.display, sel = cm.doc.sel.primary();\n var from = sel.from(), to = sel.to();\n if (from.line < display.viewFrom || to.line > display.viewTo - 1) return false;\n\n var fromIndex;\n if (from.line == display.viewFrom || (fromIndex = findViewIndex(cm, from.line)) == 0) {\n var fromLine = lineNo(display.view[0].line);\n var fromNode = display.view[0].node;\n } else {\n var fromLine = lineNo(display.view[fromIndex].line);\n var fromNode = display.view[fromIndex - 1].node.nextSibling;\n }\n var toIndex = findViewIndex(cm, to.line);\n if (toIndex == display.view.length - 1) {\n var toLine = display.viewTo - 1;\n var toNode = display.lineDiv.lastChild;\n } else {\n var toLine = lineNo(display.view[toIndex + 1].line) - 1;\n var toNode = display.view[toIndex + 1].node.previousSibling;\n }\n\n var newText = cm.doc.splitLines(domTextBetween(cm, fromNode, toNode, fromLine, toLine));\n var oldText = getBetween(cm.doc, Pos(fromLine, 0), Pos(toLine, getLine(cm.doc, toLine).text.length));\n while (newText.length > 1 && oldText.length > 1) {\n if (lst(newText) == lst(oldText)) { newText.pop(); oldText.pop(); toLine--; }\n else if (newText[0] == oldText[0]) { newText.shift(); oldText.shift(); fromLine++; }\n else break;\n }\n\n var cutFront = 0, cutEnd = 0;\n var newTop = newText[0], oldTop = oldText[0], maxCutFront = Math.min(newTop.length, oldTop.length);\n while (cutFront < maxCutFront && newTop.charCodeAt(cutFront) == oldTop.charCodeAt(cutFront))\n ++cutFront;\n var newBot = lst(newText), oldBot = lst(oldText);\n var maxCutEnd = Math.min(newBot.length - (newText.length == 1 ? cutFront : 0),\n oldBot.length - (oldText.length == 1 ? cutFront : 0));\n while (cutEnd < maxCutEnd &&\n newBot.charCodeAt(newBot.length - cutEnd - 1) == oldBot.charCodeAt(oldBot.length - cutEnd - 1))\n ++cutEnd;\n\n newText[newText.length - 1] = newBot.slice(0, newBot.length - cutEnd);\n newText[0] = newText[0].slice(cutFront);\n\n var chFrom = Pos(fromLine, cutFront);\n var chTo = Pos(toLine, oldText.length ? lst(oldText).length - cutEnd : 0);\n if (newText.length > 1 || newText[0] || cmp(chFrom, chTo)) {\n replaceRange(cm.doc, newText, chFrom, chTo, \"+input\");\n return true;\n }\n },\n\n ensurePolled: function() {\n this.forceCompositionEnd();\n },\n reset: function() {\n this.forceCompositionEnd();\n },\n forceCompositionEnd: function() {\n if (!this.composing || this.composing.handled) return;\n this.applyComposition(this.composing);\n this.composing.handled = true;\n this.div.blur();\n this.div.focus();\n },\n applyComposition: function(composing) {\n if (this.cm.isReadOnly())\n operation(this.cm, regChange)(this.cm)\n else if (composing.data && composing.data != composing.startData)\n operation(this.cm, applyTextInput)(this.cm, composing.data, 0, composing.sel);\n },\n\n setUneditable: function(node) {\n node.contentEditable = \"false\"\n },\n\n onKeyPress: function(e) {\n e.preventDefault();\n if (!this.cm.isReadOnly())\n operation(this.cm, applyTextInput)(this.cm, String.fromCharCode(e.charCode == null ? e.keyCode : e.charCode), 0);\n },\n\n readOnlyChanged: function(val) {\n this.div.contentEditable = String(val != \"nocursor\")\n },\n\n onContextMenu: nothing,\n resetPosition: nothing,\n\n needsContentAttribute: true\n }, ContentEditableInput.prototype);\n\n function posToDOM(cm, pos) {\n var view = findViewForLine(cm, pos.line);\n if (!view || view.hidden) return null;\n var line = getLine(cm.doc, pos.line);\n var info = mapFromLineView(view, line, pos.line);\n\n var order = getOrder(line), side = \"left\";\n if (order) {\n var partPos = getBidiPartAt(order, pos.ch);\n side = partPos % 2 ? \"right\" : \"left\";\n }\n var result = nodeAndOffsetInLineMap(info.map, pos.ch, side);\n result.offset = result.collapse == \"right\" ? result.end : result.start;\n return result;\n }\n\n function badPos(pos, bad) { if (bad) pos.bad = true; return pos; }\n\n function domToPos(cm, node, offset) {\n var lineNode;\n if (node == cm.display.lineDiv) {\n lineNode = cm.display.lineDiv.childNodes[offset];\n if (!lineNode) return badPos(cm.clipPos(Pos(cm.display.viewTo - 1)), true);\n node = null; offset = 0;\n } else {\n for (lineNode = node;; lineNode = lineNode.parentNode) {\n if (!lineNode || lineNode == cm.display.lineDiv) return null;\n if (lineNode.parentNode && lineNode.parentNode == cm.display.lineDiv) break;\n }\n }\n for (var i = 0; i < cm.display.view.length; i++) {\n var lineView = cm.display.view[i];\n if (lineView.node == lineNode)\n return locateNodeInLineView(lineView, node, offset);\n }\n }\n\n function locateNodeInLineView(lineView, node, offset) {\n var wrapper = lineView.text.firstChild, bad = false;\n if (!node || !contains(wrapper, node)) return badPos(Pos(lineNo(lineView.line), 0), true);\n if (node == wrapper) {\n bad = true;\n node = wrapper.childNodes[offset];\n offset = 0;\n if (!node) {\n var line = lineView.rest ? lst(lineView.rest) : lineView.line;\n return badPos(Pos(lineNo(line), line.text.length), bad);\n }\n }\n\n var textNode = node.nodeType == 3 ? node : null, topNode = node;\n if (!textNode && node.childNodes.length == 1 && node.firstChild.nodeType == 3) {\n textNode = node.firstChild;\n if (offset) offset = textNode.nodeValue.length;\n }\n while (topNode.parentNode != wrapper) topNode = topNode.parentNode;\n var measure = lineView.measure, maps = measure.maps;\n\n function find(textNode, topNode, offset) {\n for (var i = -1; i < (maps ? maps.length : 0); i++) {\n var map = i < 0 ? measure.map : maps[i];\n for (var j = 0; j < map.length; j += 3) {\n var curNode = map[j + 2];\n if (curNode == textNode || curNode == topNode) {\n var line = lineNo(i < 0 ? lineView.line : lineView.rest[i]);\n var ch = map[j] + offset;\n if (offset < 0 || curNode != textNode) ch = map[j + (offset ? 1 : 0)];\n return Pos(line, ch);\n }\n }\n }\n }\n var found = find(textNode, topNode, offset);\n if (found) return badPos(found, bad);\n\n // FIXME this is all really shaky. might handle the few cases it needs to handle, but likely to cause problems\n for (var after = topNode.nextSibling, dist = textNode ? textNode.nodeValue.length - offset : 0; after; after = after.nextSibling) {\n found = find(after, after.firstChild, 0);\n if (found)\n return badPos(Pos(found.line, found.ch - dist), bad);\n else\n dist += after.textContent.length;\n }\n for (var before = topNode.previousSibling, dist = offset; before; before = before.previousSibling) {\n found = find(before, before.firstChild, -1);\n if (found)\n return badPos(Pos(found.line, found.ch + dist), bad);\n else\n dist += after.textContent.length;\n }\n }\n\n function domTextBetween(cm, from, to, fromLine, toLine) {\n var text = \"\", closing = false, lineSep = cm.doc.lineSeparator();\n function recognizeMarker(id) { return function(marker) { return marker.id == id; }; }\n function walk(node) {\n if (node.nodeType == 1) {\n var cmText = node.getAttribute(\"cm-text\");\n if (cmText != null) {\n if (cmText == \"\") cmText = node.textContent.replace(/\\u200b/g, \"\");\n text += cmText;\n return;\n }\n var markerID = node.getAttribute(\"cm-marker\"), range;\n if (markerID) {\n var found = cm.findMarks(Pos(fromLine, 0), Pos(toLine + 1, 0), recognizeMarker(+markerID));\n if (found.length && (range = found[0].find()))\n text += getBetween(cm.doc, range.from, range.to).join(lineSep);\n return;\n }\n if (node.getAttribute(\"contenteditable\") == \"false\") return;\n for (var i = 0; i < node.childNodes.length; i++)\n walk(node.childNodes[i]);\n if (/^(pre|div|p)$/i.test(node.nodeName))\n closing = true;\n } else if (node.nodeType == 3) {\n var val = node.nodeValue;\n if (!val) return;\n if (closing) {\n text += lineSep;\n closing = false;\n }\n text += val;\n }\n }\n for (;;) {\n walk(from);\n if (from == to) break;\n from = from.nextSibling;\n }\n return text;\n }\n\n CodeMirror.inputStyles = {\"textarea\": TextareaInput, \"contenteditable\": ContentEditableInput};\n\n // SELECTION / CURSOR\n\n // Selection objects are immutable. A new one is created every time\n // the selection changes. A selection is one or more non-overlapping\n // (and non-touching) ranges, sorted, and an integer that indicates\n // which one is the primary selection (the one that's scrolled into\n // view, that getCursor returns, etc).\n function Selection(ranges, primIndex) {\n this.ranges = ranges;\n this.primIndex = primIndex;\n }\n\n Selection.prototype = {\n primary: function() { return this.ranges[this.primIndex]; },\n equals: function(other) {\n if (other == this) return true;\n if (other.primIndex != this.primIndex || other.ranges.length != this.ranges.length) return false;\n for (var i = 0; i < this.ranges.length; i++) {\n var here = this.ranges[i], there = other.ranges[i];\n if (cmp(here.anchor, there.anchor) != 0 || cmp(here.head, there.head) != 0) return false;\n }\n return true;\n },\n deepCopy: function() {\n for (var out = [], i = 0; i < this.ranges.length; i++)\n out[i] = new Range(copyPos(this.ranges[i].anchor), copyPos(this.ranges[i].head));\n return new Selection(out, this.primIndex);\n },\n somethingSelected: function() {\n for (var i = 0; i < this.ranges.length; i++)\n if (!this.ranges[i].empty()) return true;\n return false;\n },\n contains: function(pos, end) {\n if (!end) end = pos;\n for (var i = 0; i < this.ranges.length; i++) {\n var range = this.ranges[i];\n if (cmp(end, range.from()) >= 0 && cmp(pos, range.to()) <= 0)\n return i;\n }\n return -1;\n }\n };\n\n function Range(anchor, head) {\n this.anchor = anchor; this.head = head;\n }\n\n Range.prototype = {\n from: function() { return minPos(this.anchor, this.head); },\n to: function() { return maxPos(this.anchor, this.head); },\n empty: function() {\n return this.head.line == this.anchor.line && this.head.ch == this.anchor.ch;\n }\n };\n\n // Take an unsorted, potentially overlapping set of ranges, and\n // build a selection out of it. 'Consumes' ranges array (modifying\n // it).\n function normalizeSelection(ranges, primIndex) {\n var prim = ranges[primIndex];\n ranges.sort(function(a, b) { return cmp(a.from(), b.from()); });\n primIndex = indexOf(ranges, prim);\n for (var i = 1; i < ranges.length; i++) {\n var cur = ranges[i], prev = ranges[i - 1];\n if (cmp(prev.to(), cur.from()) >= 0) {\n var from = minPos(prev.from(), cur.from()), to = maxPos(prev.to(), cur.to());\n var inv = prev.empty() ? cur.from() == cur.head : prev.from() == prev.head;\n if (i <= primIndex) --primIndex;\n ranges.splice(--i, 2, new Range(inv ? to : from, inv ? from : to));\n }\n }\n return new Selection(ranges, primIndex);\n }\n\n function simpleSelection(anchor, head) {\n return new Selection([new Range(anchor, head || anchor)], 0);\n }\n\n // Most of the external API clips given positions to make sure they\n // actually exist within the document.\n function clipLine(doc, n) {return Math.max(doc.first, Math.min(n, doc.first + doc.size - 1));}\n function clipPos(doc, pos) {\n if (pos.line < doc.first) return Pos(doc.first, 0);\n var last = doc.first + doc.size - 1;\n if (pos.line > last) return Pos(last, getLine(doc, last).text.length);\n return clipToLen(pos, getLine(doc, pos.line).text.length);\n }\n function clipToLen(pos, linelen) {\n var ch = pos.ch;\n if (ch == null || ch > linelen) return Pos(pos.line, linelen);\n else if (ch < 0) return Pos(pos.line, 0);\n else return pos;\n }\n function isLine(doc, l) {return l >= doc.first && l < doc.first + doc.size;}\n function clipPosArray(doc, array) {\n for (var out = [], i = 0; i < array.length; i++) out[i] = clipPos(doc, array[i]);\n return out;\n }\n\n // SELECTION UPDATES\n\n // The 'scroll' parameter given to many of these indicated whether\n // the new cursor position should be scrolled into view after\n // modifying the selection.\n\n // If shift is held or the extend flag is set, extends a range to\n // include a given position (and optionally a second position).\n // Otherwise, simply returns the range between the given positions.\n // Used for cursor motion and such.\n function extendRange(doc, range, head, other) {\n if (doc.cm && doc.cm.display.shift || doc.extend) {\n var anchor = range.anchor;\n if (other) {\n var posBefore = cmp(head, anchor) < 0;\n if (posBefore != (cmp(other, anchor) < 0)) {\n anchor = head;\n head = other;\n } else if (posBefore != (cmp(head, other) < 0)) {\n head = other;\n }\n }\n return new Range(anchor, head);\n } else {\n return new Range(other || head, head);\n }\n }\n\n // Extend the primary selection range, discard the rest.\n function extendSelection(doc, head, other, options) {\n setSelection(doc, new Selection([extendRange(doc, doc.sel.primary(), head, other)], 0), options);\n }\n\n // Extend all selections (pos is an array of selections with length\n // equal the number of selections)\n function extendSelections(doc, heads, options) {\n for (var out = [], i = 0; i < doc.sel.ranges.length; i++)\n out[i] = extendRange(doc, doc.sel.ranges[i], heads[i], null);\n var newSel = normalizeSelection(out, doc.sel.primIndex);\n setSelection(doc, newSel, options);\n }\n\n // Updates a single range in the selection.\n function replaceOneSelection(doc, i, range, options) {\n var ranges = doc.sel.ranges.slice(0);\n ranges[i] = range;\n setSelection(doc, normalizeSelection(ranges, doc.sel.primIndex), options);\n }\n\n // Reset the selection to a single range.\n function setSimpleSelection(doc, anchor, head, options) {\n setSelection(doc, simpleSelection(anchor, head), options);\n }\n\n // Give beforeSelectionChange handlers a change to influence a\n // selection update.\n function filterSelectionChange(doc, sel, options) {\n var obj = {\n ranges: sel.ranges,\n update: function(ranges) {\n this.ranges = [];\n for (var i = 0; i < ranges.length; i++)\n this.ranges[i] = new Range(clipPos(doc, ranges[i].anchor),\n clipPos(doc, ranges[i].head));\n },\n origin: options && options.origin\n };\n signal(doc, \"beforeSelectionChange\", doc, obj);\n if (doc.cm) signal(doc.cm, \"beforeSelectionChange\", doc.cm, obj);\n if (obj.ranges != sel.ranges) return normalizeSelection(obj.ranges, obj.ranges.length - 1);\n else return sel;\n }\n\n function setSelectionReplaceHistory(doc, sel, options) {\n var done = doc.history.done, last = lst(done);\n if (last && last.ranges) {\n done[done.length - 1] = sel;\n setSelectionNoUndo(doc, sel, options);\n } else {\n setSelection(doc, sel, options);\n }\n }\n\n // Set a new selection.\n function setSelection(doc, sel, options) {\n setSelectionNoUndo(doc, sel, options);\n addSelectionToHistory(doc, doc.sel, doc.cm ? doc.cm.curOp.id : NaN, options);\n }\n\n function setSelectionNoUndo(doc, sel, options) {\n if (hasHandler(doc, \"beforeSelectionChange\") || doc.cm && hasHandler(doc.cm, \"beforeSelectionChange\"))\n sel = filterSelectionChange(doc, sel, options);\n\n var bias = options && options.bias ||\n (cmp(sel.primary().head, doc.sel.primary().head) < 0 ? -1 : 1);\n setSelectionInner(doc, skipAtomicInSelection(doc, sel, bias, true));\n\n if (!(options && options.scroll === false) && doc.cm)\n ensureCursorVisible(doc.cm);\n }\n\n function setSelectionInner(doc, sel) {\n if (sel.equals(doc.sel)) return;\n\n doc.sel = sel;\n\n if (doc.cm) {\n doc.cm.curOp.updateInput = doc.cm.curOp.selectionChanged = true;\n signalCursorActivity(doc.cm);\n }\n signalLater(doc, \"cursorActivity\", doc);\n }\n\n // Verify that the selection does not partially select any atomic\n // marked ranges.\n function reCheckSelection(doc) {\n setSelectionInner(doc, skipAtomicInSelection(doc, doc.sel, null, false), sel_dontScroll);\n }\n\n // Return a selection that does not partially select any atomic\n // ranges.\n function skipAtomicInSelection(doc, sel, bias, mayClear) {\n var out;\n for (var i = 0; i < sel.ranges.length; i++) {\n var range = sel.ranges[i];\n var old = sel.ranges.length == doc.sel.ranges.length && doc.sel.ranges[i];\n var newAnchor = skipAtomic(doc, range.anchor, old && old.anchor, bias, mayClear);\n var newHead = skipAtomic(doc, range.head, old && old.head, bias, mayClear);\n if (out || newAnchor != range.anchor || newHead != range.head) {\n if (!out) out = sel.ranges.slice(0, i);\n out[i] = new Range(newAnchor, newHead);\n }\n }\n return out ? normalizeSelection(out, sel.primIndex) : sel;\n }\n\n function skipAtomicInner(doc, pos, oldPos, dir, mayClear) {\n var line = getLine(doc, pos.line);\n if (line.markedSpans) for (var i = 0; i < line.markedSpans.length; ++i) {\n var sp = line.markedSpans[i], m = sp.marker;\n if ((sp.from == null || (m.inclusiveLeft ? sp.from <= pos.ch : sp.from < pos.ch)) &&\n (sp.to == null || (m.inclusiveRight ? sp.to >= pos.ch : sp.to > pos.ch))) {\n if (mayClear) {\n signal(m, \"beforeCursorEnter\");\n if (m.explicitlyCleared) {\n if (!line.markedSpans) break;\n else {--i; continue;}\n }\n }\n if (!m.atomic) continue;\n\n if (oldPos) {\n var near = m.find(dir < 0 ? 1 : -1), diff;\n if (dir < 0 ? m.inclusiveRight : m.inclusiveLeft)\n near = movePos(doc, near, -dir, near && near.line == pos.line ? line : null);\n if (near && near.line == pos.line && (diff = cmp(near, oldPos)) && (dir < 0 ? diff < 0 : diff > 0))\n return skipAtomicInner(doc, near, pos, dir, mayClear);\n }\n\n var far = m.find(dir < 0 ? -1 : 1);\n if (dir < 0 ? m.inclusiveLeft : m.inclusiveRight)\n far = movePos(doc, far, dir, far.line == pos.line ? line : null);\n return far ? skipAtomicInner(doc, far, pos, dir, mayClear) : null;\n }\n }\n return pos;\n }\n\n // Ensure a given position is not inside an atomic range.\n function skipAtomic(doc, pos, oldPos, bias, mayClear) {\n var dir = bias || 1;\n var found = skipAtomicInner(doc, pos, oldPos, dir, mayClear) ||\n (!mayClear && skipAtomicInner(doc, pos, oldPos, dir, true)) ||\n skipAtomicInner(doc, pos, oldPos, -dir, mayClear) ||\n (!mayClear && skipAtomicInner(doc, pos, oldPos, -dir, true));\n if (!found) {\n doc.cantEdit = true;\n return Pos(doc.first, 0);\n }\n return found;\n }\n\n function movePos(doc, pos, dir, line) {\n if (dir < 0 && pos.ch == 0) {\n if (pos.line > doc.first) return clipPos(doc, Pos(pos.line - 1));\n else return null;\n } else if (dir > 0 && pos.ch == (line || getLine(doc, pos.line)).text.length) {\n if (pos.line < doc.first + doc.size - 1) return Pos(pos.line + 1, 0);\n else return null;\n } else {\n return new Pos(pos.line, pos.ch + dir);\n }\n }\n\n // SELECTION DRAWING\n\n function updateSelection(cm) {\n cm.display.input.showSelection(cm.display.input.prepareSelection());\n }\n\n function prepareSelection(cm, primary) {\n var doc = cm.doc, result = {};\n var curFragment = result.cursors = document.createDocumentFragment();\n var selFragment = result.selection = document.createDocumentFragment();\n\n for (var i = 0; i < doc.sel.ranges.length; i++) {\n if (primary === false && i == doc.sel.primIndex) continue;\n var range = doc.sel.ranges[i];\n if (range.from().line >= cm.display.viewTo || range.to().line < cm.display.viewFrom) continue;\n var collapsed = range.empty();\n if (collapsed || cm.options.showCursorWhenSelecting)\n drawSelectionCursor(cm, range.head, curFragment);\n if (!collapsed)\n drawSelectionRange(cm, range, selFragment);\n }\n return result;\n }\n\n // Draws a cursor for the given range\n function drawSelectionCursor(cm, head, output) {\n var pos = cursorCoords(cm, head, \"div\", null, null, !cm.options.singleCursorHeightPerLine);\n\n var cursor = output.appendChild(elt(\"div\", \"\\u00a0\", \"CodeMirror-cursor\"));\n cursor.style.left = pos.left + \"px\";\n cursor.style.top = pos.top + \"px\";\n cursor.style.height = Math.max(0, pos.bottom - pos.top) * cm.options.cursorHeight + \"px\";\n\n if (pos.other) {\n // Secondary cursor, shown when on a 'jump' in bi-directional text\n var otherCursor = output.appendChild(elt(\"div\", \"\\u00a0\", \"CodeMirror-cursor CodeMirror-secondarycursor\"));\n otherCursor.style.display = \"\";\n otherCursor.style.left = pos.other.left + \"px\";\n otherCursor.style.top = pos.other.top + \"px\";\n otherCursor.style.height = (pos.other.bottom - pos.other.top) * .85 + \"px\";\n }\n }\n\n // Draws the given range as a highlighted selection\n function drawSelectionRange(cm, range, output) {\n var display = cm.display, doc = cm.doc;\n var fragment = document.createDocumentFragment();\n var padding = paddingH(cm.display), leftSide = padding.left;\n var rightSide = Math.max(display.sizerWidth, displayWidth(cm) - display.sizer.offsetLeft) - padding.right;\n\n function add(left, top, width, bottom) {\n if (top < 0) top = 0;\n top = Math.round(top);\n bottom = Math.round(bottom);\n fragment.appendChild(elt(\"div\", null, \"CodeMirror-selected\", \"position: absolute; left: \" + left +\n \"px; top: \" + top + \"px; width: \" + (width == null ? rightSide - left : width) +\n \"px; height: \" + (bottom - top) + \"px\"));\n }\n\n function drawForLine(line, fromArg, toArg) {\n var lineObj = getLine(doc, line);\n var lineLen = lineObj.text.length;\n var start, end;\n function coords(ch, bias) {\n return charCoords(cm, Pos(line, ch), \"div\", lineObj, bias);\n }\n\n iterateBidiSections(getOrder(lineObj), fromArg || 0, toArg == null ? lineLen : toArg, function(from, to, dir) {\n var leftPos = coords(from, \"left\"), rightPos, left, right;\n if (from == to) {\n rightPos = leftPos;\n left = right = leftPos.left;\n } else {\n rightPos = coords(to - 1, \"right\");\n if (dir == \"rtl\") { var tmp = leftPos; leftPos = rightPos; rightPos = tmp; }\n left = leftPos.left;\n right = rightPos.right;\n }\n if (fromArg == null && from == 0) left = leftSide;\n if (rightPos.top - leftPos.top > 3) { // Different lines, draw top part\n add(left, leftPos.top, null, leftPos.bottom);\n left = leftSide;\n if (leftPos.bottom < rightPos.top) add(left, leftPos.bottom, null, rightPos.top);\n }\n if (toArg == null && to == lineLen) right = rightSide;\n if (!start || leftPos.top < start.top || leftPos.top == start.top && leftPos.left < start.left)\n start = leftPos;\n if (!end || rightPos.bottom > end.bottom || rightPos.bottom == end.bottom && rightPos.right > end.right)\n end = rightPos;\n if (left < leftSide + 1) left = leftSide;\n add(left, rightPos.top, right - left, rightPos.bottom);\n });\n return {start: start, end: end};\n }\n\n var sFrom = range.from(), sTo = range.to();\n if (sFrom.line == sTo.line) {\n drawForLine(sFrom.line, sFrom.ch, sTo.ch);\n } else {\n var fromLine = getLine(doc, sFrom.line), toLine = getLine(doc, sTo.line);\n var singleVLine = visualLine(fromLine) == visualLine(toLine);\n var leftEnd = drawForLine(sFrom.line, sFrom.ch, singleVLine ? fromLine.text.length + 1 : null).end;\n var rightStart = drawForLine(sTo.line, singleVLine ? 0 : null, sTo.ch).start;\n if (singleVLine) {\n if (leftEnd.top < rightStart.top - 2) {\n add(leftEnd.right, leftEnd.top, null, leftEnd.bottom);\n add(leftSide, rightStart.top, rightStart.left, rightStart.bottom);\n } else {\n add(leftEnd.right, leftEnd.top, rightStart.left - leftEnd.right, leftEnd.bottom);\n }\n }\n if (leftEnd.bottom < rightStart.top)\n add(leftSide, leftEnd.bottom, null, rightStart.top);\n }\n\n output.appendChild(fragment);\n }\n\n // Cursor-blinking\n function restartBlink(cm) {\n if (!cm.state.focused) return;\n var display = cm.display;\n clearInterval(display.blinker);\n var on = true;\n display.cursorDiv.style.visibility = \"\";\n if (cm.options.cursorBlinkRate > 0)\n display.blinker = setInterval(function() {\n display.cursorDiv.style.visibility = (on = !on) ? \"\" : \"hidden\";\n }, cm.options.cursorBlinkRate);\n else if (cm.options.cursorBlinkRate < 0)\n display.cursorDiv.style.visibility = \"hidden\";\n }\n\n // HIGHLIGHT WORKER\n\n function startWorker(cm, time) {\n if (cm.doc.mode.startState && cm.doc.frontier < cm.display.viewTo)\n cm.state.highlight.set(time, bind(highlightWorker, cm));\n }\n\n function highlightWorker(cm) {\n var doc = cm.doc;\n if (doc.frontier < doc.first) doc.frontier = doc.first;\n if (doc.frontier >= cm.display.viewTo) return;\n var end = +new Date + cm.options.workTime;\n var state = copyState(doc.mode, getStateBefore(cm, doc.frontier));\n var changedLines = [];\n\n doc.iter(doc.frontier, Math.min(doc.first + doc.size, cm.display.viewTo + 500), function(line) {\n if (doc.frontier >= cm.display.viewFrom) { // Visible\n var oldStyles = line.styles, tooLong = line.text.length > cm.options.maxHighlightLength;\n var highlighted = highlightLine(cm, line, tooLong ? copyState(doc.mode, state) : state, true);\n line.styles = highlighted.styles;\n var oldCls = line.styleClasses, newCls = highlighted.classes;\n if (newCls) line.styleClasses = newCls;\n else if (oldCls) line.styleClasses = null;\n var ischange = !oldStyles || oldStyles.length != line.styles.length ||\n oldCls != newCls && (!oldCls || !newCls || oldCls.bgClass != newCls.bgClass || oldCls.textClass != newCls.textClass);\n for (var i = 0; !ischange && i < oldStyles.length; ++i) ischange = oldStyles[i] != line.styles[i];\n if (ischange) changedLines.push(doc.frontier);\n line.stateAfter = tooLong ? state : copyState(doc.mode, state);\n } else {\n if (line.text.length <= cm.options.maxHighlightLength)\n processLine(cm, line.text, state);\n line.stateAfter = doc.frontier % 5 == 0 ? copyState(doc.mode, state) : null;\n }\n ++doc.frontier;\n if (+new Date > end) {\n startWorker(cm, cm.options.workDelay);\n return true;\n }\n });\n if (changedLines.length) runInOp(cm, function() {\n for (var i = 0; i < changedLines.length; i++)\n regLineChange(cm, changedLines[i], \"text\");\n });\n }\n\n // Finds the line to start with when starting a parse. Tries to\n // find a line with a stateAfter, so that it can start with a\n // valid state. If that fails, it returns the line with the\n // smallest indentation, which tends to need the least context to\n // parse correctly.\n function findStartLine(cm, n, precise) {\n var minindent, minline, doc = cm.doc;\n var lim = precise ? -1 : n - (cm.doc.mode.innerMode ? 1000 : 100);\n for (var search = n; search > lim; --search) {\n if (search <= doc.first) return doc.first;\n var line = getLine(doc, search - 1);\n if (line.stateAfter && (!precise || search <= doc.frontier)) return search;\n var indented = countColumn(line.text, null, cm.options.tabSize);\n if (minline == null || minindent > indented) {\n minline = search - 1;\n minindent = indented;\n }\n }\n return minline;\n }\n\n function getStateBefore(cm, n, precise) {\n var doc = cm.doc, display = cm.display;\n if (!doc.mode.startState) return true;\n var pos = findStartLine(cm, n, precise), state = pos > doc.first && getLine(doc, pos-1).stateAfter;\n if (!state) state = startState(doc.mode);\n else state = copyState(doc.mode, state);\n doc.iter(pos, n, function(line) {\n processLine(cm, line.text, state);\n var save = pos == n - 1 || pos % 5 == 0 || pos >= display.viewFrom && pos < display.viewTo;\n line.stateAfter = save ? copyState(doc.mode, state) : null;\n ++pos;\n });\n if (precise) doc.frontier = pos;\n return state;\n }\n\n // POSITION MEASUREMENT\n\n function paddingTop(display) {return display.lineSpace.offsetTop;}\n function paddingVert(display) {return display.mover.offsetHeight - display.lineSpace.offsetHeight;}\n function paddingH(display) {\n if (display.cachedPaddingH) return display.cachedPaddingH;\n var e = removeChildrenAndAdd(display.measure, elt(\"pre\", \"x\"));\n var style = window.getComputedStyle ? window.getComputedStyle(e) : e.currentStyle;\n var data = {left: parseInt(style.paddingLeft), right: parseInt(style.paddingRight)};\n if (!isNaN(data.left) && !isNaN(data.right)) display.cachedPaddingH = data;\n return data;\n }\n\n function scrollGap(cm) { return scrollerGap - cm.display.nativeBarWidth; }\n function displayWidth(cm) {\n return cm.display.scroller.clientWidth - scrollGap(cm) - cm.display.barWidth;\n }\n function displayHeight(cm) {\n return cm.display.scroller.clientHeight - scrollGap(cm) - cm.display.barHeight;\n }\n\n // Ensure the lineView.wrapping.heights array is populated. This is\n // an array of bottom offsets for the lines that make up a drawn\n // line. When lineWrapping is on, there might be more than one\n // height.\n function ensureLineHeights(cm, lineView, rect) {\n var wrapping = cm.options.lineWrapping;\n var curWidth = wrapping && displayWidth(cm);\n if (!lineView.measure.heights || wrapping && lineView.measure.width != curWidth) {\n var heights = lineView.measure.heights = [];\n if (wrapping) {\n lineView.measure.width = curWidth;\n var rects = lineView.text.firstChild.getClientRects();\n for (var i = 0; i < rects.length - 1; i++) {\n var cur = rects[i], next = rects[i + 1];\n if (Math.abs(cur.bottom - next.bottom) > 2)\n heights.push((cur.bottom + next.top) / 2 - rect.top);\n }\n }\n heights.push(rect.bottom - rect.top);\n }\n }\n\n // Find a line map (mapping character offsets to text nodes) and a\n // measurement cache for the given line number. (A line view might\n // contain multiple lines when collapsed ranges are present.)\n function mapFromLineView(lineView, line, lineN) {\n if (lineView.line == line)\n return {map: lineView.measure.map, cache: lineView.measure.cache};\n for (var i = 0; i < lineView.rest.length; i++)\n if (lineView.rest[i] == line)\n return {map: lineView.measure.maps[i], cache: lineView.measure.caches[i]};\n for (var i = 0; i < lineView.rest.length; i++)\n if (lineNo(lineView.rest[i]) > lineN)\n return {map: lineView.measure.maps[i], cache: lineView.measure.caches[i], before: true};\n }\n\n // Render a line into the hidden node display.externalMeasured. Used\n // when measurement is needed for a line that's not in the viewport.\n function updateExternalMeasurement(cm, line) {\n line = visualLine(line);\n var lineN = lineNo(line);\n var view = cm.display.externalMeasured = new LineView(cm.doc, line, lineN);\n view.lineN = lineN;\n var built = view.built = buildLineContent(cm, view);\n view.text = built.pre;\n removeChildrenAndAdd(cm.display.lineMeasure, built.pre);\n return view;\n }\n\n // Get a {top, bottom, left, right} box (in line-local coordinates)\n // for a given character.\n function measureChar(cm, line, ch, bias) {\n return measureCharPrepared(cm, prepareMeasureForLine(cm, line), ch, bias);\n }\n\n // Find a line view that corresponds to the given line number.\n function findViewForLine(cm, lineN) {\n if (lineN >= cm.display.viewFrom && lineN < cm.display.viewTo)\n return cm.display.view[findViewIndex(cm, lineN)];\n var ext = cm.display.externalMeasured;\n if (ext && lineN >= ext.lineN && lineN < ext.lineN + ext.size)\n return ext;\n }\n\n // Measurement can be split in two steps, the set-up work that\n // applies to the whole line, and the measurement of the actual\n // character. Functions like coordsChar, that need to do a lot of\n // measurements in a row, can thus ensure that the set-up work is\n // only done once.\n function prepareMeasureForLine(cm, line) {\n var lineN = lineNo(line);\n var view = findViewForLine(cm, lineN);\n if (view && !view.text) {\n view = null;\n } else if (view && view.changes) {\n updateLineForChanges(cm, view, lineN, getDimensions(cm));\n cm.curOp.forceUpdate = true;\n }\n if (!view)\n view = updateExternalMeasurement(cm, line);\n\n var info = mapFromLineView(view, line, lineN);\n return {\n line: line, view: view, rect: null,\n map: info.map, cache: info.cache, before: info.before,\n hasHeights: false\n };\n }\n\n // Given a prepared measurement object, measures the position of an\n // actual character (or fetches it from the cache).\n function measureCharPrepared(cm, prepared, ch, bias, varHeight) {\n if (prepared.before) ch = -1;\n var key = ch + (bias || \"\"), found;\n if (prepared.cache.hasOwnProperty(key)) {\n found = prepared.cache[key];\n } else {\n if (!prepared.rect)\n prepared.rect = prepared.view.text.getBoundingClientRect();\n if (!prepared.hasHeights) {\n ensureLineHeights(cm, prepared.view, prepared.rect);\n prepared.hasHeights = true;\n }\n found = measureCharInner(cm, prepared, ch, bias);\n if (!found.bogus) prepared.cache[key] = found;\n }\n return {left: found.left, right: found.right,\n top: varHeight ? found.rtop : found.top,\n bottom: varHeight ? found.rbottom : found.bottom};\n }\n\n var nullRect = {left: 0, right: 0, top: 0, bottom: 0};\n\n function nodeAndOffsetInLineMap(map, ch, bias) {\n var node, start, end, collapse;\n // First, search the line map for the text node corresponding to,\n // or closest to, the target character.\n for (var i = 0; i < map.length; i += 3) {\n var mStart = map[i], mEnd = map[i + 1];\n if (ch < mStart) {\n start = 0; end = 1;\n collapse = \"left\";\n } else if (ch < mEnd) {\n start = ch - mStart;\n end = start + 1;\n } else if (i == map.length - 3 || ch == mEnd && map[i + 3] > ch) {\n end = mEnd - mStart;\n start = end - 1;\n if (ch >= mEnd) collapse = \"right\";\n }\n if (start != null) {\n node = map[i + 2];\n if (mStart == mEnd && bias == (node.insertLeft ? \"left\" : \"right\"))\n collapse = bias;\n if (bias == \"left\" && start == 0)\n while (i && map[i - 2] == map[i - 3] && map[i - 1].insertLeft) {\n node = map[(i -= 3) + 2];\n collapse = \"left\";\n }\n if (bias == \"right\" && start == mEnd - mStart)\n while (i < map.length - 3 && map[i + 3] == map[i + 4] && !map[i + 5].insertLeft) {\n node = map[(i += 3) + 2];\n collapse = \"right\";\n }\n break;\n }\n }\n return {node: node, start: start, end: end, collapse: collapse, coverStart: mStart, coverEnd: mEnd};\n }\n\n function measureCharInner(cm, prepared, ch, bias) {\n var place = nodeAndOffsetInLineMap(prepared.map, ch, bias);\n var node = place.node, start = place.start, end = place.end, collapse = place.collapse;\n\n var rect;\n if (node.nodeType == 3) { // If it is a text node, use a range to retrieve the coordinates.\n for (var i = 0; i < 4; i++) { // Retry a maximum of 4 times when nonsense rectangles are returned\n while (start && isExtendingChar(prepared.line.text.charAt(place.coverStart + start))) --start;\n while (place.coverStart + end < place.coverEnd && isExtendingChar(prepared.line.text.charAt(place.coverStart + end))) ++end;\n if (ie && ie_version < 9 && start == 0 && end == place.coverEnd - place.coverStart) {\n rect = node.parentNode.getBoundingClientRect();\n } else if (ie && cm.options.lineWrapping) {\n var rects = range(node, start, end).getClientRects();\n if (rects.length)\n rect = rects[bias == \"right\" ? rects.length - 1 : 0];\n else\n rect = nullRect;\n } else {\n rect = range(node, start, end).getBoundingClientRect() || nullRect;\n }\n if (rect.left || rect.right || start == 0) break;\n end = start;\n start = start - 1;\n collapse = \"right\";\n }\n if (ie && ie_version < 11) rect = maybeUpdateRectForZooming(cm.display.measure, rect);\n } else { // If it is a widget, simply get the box for the whole widget.\n if (start > 0) collapse = bias = \"right\";\n var rects;\n if (cm.options.lineWrapping && (rects = node.getClientRects()).length > 1)\n rect = rects[bias == \"right\" ? rects.length - 1 : 0];\n else\n rect = node.getBoundingClientRect();\n }\n if (ie && ie_version < 9 && !start && (!rect || !rect.left && !rect.right)) {\n var rSpan = node.parentNode.getClientRects()[0];\n if (rSpan)\n rect = {left: rSpan.left, right: rSpan.left + charWidth(cm.display), top: rSpan.top, bottom: rSpan.bottom};\n else\n rect = nullRect;\n }\n\n var rtop = rect.top - prepared.rect.top, rbot = rect.bottom - prepared.rect.top;\n var mid = (rtop + rbot) / 2;\n var heights = prepared.view.measure.heights;\n for (var i = 0; i < heights.length - 1; i++)\n if (mid < heights[i]) break;\n var top = i ? heights[i - 1] : 0, bot = heights[i];\n var result = {left: (collapse == \"right\" ? rect.right : rect.left) - prepared.rect.left,\n right: (collapse == \"left\" ? rect.left : rect.right) - prepared.rect.left,\n top: top, bottom: bot};\n if (!rect.left && !rect.right) result.bogus = true;\n if (!cm.options.singleCursorHeightPerLine) { result.rtop = rtop; result.rbottom = rbot; }\n\n return result;\n }\n\n // Work around problem with bounding client rects on ranges being\n // returned incorrectly when zoomed on IE10 and below.\n function maybeUpdateRectForZooming(measure, rect) {\n if (!window.screen || screen.logicalXDPI == null ||\n screen.logicalXDPI == screen.deviceXDPI || !hasBadZoomedRects(measure))\n return rect;\n var scaleX = screen.logicalXDPI / screen.deviceXDPI;\n var scaleY = screen.logicalYDPI / screen.deviceYDPI;\n return {left: rect.left * scaleX, right: rect.right * scaleX,\n top: rect.top * scaleY, bottom: rect.bottom * scaleY};\n }\n\n function clearLineMeasurementCacheFor(lineView) {\n if (lineView.measure) {\n lineView.measure.cache = {};\n lineView.measure.heights = null;\n if (lineView.rest) for (var i = 0; i < lineView.rest.length; i++)\n lineView.measure.caches[i] = {};\n }\n }\n\n function clearLineMeasurementCache(cm) {\n cm.display.externalMeasure = null;\n removeChildren(cm.display.lineMeasure);\n for (var i = 0; i < cm.display.view.length; i++)\n clearLineMeasurementCacheFor(cm.display.view[i]);\n }\n\n function clearCaches(cm) {\n clearLineMeasurementCache(cm);\n cm.display.cachedCharWidth = cm.display.cachedTextHeight = cm.display.cachedPaddingH = null;\n if (!cm.options.lineWrapping) cm.display.maxLineChanged = true;\n cm.display.lineNumChars = null;\n }\n\n function pageScrollX() { return window.pageXOffset || (document.documentElement || document.body).scrollLeft; }\n function pageScrollY() { return window.pageYOffset || (document.documentElement || document.body).scrollTop; }\n\n // Converts a {top, bottom, left, right} box from line-local\n // coordinates into another coordinate system. Context may be one of\n // \"line\", \"div\" (display.lineDiv), \"local\"/null (editor), \"window\",\n // or \"page\".\n function intoCoordSystem(cm, lineObj, rect, context) {\n if (lineObj.widgets) for (var i = 0; i < lineObj.widgets.length; ++i) if (lineObj.widgets[i].above) {\n var size = widgetHeight(lineObj.widgets[i]);\n rect.top += size; rect.bottom += size;\n }\n if (context == \"line\") return rect;\n if (!context) context = \"local\";\n var yOff = heightAtLine(lineObj);\n if (context == \"local\") yOff += paddingTop(cm.display);\n else yOff -= cm.display.viewOffset;\n if (context == \"page\" || context == \"window\") {\n var lOff = cm.display.lineSpace.getBoundingClientRect();\n yOff += lOff.top + (context == \"window\" ? 0 : pageScrollY());\n var xOff = lOff.left + (context == \"window\" ? 0 : pageScrollX());\n rect.left += xOff; rect.right += xOff;\n }\n rect.top += yOff; rect.bottom += yOff;\n return rect;\n }\n\n // Coverts a box from \"div\" coords to another coordinate system.\n // Context may be \"window\", \"page\", \"div\", or \"local\"/null.\n function fromCoordSystem(cm, coords, context) {\n if (context == \"div\") return coords;\n var left = coords.left, top = coords.top;\n // First move into \"page\" coordinate system\n if (context == \"page\") {\n left -= pageScrollX();\n top -= pageScrollY();\n } else if (context == \"local\" || !context) {\n var localBox = cm.display.sizer.getBoundingClientRect();\n left += localBox.left;\n top += localBox.top;\n }\n\n var lineSpaceBox = cm.display.lineSpace.getBoundingClientRect();\n return {left: left - lineSpaceBox.left, top: top - lineSpaceBox.top};\n }\n\n function charCoords(cm, pos, context, lineObj, bias) {\n if (!lineObj) lineObj = getLine(cm.doc, pos.line);\n return intoCoordSystem(cm, lineObj, measureChar(cm, lineObj, pos.ch, bias), context);\n }\n\n // Returns a box for a given cursor position, which may have an\n // 'other' property containing the position of the secondary cursor\n // on a bidi boundary.\n function cursorCoords(cm, pos, context, lineObj, preparedMeasure, varHeight) {\n lineObj = lineObj || getLine(cm.doc, pos.line);\n if (!preparedMeasure) preparedMeasure = prepareMeasureForLine(cm, lineObj);\n function get(ch, right) {\n var m = measureCharPrepared(cm, preparedMeasure, ch, right ? \"right\" : \"left\", varHeight);\n if (right) m.left = m.right; else m.right = m.left;\n return intoCoordSystem(cm, lineObj, m, context);\n }\n function getBidi(ch, partPos) {\n var part = order[partPos], right = part.level % 2;\n if (ch == bidiLeft(part) && partPos && part.level < order[partPos - 1].level) {\n part = order[--partPos];\n ch = bidiRight(part) - (part.level % 2 ? 0 : 1);\n right = true;\n } else if (ch == bidiRight(part) && partPos < order.length - 1 && part.level < order[partPos + 1].level) {\n part = order[++partPos];\n ch = bidiLeft(part) - part.level % 2;\n right = false;\n }\n if (right && ch == part.to && ch > part.from) return get(ch - 1);\n return get(ch, right);\n }\n var order = getOrder(lineObj), ch = pos.ch;\n if (!order) return get(ch);\n var partPos = getBidiPartAt(order, ch);\n var val = getBidi(ch, partPos);\n if (bidiOther != null) val.other = getBidi(ch, bidiOther);\n return val;\n }\n\n // Used to cheaply estimate the coordinates for a position. Used for\n // intermediate scroll updates.\n function estimateCoords(cm, pos) {\n var left = 0, pos = clipPos(cm.doc, pos);\n if (!cm.options.lineWrapping) left = charWidth(cm.display) * pos.ch;\n var lineObj = getLine(cm.doc, pos.line);\n var top = heightAtLine(lineObj) + paddingTop(cm.display);\n return {left: left, right: left, top: top, bottom: top + lineObj.height};\n }\n\n // Positions returned by coordsChar contain some extra information.\n // xRel is the relative x position of the input coordinates compared\n // to the found position (so xRel > 0 means the coordinates are to\n // the right of the character position, for example). When outside\n // is true, that means the coordinates lie outside the line's\n // vertical range.\n function PosWithInfo(line, ch, outside, xRel) {\n var pos = Pos(line, ch);\n pos.xRel = xRel;\n if (outside) pos.outside = true;\n return pos;\n }\n\n // Compute the character position closest to the given coordinates.\n // Input must be lineSpace-local (\"div\" coordinate system).\n function coordsChar(cm, x, y) {\n var doc = cm.doc;\n y += cm.display.viewOffset;\n if (y < 0) return PosWithInfo(doc.first, 0, true, -1);\n var lineN = lineAtHeight(doc, y), last = doc.first + doc.size - 1;\n if (lineN > last)\n return PosWithInfo(doc.first + doc.size - 1, getLine(doc, last).text.length, true, 1);\n if (x < 0) x = 0;\n\n var lineObj = getLine(doc, lineN);\n for (;;) {\n var found = coordsCharInner(cm, lineObj, lineN, x, y);\n var merged = collapsedSpanAtEnd(lineObj);\n var mergedPos = merged && merged.find(0, true);\n if (merged && (found.ch > mergedPos.from.ch || found.ch == mergedPos.from.ch && found.xRel > 0))\n lineN = lineNo(lineObj = mergedPos.to.line);\n else\n return found;\n }\n }\n\n function coordsCharInner(cm, lineObj, lineNo, x, y) {\n var innerOff = y - heightAtLine(lineObj);\n var wrongLine = false, adjust = 2 * cm.display.wrapper.clientWidth;\n var preparedMeasure = prepareMeasureForLine(cm, lineObj);\n\n function getX(ch) {\n var sp = cursorCoords(cm, Pos(lineNo, ch), \"line\", lineObj, preparedMeasure);\n wrongLine = true;\n if (innerOff > sp.bottom) return sp.left - adjust;\n else if (innerOff < sp.top) return sp.left + adjust;\n else wrongLine = false;\n return sp.left;\n }\n\n var bidi = getOrder(lineObj), dist = lineObj.text.length;\n var from = lineLeft(lineObj), to = lineRight(lineObj);\n var fromX = getX(from), fromOutside = wrongLine, toX = getX(to), toOutside = wrongLine;\n\n if (x > toX) return PosWithInfo(lineNo, to, toOutside, 1);\n // Do a binary search between these bounds.\n for (;;) {\n if (bidi ? to == from || to == moveVisually(lineObj, from, 1) : to - from <= 1) {\n var ch = x < fromX || x - fromX <= toX - x ? from : to;\n var xDiff = x - (ch == from ? fromX : toX);\n while (isExtendingChar(lineObj.text.charAt(ch))) ++ch;\n var pos = PosWithInfo(lineNo, ch, ch == from ? fromOutside : toOutside,\n xDiff < -1 ? -1 : xDiff > 1 ? 1 : 0);\n return pos;\n }\n var step = Math.ceil(dist / 2), middle = from + step;\n if (bidi) {\n middle = from;\n for (var i = 0; i < step; ++i) middle = moveVisually(lineObj, middle, 1);\n }\n var middleX = getX(middle);\n if (middleX > x) {to = middle; toX = middleX; if (toOutside = wrongLine) toX += 1000; dist = step;}\n else {from = middle; fromX = middleX; fromOutside = wrongLine; dist -= step;}\n }\n }\n\n var measureText;\n // Compute the default text height.\n function textHeight(display) {\n if (display.cachedTextHeight != null) return display.cachedTextHeight;\n if (measureText == null) {\n measureText = elt(\"pre\");\n // Measure a bunch of lines, for browsers that compute\n // fractional heights.\n for (var i = 0; i < 49; ++i) {\n measureText.appendChild(document.createTextNode(\"x\"));\n measureText.appendChild(elt(\"br\"));\n }\n measureText.appendChild(document.createTextNode(\"x\"));\n }\n removeChildrenAndAdd(display.measure, measureText);\n var height = measureText.offsetHeight / 50;\n if (height > 3) display.cachedTextHeight = height;\n removeChildren(display.measure);\n return height || 1;\n }\n\n // Compute the default character width.\n function charWidth(display) {\n if (display.cachedCharWidth != null) return display.cachedCharWidth;\n var anchor = elt(\"span\", \"xxxxxxxxxx\");\n var pre = elt(\"pre\", [anchor]);\n removeChildrenAndAdd(display.measure, pre);\n var rect = anchor.getBoundingClientRect(), width = (rect.right - rect.left) / 10;\n if (width > 2) display.cachedCharWidth = width;\n return width || 10;\n }\n\n // OPERATIONS\n\n // Operations are used to wrap a series of changes to the editor\n // state in such a way that each change won't have to update the\n // cursor and display (which would be awkward, slow, and\n // error-prone). Instead, display updates are batched and then all\n // combined and executed at once.\n\n var operationGroup = null;\n\n var nextOpId = 0;\n // Start a new operation.\n function startOperation(cm) {\n cm.curOp = {\n cm: cm,\n viewChanged: false, // Flag that indicates that lines might need to be redrawn\n startHeight: cm.doc.height, // Used to detect need to update scrollbar\n forceUpdate: false, // Used to force a redraw\n updateInput: null, // Whether to reset the input textarea\n typing: false, // Whether this reset should be careful to leave existing text (for compositing)\n changeObjs: null, // Accumulated changes, for firing change events\n cursorActivityHandlers: null, // Set of handlers to fire cursorActivity on\n cursorActivityCalled: 0, // Tracks which cursorActivity handlers have been called already\n selectionChanged: false, // Whether the selection needs to be redrawn\n updateMaxLine: false, // Set when the widest line needs to be determined anew\n scrollLeft: null, scrollTop: null, // Intermediate scroll position, not pushed to DOM yet\n scrollToPos: null, // Used to scroll to a specific position\n focus: false,\n id: ++nextOpId // Unique ID\n };\n if (operationGroup) {\n operationGroup.ops.push(cm.curOp);\n } else {\n cm.curOp.ownsGroup = operationGroup = {\n ops: [cm.curOp],\n delayedCallbacks: []\n };\n }\n }\n\n function fireCallbacksForOps(group) {\n // Calls delayed callbacks and cursorActivity handlers until no\n // new ones appear\n var callbacks = group.delayedCallbacks, i = 0;\n do {\n for (; i < callbacks.length; i++)\n callbacks[i].call(null);\n for (var j = 0; j < group.ops.length; j++) {\n var op = group.ops[j];\n if (op.cursorActivityHandlers)\n while (op.cursorActivityCalled < op.cursorActivityHandlers.length)\n op.cursorActivityHandlers[op.cursorActivityCalled++].call(null, op.cm);\n }\n } while (i < callbacks.length);\n }\n\n // Finish an operation, updating the display and signalling delayed events\n function endOperation(cm) {\n var op = cm.curOp, group = op.ownsGroup;\n if (!group) return;\n\n try { fireCallbacksForOps(group); }\n finally {\n operationGroup = null;\n for (var i = 0; i < group.ops.length; i++)\n group.ops[i].cm.curOp = null;\n endOperations(group);\n }\n }\n\n // The DOM updates done when an operation finishes are batched so\n // that the minimum number of relayouts are required.\n function endOperations(group) {\n var ops = group.ops;\n for (var i = 0; i < ops.length; i++) // Read DOM\n endOperation_R1(ops[i]);\n for (var i = 0; i < ops.length; i++) // Write DOM (maybe)\n endOperation_W1(ops[i]);\n for (var i = 0; i < ops.length; i++) // Read DOM\n endOperation_R2(ops[i]);\n for (var i = 0; i < ops.length; i++) // Write DOM (maybe)\n endOperation_W2(ops[i]);\n for (var i = 0; i < ops.length; i++) // Read DOM\n endOperation_finish(ops[i]);\n }\n\n function endOperation_R1(op) {\n var cm = op.cm, display = cm.display;\n maybeClipScrollbars(cm);\n if (op.updateMaxLine) findMaxLine(cm);\n\n op.mustUpdate = op.viewChanged || op.forceUpdate || op.scrollTop != null ||\n op.scrollToPos && (op.scrollToPos.from.line < display.viewFrom ||\n op.scrollToPos.to.line >= display.viewTo) ||\n display.maxLineChanged && cm.options.lineWrapping;\n op.update = op.mustUpdate &&\n new DisplayUpdate(cm, op.mustUpdate && {top: op.scrollTop, ensure: op.scrollToPos}, op.forceUpdate);\n }\n\n function endOperation_W1(op) {\n op.updatedDisplay = op.mustUpdate && updateDisplayIfNeeded(op.cm, op.update);\n }\n\n function endOperation_R2(op) {\n var cm = op.cm, display = cm.display;\n if (op.updatedDisplay) updateHeightsInViewport(cm);\n\n op.barMeasure = measureForScrollbars(cm);\n\n // If the max line changed since it was last measured, measure it,\n // and ensure the document's width matches it.\n // updateDisplay_W2 will use these properties to do the actual resizing\n if (display.maxLineChanged && !cm.options.lineWrapping) {\n op.adjustWidthTo = measureChar(cm, display.maxLine, display.maxLine.text.length).left + 3;\n cm.display.sizerWidth = op.adjustWidthTo;\n op.barMeasure.scrollWidth =\n Math.max(display.scroller.clientWidth, display.sizer.offsetLeft + op.adjustWidthTo + scrollGap(cm) + cm.display.barWidth);\n op.maxScrollLeft = Math.max(0, display.sizer.offsetLeft + op.adjustWidthTo - displayWidth(cm));\n }\n\n if (op.updatedDisplay || op.selectionChanged)\n op.preparedSelection = display.input.prepareSelection();\n }\n\n function endOperation_W2(op) {\n var cm = op.cm;\n\n if (op.adjustWidthTo != null) {\n cm.display.sizer.style.minWidth = op.adjustWidthTo + \"px\";\n if (op.maxScrollLeft < cm.doc.scrollLeft)\n setScrollLeft(cm, Math.min(cm.display.scroller.scrollLeft, op.maxScrollLeft), true);\n cm.display.maxLineChanged = false;\n }\n\n if (op.preparedSelection)\n cm.display.input.showSelection(op.preparedSelection);\n if (op.updatedDisplay || op.startHeight != cm.doc.height)\n updateScrollbars(cm, op.barMeasure);\n if (op.updatedDisplay)\n setDocumentHeight(cm, op.barMeasure);\n\n if (op.selectionChanged) restartBlink(cm);\n\n if (cm.state.focused && op.updateInput)\n cm.display.input.reset(op.typing);\n if (op.focus && op.focus == activeElt() && (!document.hasFocus || document.hasFocus()))\n ensureFocus(op.cm);\n }\n\n function endOperation_finish(op) {\n var cm = op.cm, display = cm.display, doc = cm.doc;\n\n if (op.updatedDisplay) postUpdateDisplay(cm, op.update);\n\n // Abort mouse wheel delta measurement, when scrolling explicitly\n if (display.wheelStartX != null && (op.scrollTop != null || op.scrollLeft != null || op.scrollToPos))\n display.wheelStartX = display.wheelStartY = null;\n\n // Propagate the scroll position to the actual DOM scroller\n if (op.scrollTop != null && (display.scroller.scrollTop != op.scrollTop || op.forceScroll)) {\n doc.scrollTop = Math.max(0, Math.min(display.scroller.scrollHeight - display.scroller.clientHeight, op.scrollTop));\n display.scrollbars.setScrollTop(doc.scrollTop);\n display.scroller.scrollTop = doc.scrollTop;\n }\n if (op.scrollLeft != null && (display.scroller.scrollLeft != op.scrollLeft || op.forceScroll)) {\n doc.scrollLeft = Math.max(0, Math.min(display.scroller.scrollWidth - display.scroller.clientWidth, op.scrollLeft));\n display.scrollbars.setScrollLeft(doc.scrollLeft);\n display.scroller.scrollLeft = doc.scrollLeft;\n alignHorizontally(cm);\n }\n // If we need to scroll a specific position into view, do so.\n if (op.scrollToPos) {\n var coords = scrollPosIntoView(cm, clipPos(doc, op.scrollToPos.from),\n clipPos(doc, op.scrollToPos.to), op.scrollToPos.margin);\n if (op.scrollToPos.isCursor && cm.state.focused) maybeScrollWindow(cm, coords);\n }\n\n // Fire events for markers that are hidden/unidden by editing or\n // undoing\n var hidden = op.maybeHiddenMarkers, unhidden = op.maybeUnhiddenMarkers;\n if (hidden) for (var i = 0; i < hidden.length; ++i)\n if (!hidden[i].lines.length) signal(hidden[i], \"hide\");\n if (unhidden) for (var i = 0; i < unhidden.length; ++i)\n if (unhidden[i].lines.length) signal(unhidden[i], \"unhide\");\n\n if (display.wrapper.offsetHeight)\n doc.scrollTop = cm.display.scroller.scrollTop;\n\n // Fire change events, and delayed event handlers\n if (op.changeObjs)\n signal(cm, \"changes\", cm, op.changeObjs);\n if (op.update)\n op.update.finish();\n }\n\n // Run the given function in an operation\n function runInOp(cm, f) {\n if (cm.curOp) return f();\n startOperation(cm);\n try { return f(); }\n finally { endOperation(cm); }\n }\n // Wraps a function in an operation. Returns the wrapped function.\n function operation(cm, f) {\n return function() {\n if (cm.curOp) return f.apply(cm, arguments);\n startOperation(cm);\n try { return f.apply(cm, arguments); }\n finally { endOperation(cm); }\n };\n }\n // Used to add methods to editor and doc instances, wrapping them in\n // operations.\n function methodOp(f) {\n return function() {\n if (this.curOp) return f.apply(this, arguments);\n startOperation(this);\n try { return f.apply(this, arguments); }\n finally { endOperation(this); }\n };\n }\n function docMethodOp(f) {\n return function() {\n var cm = this.cm;\n if (!cm || cm.curOp) return f.apply(this, arguments);\n startOperation(cm);\n try { return f.apply(this, arguments); }\n finally { endOperation(cm); }\n };\n }\n\n // VIEW TRACKING\n\n // These objects are used to represent the visible (currently drawn)\n // part of the document. A LineView may correspond to multiple\n // logical lines, if those are connected by collapsed ranges.\n function LineView(doc, line, lineN) {\n // The starting line\n this.line = line;\n // Continuing lines, if any\n this.rest = visualLineContinued(line);\n // Number of logical lines in this visual line\n this.size = this.rest ? lineNo(lst(this.rest)) - lineN + 1 : 1;\n this.node = this.text = null;\n this.hidden = lineIsHidden(doc, line);\n }\n\n // Create a range of LineView objects for the given lines.\n function buildViewArray(cm, from, to) {\n var array = [], nextPos;\n for (var pos = from; pos < to; pos = nextPos) {\n var view = new LineView(cm.doc, getLine(cm.doc, pos), pos);\n nextPos = pos + view.size;\n array.push(view);\n }\n return array;\n }\n\n // Updates the display.view data structure for a given change to the\n // document. From and to are in pre-change coordinates. Lendiff is\n // the amount of lines added or subtracted by the change. This is\n // used for changes that span multiple lines, or change the way\n // lines are divided into visual lines. regLineChange (below)\n // registers single-line changes.\n function regChange(cm, from, to, lendiff) {\n if (from == null) from = cm.doc.first;\n if (to == null) to = cm.doc.first + cm.doc.size;\n if (!lendiff) lendiff = 0;\n\n var display = cm.display;\n if (lendiff && to < display.viewTo &&\n (display.updateLineNumbers == null || display.updateLineNumbers > from))\n display.updateLineNumbers = from;\n\n cm.curOp.viewChanged = true;\n\n if (from >= display.viewTo) { // Change after\n if (sawCollapsedSpans && visualLineNo(cm.doc, from) < display.viewTo)\n resetView(cm);\n } else if (to <= display.viewFrom) { // Change before\n if (sawCollapsedSpans && visualLineEndNo(cm.doc, to + lendiff) > display.viewFrom) {\n resetView(cm);\n } else {\n display.viewFrom += lendiff;\n display.viewTo += lendiff;\n }\n } else if (from <= display.viewFrom && to >= display.viewTo) { // Full overlap\n resetView(cm);\n } else if (from <= display.viewFrom) { // Top overlap\n var cut = viewCuttingPoint(cm, to, to + lendiff, 1);\n if (cut) {\n display.view = display.view.slice(cut.index);\n display.viewFrom = cut.lineN;\n display.viewTo += lendiff;\n } else {\n resetView(cm);\n }\n } else if (to >= display.viewTo) { // Bottom overlap\n var cut = viewCuttingPoint(cm, from, from, -1);\n if (cut) {\n display.view = display.view.slice(0, cut.index);\n display.viewTo = cut.lineN;\n } else {\n resetView(cm);\n }\n } else { // Gap in the middle\n var cutTop = viewCuttingPoint(cm, from, from, -1);\n var cutBot = viewCuttingPoint(cm, to, to + lendiff, 1);\n if (cutTop && cutBot) {\n display.view = display.view.slice(0, cutTop.index)\n .concat(buildViewArray(cm, cutTop.lineN, cutBot.lineN))\n .concat(display.view.slice(cutBot.index));\n display.viewTo += lendiff;\n } else {\n resetView(cm);\n }\n }\n\n var ext = display.externalMeasured;\n if (ext) {\n if (to < ext.lineN)\n ext.lineN += lendiff;\n else if (from < ext.lineN + ext.size)\n display.externalMeasured = null;\n }\n }\n\n // Register a change to a single line. Type must be one of \"text\",\n // \"gutter\", \"class\", \"widget\"\n function regLineChange(cm, line, type) {\n cm.curOp.viewChanged = true;\n var display = cm.display, ext = cm.display.externalMeasured;\n if (ext && line >= ext.lineN && line < ext.lineN + ext.size)\n display.externalMeasured = null;\n\n if (line < display.viewFrom || line >= display.viewTo) return;\n var lineView = display.view[findViewIndex(cm, line)];\n if (lineView.node == null) return;\n var arr = lineView.changes || (lineView.changes = []);\n if (indexOf(arr, type) == -1) arr.push(type);\n }\n\n // Clear the view.\n function resetView(cm) {\n cm.display.viewFrom = cm.display.viewTo = cm.doc.first;\n cm.display.view = [];\n cm.display.viewOffset = 0;\n }\n\n // Find the view element corresponding to a given line. Return null\n // when the line isn't visible.\n function findViewIndex(cm, n) {\n if (n >= cm.display.viewTo) return null;\n n -= cm.display.viewFrom;\n if (n < 0) return null;\n var view = cm.display.view;\n for (var i = 0; i < view.length; i++) {\n n -= view[i].size;\n if (n < 0) return i;\n }\n }\n\n function viewCuttingPoint(cm, oldN, newN, dir) {\n var index = findViewIndex(cm, oldN), diff, view = cm.display.view;\n if (!sawCollapsedSpans || newN == cm.doc.first + cm.doc.size)\n return {index: index, lineN: newN};\n for (var i = 0, n = cm.display.viewFrom; i < index; i++)\n n += view[i].size;\n if (n != oldN) {\n if (dir > 0) {\n if (index == view.length - 1) return null;\n diff = (n + view[index].size) - oldN;\n index++;\n } else {\n diff = n - oldN;\n }\n oldN += diff; newN += diff;\n }\n while (visualLineNo(cm.doc, newN) != newN) {\n if (index == (dir < 0 ? 0 : view.length - 1)) return null;\n newN += dir * view[index - (dir < 0 ? 1 : 0)].size;\n index += dir;\n }\n return {index: index, lineN: newN};\n }\n\n // Force the view to cover a given range, adding empty view element\n // or clipping off existing ones as needed.\n function adjustView(cm, from, to) {\n var display = cm.display, view = display.view;\n if (view.length == 0 || from >= display.viewTo || to <= display.viewFrom) {\n display.view = buildViewArray(cm, from, to);\n display.viewFrom = from;\n } else {\n if (display.viewFrom > from)\n display.view = buildViewArray(cm, from, display.viewFrom).concat(display.view);\n else if (display.viewFrom < from)\n display.view = display.view.slice(findViewIndex(cm, from));\n display.viewFrom = from;\n if (display.viewTo < to)\n display.view = display.view.concat(buildViewArray(cm, display.viewTo, to));\n else if (display.viewTo > to)\n display.view = display.view.slice(0, findViewIndex(cm, to));\n }\n display.viewTo = to;\n }\n\n // Count the number of lines in the view whose DOM representation is\n // out of date (or nonexistent).\n function countDirtyView(cm) {\n var view = cm.display.view, dirty = 0;\n for (var i = 0; i < view.length; i++) {\n var lineView = view[i];\n if (!lineView.hidden && (!lineView.node || lineView.changes)) ++dirty;\n }\n return dirty;\n }\n\n // EVENT HANDLERS\n\n // Attach the necessary event handlers when initializing the editor\n function registerEventHandlers(cm) {\n var d = cm.display;\n on(d.scroller, \"mousedown\", operation(cm, onMouseDown));\n // Older IE's will not fire a second mousedown for a double click\n if (ie && ie_version < 11)\n on(d.scroller, \"dblclick\", operation(cm, function(e) {\n if (signalDOMEvent(cm, e)) return;\n var pos = posFromMouse(cm, e);\n if (!pos || clickInGutter(cm, e) || eventInWidget(cm.display, e)) return;\n e_preventDefault(e);\n var word = cm.findWordAt(pos);\n extendSelection(cm.doc, word.anchor, word.head);\n }));\n else\n on(d.scroller, \"dblclick\", function(e) { signalDOMEvent(cm, e) || e_preventDefault(e); });\n // Some browsers fire contextmenu *after* opening the menu, at\n // which point we can't mess with it anymore. Context menu is\n // handled in onMouseDown for these browsers.\n if (!captureRightClick) on(d.scroller, \"contextmenu\", function(e) {onContextMenu(cm, e);});\n\n // Used to suppress mouse event handling when a touch happens\n var touchFinished, prevTouch = {end: 0};\n function finishTouch() {\n if (d.activeTouch) {\n touchFinished = setTimeout(function() {d.activeTouch = null;}, 1000);\n prevTouch = d.activeTouch;\n prevTouch.end = +new Date;\n }\n };\n function isMouseLikeTouchEvent(e) {\n if (e.touches.length != 1) return false;\n var touch = e.touches[0];\n return touch.radiusX <= 1 && touch.radiusY <= 1;\n }\n function farAway(touch, other) {\n if (other.left == null) return true;\n var dx = other.left - touch.left, dy = other.top - touch.top;\n return dx * dx + dy * dy > 20 * 20;\n }\n on(d.scroller, \"touchstart\", function(e) {\n if (!signalDOMEvent(cm, e) && !isMouseLikeTouchEvent(e)) {\n clearTimeout(touchFinished);\n var now = +new Date;\n d.activeTouch = {start: now, moved: false,\n prev: now - prevTouch.end <= 300 ? prevTouch : null};\n if (e.touches.length == 1) {\n d.activeTouch.left = e.touches[0].pageX;\n d.activeTouch.top = e.touches[0].pageY;\n }\n }\n });\n on(d.scroller, \"touchmove\", function() {\n if (d.activeTouch) d.activeTouch.moved = true;\n });\n on(d.scroller, \"touchend\", function(e) {\n var touch = d.activeTouch;\n if (touch && !eventInWidget(d, e) && touch.left != null &&\n !touch.moved && new Date - touch.start < 300) {\n var pos = cm.coordsChar(d.activeTouch, \"page\"), range;\n if (!touch.prev || farAway(touch, touch.prev)) // Single tap\n range = new Range(pos, pos);\n else if (!touch.prev.prev || farAway(touch, touch.prev.prev)) // Double tap\n range = cm.findWordAt(pos);\n else // Triple tap\n range = new Range(Pos(pos.line, 0), clipPos(cm.doc, Pos(pos.line + 1, 0)));\n cm.setSelection(range.anchor, range.head);\n cm.focus();\n e_preventDefault(e);\n }\n finishTouch();\n });\n on(d.scroller, \"touchcancel\", finishTouch);\n\n // Sync scrolling between fake scrollbars and real scrollable\n // area, ensure viewport is updated when scrolling.\n on(d.scroller, \"scroll\", function() {\n if (d.scroller.clientHeight) {\n setScrollTop(cm, d.scroller.scrollTop);\n setScrollLeft(cm, d.scroller.scrollLeft, true);\n signal(cm, \"scroll\", cm);\n }\n });\n\n // Listen to wheel events in order to try and update the viewport on time.\n on(d.scroller, \"mousewheel\", function(e){onScrollWheel(cm, e);});\n on(d.scroller, \"DOMMouseScroll\", function(e){onScrollWheel(cm, e);});\n\n // Prevent wrapper from ever scrolling\n on(d.wrapper, \"scroll\", function() { d.wrapper.scrollTop = d.wrapper.scrollLeft = 0; });\n\n d.dragFunctions = {\n enter: function(e) {if (!signalDOMEvent(cm, e)) e_stop(e);},\n over: function(e) {if (!signalDOMEvent(cm, e)) { onDragOver(cm, e); e_stop(e); }},\n start: function(e){onDragStart(cm, e);},\n drop: operation(cm, onDrop),\n leave: function(e) {if (!signalDOMEvent(cm, e)) { clearDragCursor(cm); }}\n };\n\n var inp = d.input.getField();\n on(inp, \"keyup\", function(e) { onKeyUp.call(cm, e); });\n on(inp, \"keydown\", operation(cm, onKeyDown));\n on(inp, \"keypress\", operation(cm, onKeyPress));\n on(inp, \"focus\", bind(onFocus, cm));\n on(inp, \"blur\", bind(onBlur, cm));\n }\n\n function dragDropChanged(cm, value, old) {\n var wasOn = old && old != CodeMirror.Init;\n if (!value != !wasOn) {\n var funcs = cm.display.dragFunctions;\n var toggle = value ? on : off;\n toggle(cm.display.scroller, \"dragstart\", funcs.start);\n toggle(cm.display.scroller, \"dragenter\", funcs.enter);\n toggle(cm.display.scroller, \"dragover\", funcs.over);\n toggle(cm.display.scroller, \"dragleave\", funcs.leave);\n toggle(cm.display.scroller, \"drop\", funcs.drop);\n }\n }\n\n // Called when the window resizes\n function onResize(cm) {\n var d = cm.display;\n if (d.lastWrapHeight == d.wrapper.clientHeight && d.lastWrapWidth == d.wrapper.clientWidth)\n return;\n // Might be a text scaling operation, clear size caches.\n d.cachedCharWidth = d.cachedTextHeight = d.cachedPaddingH = null;\n d.scrollbarsClipped = false;\n cm.setSize();\n }\n\n // MOUSE EVENTS\n\n // Return true when the given mouse event happened in a widget\n function eventInWidget(display, e) {\n for (var n = e_target(e); n != display.wrapper; n = n.parentNode) {\n if (!n || (n.nodeType == 1 && n.getAttribute(\"cm-ignore-events\") == \"true\") ||\n (n.parentNode == display.sizer && n != display.mover))\n return true;\n }\n }\n\n // Given a mouse event, find the corresponding position. If liberal\n // is false, it checks whether a gutter or scrollbar was clicked,\n // and returns null if it was. forRect is used by rectangular\n // selections, and tries to estimate a character position even for\n // coordinates beyond the right of the text.\n function posFromMouse(cm, e, liberal, forRect) {\n var display = cm.display;\n if (!liberal && e_target(e).getAttribute(\"cm-not-content\") == \"true\") return null;\n\n var x, y, space = display.lineSpace.getBoundingClientRect();\n // Fails unpredictably on IE[67] when mouse is dragged around quickly.\n try { x = e.clientX - space.left; y = e.clientY - space.top; }\n catch (e) { return null; }\n var coords = coordsChar(cm, x, y), line;\n if (forRect && coords.xRel == 1 && (line = getLine(cm.doc, coords.line).text).length == coords.ch) {\n var colDiff = countColumn(line, line.length, cm.options.tabSize) - line.length;\n coords = Pos(coords.line, Math.max(0, Math.round((x - paddingH(cm.display).left) / charWidth(cm.display)) - colDiff));\n }\n return coords;\n }\n\n // A mouse down can be a single click, double click, triple click,\n // start of selection drag, start of text drag, new cursor\n // (ctrl-click), rectangle drag (alt-drag), or xwin\n // middle-click-paste. Or it might be a click on something we should\n // not interfere with, such as a scrollbar or widget.\n function onMouseDown(e) {\n var cm = this, display = cm.display;\n if (signalDOMEvent(cm, e) || display.activeTouch && display.input.supportsTouch()) return;\n display.shift = e.shiftKey;\n\n if (eventInWidget(display, e)) {\n if (!webkit) {\n // Briefly turn off draggability, to allow widgets to do\n // normal dragging things.\n display.scroller.draggable = false;\n setTimeout(function(){display.scroller.draggable = true;}, 100);\n }\n return;\n }\n if (clickInGutter(cm, e)) return;\n var start = posFromMouse(cm, e);\n window.focus();\n\n switch (e_button(e)) {\n case 1:\n // #3261: make sure, that we're not starting a second selection\n if (cm.state.selectingText)\n cm.state.selectingText(e);\n else if (start)\n leftButtonDown(cm, e, start);\n else if (e_target(e) == display.scroller)\n e_preventDefault(e);\n break;\n case 2:\n if (webkit) cm.state.lastMiddleDown = +new Date;\n if (start) extendSelection(cm.doc, start);\n setTimeout(function() {display.input.focus();}, 20);\n e_preventDefault(e);\n break;\n case 3:\n if (captureRightClick) onContextMenu(cm, e);\n else delayBlurEvent(cm);\n break;\n }\n }\n\n var lastClick, lastDoubleClick;\n function leftButtonDown(cm, e, start) {\n if (ie) setTimeout(bind(ensureFocus, cm), 0);\n else cm.curOp.focus = activeElt();\n\n var now = +new Date, type;\n if (lastDoubleClick && lastDoubleClick.time > now - 400 && cmp(lastDoubleClick.pos, start) == 0) {\n type = \"triple\";\n } else if (lastClick && lastClick.time > now - 400 && cmp(lastClick.pos, start) == 0) {\n type = \"double\";\n lastDoubleClick = {time: now, pos: start};\n } else {\n type = \"single\";\n lastClick = {time: now, pos: start};\n }\n\n var sel = cm.doc.sel, modifier = mac ? e.metaKey : e.ctrlKey, contained;\n if (cm.options.dragDrop && dragAndDrop && !cm.isReadOnly() &&\n type == \"single\" && (contained = sel.contains(start)) > -1 &&\n (cmp((contained = sel.ranges[contained]).from(), start) < 0 || start.xRel > 0) &&\n (cmp(contained.to(), start) > 0 || start.xRel < 0))\n leftButtonStartDrag(cm, e, start, modifier);\n else\n leftButtonSelect(cm, e, start, type, modifier);\n }\n\n // Start a text drag. When it ends, see if any dragging actually\n // happen, and treat as a click if it didn't.\n function leftButtonStartDrag(cm, e, start, modifier) {\n var display = cm.display, startTime = +new Date;\n var dragEnd = operation(cm, function(e2) {\n if (webkit) display.scroller.draggable = false;\n cm.state.draggingText = false;\n off(document, \"mouseup\", dragEnd);\n off(display.scroller, \"drop\", dragEnd);\n if (Math.abs(e.clientX - e2.clientX) + Math.abs(e.clientY - e2.clientY) < 10) {\n e_preventDefault(e2);\n if (!modifier && +new Date - 200 < startTime)\n extendSelection(cm.doc, start);\n // Work around unexplainable focus problem in IE9 (#2127) and Chrome (#3081)\n if (webkit || ie && ie_version == 9)\n setTimeout(function() {document.body.focus(); display.input.focus();}, 20);\n else\n display.input.focus();\n }\n });\n // Let the drag handler handle this.\n if (webkit) display.scroller.draggable = true;\n cm.state.draggingText = dragEnd;\n // IE's approach to draggable\n if (display.scroller.dragDrop) display.scroller.dragDrop();\n on(document, \"mouseup\", dragEnd);\n on(display.scroller, \"drop\", dragEnd);\n }\n\n // Normal selection, as opposed to text dragging.\n function leftButtonSelect(cm, e, start, type, addNew) {\n var display = cm.display, doc = cm.doc;\n e_preventDefault(e);\n\n var ourRange, ourIndex, startSel = doc.sel, ranges = startSel.ranges;\n if (addNew && !e.shiftKey) {\n ourIndex = doc.sel.contains(start);\n if (ourIndex > -1)\n ourRange = ranges[ourIndex];\n else\n ourRange = new Range(start, start);\n } else {\n ourRange = doc.sel.primary();\n ourIndex = doc.sel.primIndex;\n }\n\n if (e.altKey) {\n type = \"rect\";\n if (!addNew) ourRange = new Range(start, start);\n start = posFromMouse(cm, e, true, true);\n ourIndex = -1;\n } else if (type == \"double\") {\n var word = cm.findWordAt(start);\n if (cm.display.shift || doc.extend)\n ourRange = extendRange(doc, ourRange, word.anchor, word.head);\n else\n ourRange = word;\n } else if (type == \"triple\") {\n var line = new Range(Pos(start.line, 0), clipPos(doc, Pos(start.line + 1, 0)));\n if (cm.display.shift || doc.extend)\n ourRange = extendRange(doc, ourRange, line.anchor, line.head);\n else\n ourRange = line;\n } else {\n ourRange = extendRange(doc, ourRange, start);\n }\n\n if (!addNew) {\n ourIndex = 0;\n setSelection(doc, new Selection([ourRange], 0), sel_mouse);\n startSel = doc.sel;\n } else if (ourIndex == -1) {\n ourIndex = ranges.length;\n setSelection(doc, normalizeSelection(ranges.concat([ourRange]), ourIndex),\n {scroll: false, origin: \"*mouse\"});\n } else if (ranges.length > 1 && ranges[ourIndex].empty() && type == \"single\" && !e.shiftKey) {\n setSelection(doc, normalizeSelection(ranges.slice(0, ourIndex).concat(ranges.slice(ourIndex + 1)), 0),\n {scroll: false, origin: \"*mouse\"});\n startSel = doc.sel;\n } else {\n replaceOneSelection(doc, ourIndex, ourRange, sel_mouse);\n }\n\n var lastPos = start;\n function extendTo(pos) {\n if (cmp(lastPos, pos) == 0) return;\n lastPos = pos;\n\n if (type == \"rect\") {\n var ranges = [], tabSize = cm.options.tabSize;\n var startCol = countColumn(getLine(doc, start.line).text, start.ch, tabSize);\n var posCol = countColumn(getLine(doc, pos.line).text, pos.ch, tabSize);\n var left = Math.min(startCol, posCol), right = Math.max(startCol, posCol);\n for (var line = Math.min(start.line, pos.line), end = Math.min(cm.lastLine(), Math.max(start.line, pos.line));\n line <= end; line++) {\n var text = getLine(doc, line).text, leftPos = findColumn(text, left, tabSize);\n if (left == right)\n ranges.push(new Range(Pos(line, leftPos), Pos(line, leftPos)));\n else if (text.length > leftPos)\n ranges.push(new Range(Pos(line, leftPos), Pos(line, findColumn(text, right, tabSize))));\n }\n if (!ranges.length) ranges.push(new Range(start, start));\n setSelection(doc, normalizeSelection(startSel.ranges.slice(0, ourIndex).concat(ranges), ourIndex),\n {origin: \"*mouse\", scroll: false});\n cm.scrollIntoView(pos);\n } else {\n var oldRange = ourRange;\n var anchor = oldRange.anchor, head = pos;\n if (type != \"single\") {\n if (type == \"double\")\n var range = cm.findWordAt(pos);\n else\n var range = new Range(Pos(pos.line, 0), clipPos(doc, Pos(pos.line + 1, 0)));\n if (cmp(range.anchor, anchor) > 0) {\n head = range.head;\n anchor = minPos(oldRange.from(), range.anchor);\n } else {\n head = range.anchor;\n anchor = maxPos(oldRange.to(), range.head);\n }\n }\n var ranges = startSel.ranges.slice(0);\n ranges[ourIndex] = new Range(clipPos(doc, anchor), head);\n setSelection(doc, normalizeSelection(ranges, ourIndex), sel_mouse);\n }\n }\n\n var editorSize = display.wrapper.getBoundingClientRect();\n // Used to ensure timeout re-tries don't fire when another extend\n // happened in the meantime (clearTimeout isn't reliable -- at\n // least on Chrome, the timeouts still happen even when cleared,\n // if the clear happens after their scheduled firing time).\n var counter = 0;\n\n function extend(e) {\n var curCount = ++counter;\n var cur = posFromMouse(cm, e, true, type == \"rect\");\n if (!cur) return;\n if (cmp(cur, lastPos) != 0) {\n cm.curOp.focus = activeElt();\n extendTo(cur);\n var visible = visibleLines(display, doc);\n if (cur.line >= visible.to || cur.line < visible.from)\n setTimeout(operation(cm, function(){if (counter == curCount) extend(e);}), 150);\n } else {\n var outside = e.clientY < editorSize.top ? -20 : e.clientY > editorSize.bottom ? 20 : 0;\n if (outside) setTimeout(operation(cm, function() {\n if (counter != curCount) return;\n display.scroller.scrollTop += outside;\n extend(e);\n }), 50);\n }\n }\n\n function done(e) {\n cm.state.selectingText = false;\n counter = Infinity;\n e_preventDefault(e);\n display.input.focus();\n off(document, \"mousemove\", move);\n off(document, \"mouseup\", up);\n doc.history.lastSelOrigin = null;\n }\n\n var move = operation(cm, function(e) {\n if (!e_button(e)) done(e);\n else extend(e);\n });\n var up = operation(cm, done);\n cm.state.selectingText = up;\n on(document, \"mousemove\", move);\n on(document, \"mouseup\", up);\n }\n\n // Determines whether an event happened in the gutter, and fires the\n // handlers for the corresponding event.\n function gutterEvent(cm, e, type, prevent) {\n try { var mX = e.clientX, mY = e.clientY; }\n catch(e) { return false; }\n if (mX >= Math.floor(cm.display.gutters.getBoundingClientRect().right)) return false;\n if (prevent) e_preventDefault(e);\n\n var display = cm.display;\n var lineBox = display.lineDiv.getBoundingClientRect();\n\n if (mY > lineBox.bottom || !hasHandler(cm, type)) return e_defaultPrevented(e);\n mY -= lineBox.top - display.viewOffset;\n\n for (var i = 0; i < cm.options.gutters.length; ++i) {\n var g = display.gutters.childNodes[i];\n if (g && g.getBoundingClientRect().right >= mX) {\n var line = lineAtHeight(cm.doc, mY);\n var gutter = cm.options.gutters[i];\n signal(cm, type, cm, line, gutter, e);\n return e_defaultPrevented(e);\n }\n }\n }\n\n function clickInGutter(cm, e) {\n return gutterEvent(cm, e, \"gutterClick\", true);\n }\n\n // Kludge to work around strange IE behavior where it'll sometimes\n // re-fire a series of drag-related events right after the drop (#1551)\n var lastDrop = 0;\n\n function onDrop(e) {\n var cm = this;\n clearDragCursor(cm);\n if (signalDOMEvent(cm, e) || eventInWidget(cm.display, e))\n return;\n e_preventDefault(e);\n if (ie) lastDrop = +new Date;\n var pos = posFromMouse(cm, e, true), files = e.dataTransfer.files;\n if (!pos || cm.isReadOnly()) return;\n // Might be a file drop, in which case we simply extract the text\n // and insert it.\n if (files && files.length && window.FileReader && window.File) {\n var n = files.length, text = Array(n), read = 0;\n var loadFile = function(file, i) {\n if (cm.options.allowDropFileTypes &&\n indexOf(cm.options.allowDropFileTypes, file.type) == -1)\n return;\n\n var reader = new FileReader;\n reader.onload = operation(cm, function() {\n var content = reader.result;\n if (/[\\x00-\\x08\\x0e-\\x1f]{2}/.test(content)) content = \"\";\n text[i] = content;\n if (++read == n) {\n pos = clipPos(cm.doc, pos);\n var change = {from: pos, to: pos,\n text: cm.doc.splitLines(text.join(cm.doc.lineSeparator())),\n origin: \"paste\"};\n makeChange(cm.doc, change);\n setSelectionReplaceHistory(cm.doc, simpleSelection(pos, changeEnd(change)));\n }\n });\n reader.readAsText(file);\n };\n for (var i = 0; i < n; ++i) loadFile(files[i], i);\n } else { // Normal drop\n // Don't do a replace if the drop happened inside of the selected text.\n if (cm.state.draggingText && cm.doc.sel.contains(pos) > -1) {\n cm.state.draggingText(e);\n // Ensure the editor is re-focused\n setTimeout(function() {cm.display.input.focus();}, 20);\n return;\n }\n try {\n var text = e.dataTransfer.getData(\"Text\");\n if (text) {\n if (cm.state.draggingText && !(mac ? e.altKey : e.ctrlKey))\n var selected = cm.listSelections();\n setSelectionNoUndo(cm.doc, simpleSelection(pos, pos));\n if (selected) for (var i = 0; i < selected.length; ++i)\n replaceRange(cm.doc, \"\", selected[i].anchor, selected[i].head, \"drag\");\n cm.replaceSelection(text, \"around\", \"paste\");\n cm.display.input.focus();\n }\n }\n catch(e){}\n }\n }\n\n function onDragStart(cm, e) {\n if (ie && (!cm.state.draggingText || +new Date - lastDrop < 100)) { e_stop(e); return; }\n if (signalDOMEvent(cm, e) || eventInWidget(cm.display, e)) return;\n\n e.dataTransfer.setData(\"Text\", cm.getSelection());\n\n // Use dummy image instead of default browsers image.\n // Recent Safari (~6.0.2) have a tendency to segfault when this happens, so we don't do it there.\n if (e.dataTransfer.setDragImage && !safari) {\n var img = elt(\"img\", null, null, \"position: fixed; left: 0; top: 0;\");\n img.src = \"data:image/gif;base64,R0lGODlhAQABAAAAACH5BAEKAAEALAAAAAABAAEAAAICTAEAOw==\";\n if (presto) {\n img.width = img.height = 1;\n cm.display.wrapper.appendChild(img);\n // Force a relayout, or Opera won't use our image for some obscure reason\n img._top = img.offsetTop;\n }\n e.dataTransfer.setDragImage(img, 0, 0);\n if (presto) img.parentNode.removeChild(img);\n }\n }\n\n function onDragOver(cm, e) {\n var pos = posFromMouse(cm, e);\n if (!pos) return;\n var frag = document.createDocumentFragment();\n drawSelectionCursor(cm, pos, frag);\n if (!cm.display.dragCursor) {\n cm.display.dragCursor = elt(\"div\", null, \"CodeMirror-cursors CodeMirror-dragcursors\");\n cm.display.lineSpace.insertBefore(cm.display.dragCursor, cm.display.cursorDiv);\n }\n removeChildrenAndAdd(cm.display.dragCursor, frag);\n }\n\n function clearDragCursor(cm) {\n if (cm.display.dragCursor) {\n cm.display.lineSpace.removeChild(cm.display.dragCursor);\n cm.display.dragCursor = null;\n }\n }\n\n // SCROLL EVENTS\n\n // Sync the scrollable area and scrollbars, ensure the viewport\n // covers the visible area.\n function setScrollTop(cm, val) {\n if (Math.abs(cm.doc.scrollTop - val) < 2) return;\n cm.doc.scrollTop = val;\n if (!gecko) updateDisplaySimple(cm, {top: val});\n if (cm.display.scroller.scrollTop != val) cm.display.scroller.scrollTop = val;\n cm.display.scrollbars.setScrollTop(val);\n if (gecko) updateDisplaySimple(cm);\n startWorker(cm, 100);\n }\n // Sync scroller and scrollbar, ensure the gutter elements are\n // aligned.\n function setScrollLeft(cm, val, isScroller) {\n if (isScroller ? val == cm.doc.scrollLeft : Math.abs(cm.doc.scrollLeft - val) < 2) return;\n val = Math.min(val, cm.display.scroller.scrollWidth - cm.display.scroller.clientWidth);\n cm.doc.scrollLeft = val;\n alignHorizontally(cm);\n if (cm.display.scroller.scrollLeft != val) cm.display.scroller.scrollLeft = val;\n cm.display.scrollbars.setScrollLeft(val);\n }\n\n // Since the delta values reported on mouse wheel events are\n // unstandardized between browsers and even browser versions, and\n // generally horribly unpredictable, this code starts by measuring\n // the scroll effect that the first few mouse wheel events have,\n // and, from that, detects the way it can convert deltas to pixel\n // offsets afterwards.\n //\n // The reason we want to know the amount a wheel event will scroll\n // is that it gives us a chance to update the display before the\n // actual scrolling happens, reducing flickering.\n\n var wheelSamples = 0, wheelPixelsPerUnit = null;\n // Fill in a browser-detected starting value on browsers where we\n // know one. These don't have to be accurate -- the result of them\n // being wrong would just be a slight flicker on the first wheel\n // scroll (if it is large enough).\n if (ie) wheelPixelsPerUnit = -.53;\n else if (gecko) wheelPixelsPerUnit = 15;\n else if (chrome) wheelPixelsPerUnit = -.7;\n else if (safari) wheelPixelsPerUnit = -1/3;\n\n var wheelEventDelta = function(e) {\n var dx = e.wheelDeltaX, dy = e.wheelDeltaY;\n if (dx == null && e.detail && e.axis == e.HORIZONTAL_AXIS) dx = e.detail;\n if (dy == null && e.detail && e.axis == e.VERTICAL_AXIS) dy = e.detail;\n else if (dy == null) dy = e.wheelDelta;\n return {x: dx, y: dy};\n };\n CodeMirror.wheelEventPixels = function(e) {\n var delta = wheelEventDelta(e);\n delta.x *= wheelPixelsPerUnit;\n delta.y *= wheelPixelsPerUnit;\n return delta;\n };\n\n function onScrollWheel(cm, e) {\n var delta = wheelEventDelta(e), dx = delta.x, dy = delta.y;\n\n var display = cm.display, scroll = display.scroller;\n // Quit if there's nothing to scroll here\n var canScrollX = scroll.scrollWidth > scroll.clientWidth;\n var canScrollY = scroll.scrollHeight > scroll.clientHeight;\n if (!(dx && canScrollX || dy && canScrollY)) return;\n\n // Webkit browsers on OS X abort momentum scrolls when the target\n // of the scroll event is removed from the scrollable element.\n // This hack (see related code in patchDisplay) makes sure the\n // element is kept around.\n if (dy && mac && webkit) {\n outer: for (var cur = e.target, view = display.view; cur != scroll; cur = cur.parentNode) {\n for (var i = 0; i < view.length; i++) {\n if (view[i].node == cur) {\n cm.display.currentWheelTarget = cur;\n break outer;\n }\n }\n }\n }\n\n // On some browsers, horizontal scrolling will cause redraws to\n // happen before the gutter has been realigned, causing it to\n // wriggle around in a most unseemly way. When we have an\n // estimated pixels/delta value, we just handle horizontal\n // scrolling entirely here. It'll be slightly off from native, but\n // better than glitching out.\n if (dx && !gecko && !presto && wheelPixelsPerUnit != null) {\n if (dy && canScrollY)\n setScrollTop(cm, Math.max(0, Math.min(scroll.scrollTop + dy * wheelPixelsPerUnit, scroll.scrollHeight - scroll.clientHeight)));\n setScrollLeft(cm, Math.max(0, Math.min(scroll.scrollLeft + dx * wheelPixelsPerUnit, scroll.scrollWidth - scroll.clientWidth)));\n // Only prevent default scrolling if vertical scrolling is\n // actually possible. Otherwise, it causes vertical scroll\n // jitter on OSX trackpads when deltaX is small and deltaY\n // is large (issue #3579)\n if (!dy || (dy && canScrollY))\n e_preventDefault(e);\n display.wheelStartX = null; // Abort measurement, if in progress\n return;\n }\n\n // 'Project' the visible viewport to cover the area that is being\n // scrolled into view (if we know enough to estimate it).\n if (dy && wheelPixelsPerUnit != null) {\n var pixels = dy * wheelPixelsPerUnit;\n var top = cm.doc.scrollTop, bot = top + display.wrapper.clientHeight;\n if (pixels < 0) top = Math.max(0, top + pixels - 50);\n else bot = Math.min(cm.doc.height, bot + pixels + 50);\n updateDisplaySimple(cm, {top: top, bottom: bot});\n }\n\n if (wheelSamples < 20) {\n if (display.wheelStartX == null) {\n display.wheelStartX = scroll.scrollLeft; display.wheelStartY = scroll.scrollTop;\n display.wheelDX = dx; display.wheelDY = dy;\n setTimeout(function() {\n if (display.wheelStartX == null) return;\n var movedX = scroll.scrollLeft - display.wheelStartX;\n var movedY = scroll.scrollTop - display.wheelStartY;\n var sample = (movedY && display.wheelDY && movedY / display.wheelDY) ||\n (movedX && display.wheelDX && movedX / display.wheelDX);\n display.wheelStartX = display.wheelStartY = null;\n if (!sample) return;\n wheelPixelsPerUnit = (wheelPixelsPerUnit * wheelSamples + sample) / (wheelSamples + 1);\n ++wheelSamples;\n }, 200);\n } else {\n display.wheelDX += dx; display.wheelDY += dy;\n }\n }\n }\n\n // KEY EVENTS\n\n // Run a handler that was bound to a key.\n function doHandleBinding(cm, bound, dropShift) {\n if (typeof bound == \"string\") {\n bound = commands[bound];\n if (!bound) return false;\n }\n // Ensure previous input has been read, so that the handler sees a\n // consistent view of the document\n cm.display.input.ensurePolled();\n var prevShift = cm.display.shift, done = false;\n try {\n if (cm.isReadOnly()) cm.state.suppressEdits = true;\n if (dropShift) cm.display.shift = false;\n done = bound(cm) != Pass;\n } finally {\n cm.display.shift = prevShift;\n cm.state.suppressEdits = false;\n }\n return done;\n }\n\n function lookupKeyForEditor(cm, name, handle) {\n for (var i = 0; i < cm.state.keyMaps.length; i++) {\n var result = lookupKey(name, cm.state.keyMaps[i], handle, cm);\n if (result) return result;\n }\n return (cm.options.extraKeys && lookupKey(name, cm.options.extraKeys, handle, cm))\n || lookupKey(name, cm.options.keyMap, handle, cm);\n }\n\n var stopSeq = new Delayed;\n function dispatchKey(cm, name, e, handle) {\n var seq = cm.state.keySeq;\n if (seq) {\n if (isModifierKey(name)) return \"handled\";\n stopSeq.set(50, function() {\n if (cm.state.keySeq == seq) {\n cm.state.keySeq = null;\n cm.display.input.reset();\n }\n });\n name = seq + \" \" + name;\n }\n var result = lookupKeyForEditor(cm, name, handle);\n\n if (result == \"multi\")\n cm.state.keySeq = name;\n if (result == \"handled\")\n signalLater(cm, \"keyHandled\", cm, name, e);\n\n if (result == \"handled\" || result == \"multi\") {\n e_preventDefault(e);\n restartBlink(cm);\n }\n\n if (seq && !result && /\\'$/.test(name)) {\n e_preventDefault(e);\n return true;\n }\n return !!result;\n }\n\n // Handle a key from the keydown event.\n function handleKeyBinding(cm, e) {\n var name = keyName(e, true);\n if (!name) return false;\n\n if (e.shiftKey && !cm.state.keySeq) {\n // First try to resolve full name (including 'Shift-'). Failing\n // that, see if there is a cursor-motion command (starting with\n // 'go') bound to the keyname without 'Shift-'.\n return dispatchKey(cm, \"Shift-\" + name, e, function(b) {return doHandleBinding(cm, b, true);})\n || dispatchKey(cm, name, e, function(b) {\n if (typeof b == \"string\" ? /^go[A-Z]/.test(b) : b.motion)\n return doHandleBinding(cm, b);\n });\n } else {\n return dispatchKey(cm, name, e, function(b) { return doHandleBinding(cm, b); });\n }\n }\n\n // Handle a key from the keypress event\n function handleCharBinding(cm, e, ch) {\n return dispatchKey(cm, \"'\" + ch + \"'\", e,\n function(b) { return doHandleBinding(cm, b, true); });\n }\n\n var lastStoppedKey = null;\n function onKeyDown(e) {\n var cm = this;\n cm.curOp.focus = activeElt();\n if (signalDOMEvent(cm, e)) return;\n // IE does strange things with escape.\n if (ie && ie_version < 11 && e.keyCode == 27) e.returnValue = false;\n var code = e.keyCode;\n cm.display.shift = code == 16 || e.shiftKey;\n var handled = handleKeyBinding(cm, e);\n if (presto) {\n lastStoppedKey = handled ? code : null;\n // Opera has no cut event... we try to at least catch the key combo\n if (!handled && code == 88 && !hasCopyEvent && (mac ? e.metaKey : e.ctrlKey))\n cm.replaceSelection(\"\", null, \"cut\");\n }\n\n // Turn mouse into crosshair when Alt is held on Mac.\n if (code == 18 && !/\\bCodeMirror-crosshair\\b/.test(cm.display.lineDiv.className))\n showCrossHair(cm);\n }\n\n function showCrossHair(cm) {\n var lineDiv = cm.display.lineDiv;\n addClass(lineDiv, \"CodeMirror-crosshair\");\n\n function up(e) {\n if (e.keyCode == 18 || !e.altKey) {\n rmClass(lineDiv, \"CodeMirror-crosshair\");\n off(document, \"keyup\", up);\n off(document, \"mouseover\", up);\n }\n }\n on(document, \"keyup\", up);\n on(document, \"mouseover\", up);\n }\n\n function onKeyUp(e) {\n if (e.keyCode == 16) this.doc.sel.shift = false;\n signalDOMEvent(this, e);\n }\n\n function onKeyPress(e) {\n var cm = this;\n if (eventInWidget(cm.display, e) || signalDOMEvent(cm, e) || e.ctrlKey && !e.altKey || mac && e.metaKey) return;\n var keyCode = e.keyCode, charCode = e.charCode;\n if (presto && keyCode == lastStoppedKey) {lastStoppedKey = null; e_preventDefault(e); return;}\n if ((presto && (!e.which || e.which < 10)) && handleKeyBinding(cm, e)) return;\n var ch = String.fromCharCode(charCode == null ? keyCode : charCode);\n if (handleCharBinding(cm, e, ch)) return;\n cm.display.input.onKeyPress(e);\n }\n\n // FOCUS/BLUR EVENTS\n\n function delayBlurEvent(cm) {\n cm.state.delayingBlurEvent = true;\n setTimeout(function() {\n if (cm.state.delayingBlurEvent) {\n cm.state.delayingBlurEvent = false;\n onBlur(cm);\n }\n }, 100);\n }\n\n function onFocus(cm) {\n if (cm.state.delayingBlurEvent) cm.state.delayingBlurEvent = false;\n\n if (cm.options.readOnly == \"nocursor\") return;\n if (!cm.state.focused) {\n signal(cm, \"focus\", cm);\n cm.state.focused = true;\n addClass(cm.display.wrapper, \"CodeMirror-focused\");\n // This test prevents this from firing when a context\n // menu is closed (since the input reset would kill the\n // select-all detection hack)\n if (!cm.curOp && cm.display.selForContextMenu != cm.doc.sel) {\n cm.display.input.reset();\n if (webkit) setTimeout(function() { cm.display.input.reset(true); }, 20); // Issue #1730\n }\n cm.display.input.receivedFocus();\n }\n restartBlink(cm);\n }\n function onBlur(cm) {\n if (cm.state.delayingBlurEvent) return;\n\n if (cm.state.focused) {\n signal(cm, \"blur\", cm);\n cm.state.focused = false;\n rmClass(cm.display.wrapper, \"CodeMirror-focused\");\n }\n clearInterval(cm.display.blinker);\n setTimeout(function() {if (!cm.state.focused) cm.display.shift = false;}, 150);\n }\n\n // CONTEXT MENU HANDLING\n\n // To make the context menu work, we need to briefly unhide the\n // textarea (making it as unobtrusive as possible) to let the\n // right-click take effect on it.\n function onContextMenu(cm, e) {\n if (eventInWidget(cm.display, e) || contextMenuInGutter(cm, e)) return;\n if (signalDOMEvent(cm, e, \"contextmenu\")) return;\n cm.display.input.onContextMenu(e);\n }\n\n function contextMenuInGutter(cm, e) {\n if (!hasHandler(cm, \"gutterContextMenu\")) return false;\n return gutterEvent(cm, e, \"gutterContextMenu\", false);\n }\n\n // UPDATING\n\n // Compute the position of the end of a change (its 'to' property\n // refers to the pre-change end).\n var changeEnd = CodeMirror.changeEnd = function(change) {\n if (!change.text) return change.to;\n return Pos(change.from.line + change.text.length - 1,\n lst(change.text).length + (change.text.length == 1 ? change.from.ch : 0));\n };\n\n // Adjust a position to refer to the post-change position of the\n // same text, or the end of the change if the change covers it.\n function adjustForChange(pos, change) {\n if (cmp(pos, change.from) < 0) return pos;\n if (cmp(pos, change.to) <= 0) return changeEnd(change);\n\n var line = pos.line + change.text.length - (change.to.line - change.from.line) - 1, ch = pos.ch;\n if (pos.line == change.to.line) ch += changeEnd(change).ch - change.to.ch;\n return Pos(line, ch);\n }\n\n function computeSelAfterChange(doc, change) {\n var out = [];\n for (var i = 0; i < doc.sel.ranges.length; i++) {\n var range = doc.sel.ranges[i];\n out.push(new Range(adjustForChange(range.anchor, change),\n adjustForChange(range.head, change)));\n }\n return normalizeSelection(out, doc.sel.primIndex);\n }\n\n function offsetPos(pos, old, nw) {\n if (pos.line == old.line)\n return Pos(nw.line, pos.ch - old.ch + nw.ch);\n else\n return Pos(nw.line + (pos.line - old.line), pos.ch);\n }\n\n // Used by replaceSelections to allow moving the selection to the\n // start or around the replaced test. Hint may be \"start\" or \"around\".\n function computeReplacedSel(doc, changes, hint) {\n var out = [];\n var oldPrev = Pos(doc.first, 0), newPrev = oldPrev;\n for (var i = 0; i < changes.length; i++) {\n var change = changes[i];\n var from = offsetPos(change.from, oldPrev, newPrev);\n var to = offsetPos(changeEnd(change), oldPrev, newPrev);\n oldPrev = change.to;\n newPrev = to;\n if (hint == \"around\") {\n var range = doc.sel.ranges[i], inv = cmp(range.head, range.anchor) < 0;\n out[i] = new Range(inv ? to : from, inv ? from : to);\n } else {\n out[i] = new Range(from, from);\n }\n }\n return new Selection(out, doc.sel.primIndex);\n }\n\n // Allow \"beforeChange\" event handlers to influence a change\n function filterChange(doc, change, update) {\n var obj = {\n canceled: false,\n from: change.from,\n to: change.to,\n text: change.text,\n origin: change.origin,\n cancel: function() { this.canceled = true; }\n };\n if (update) obj.update = function(from, to, text, origin) {\n if (from) this.from = clipPos(doc, from);\n if (to) this.to = clipPos(doc, to);\n if (text) this.text = text;\n if (origin !== undefined) this.origin = origin;\n };\n signal(doc, \"beforeChange\", doc, obj);\n if (doc.cm) signal(doc.cm, \"beforeChange\", doc.cm, obj);\n\n if (obj.canceled) return null;\n return {from: obj.from, to: obj.to, text: obj.text, origin: obj.origin};\n }\n\n // Apply a change to a document, and add it to the document's\n // history, and propagating it to all linked documents.\n function makeChange(doc, change, ignoreReadOnly) {\n if (doc.cm) {\n if (!doc.cm.curOp) return operation(doc.cm, makeChange)(doc, change, ignoreReadOnly);\n if (doc.cm.state.suppressEdits) return;\n }\n\n if (hasHandler(doc, \"beforeChange\") || doc.cm && hasHandler(doc.cm, \"beforeChange\")) {\n change = filterChange(doc, change, true);\n if (!change) return;\n }\n\n // Possibly split or suppress the update based on the presence\n // of read-only spans in its range.\n var split = sawReadOnlySpans && !ignoreReadOnly && removeReadOnlyRanges(doc, change.from, change.to);\n if (split) {\n for (var i = split.length - 1; i >= 0; --i)\n makeChangeInner(doc, {from: split[i].from, to: split[i].to, text: i ? [\"\"] : change.text});\n } else {\n makeChangeInner(doc, change);\n }\n }\n\n function makeChangeInner(doc, change) {\n if (change.text.length == 1 && change.text[0] == \"\" && cmp(change.from, change.to) == 0) return;\n var selAfter = computeSelAfterChange(doc, change);\n addChangeToHistory(doc, change, selAfter, doc.cm ? doc.cm.curOp.id : NaN);\n\n makeChangeSingleDoc(doc, change, selAfter, stretchSpansOverChange(doc, change));\n var rebased = [];\n\n linkedDocs(doc, function(doc, sharedHist) {\n if (!sharedHist && indexOf(rebased, doc.history) == -1) {\n rebaseHist(doc.history, change);\n rebased.push(doc.history);\n }\n makeChangeSingleDoc(doc, change, null, stretchSpansOverChange(doc, change));\n });\n }\n\n // Revert a change stored in a document's history.\n function makeChangeFromHistory(doc, type, allowSelectionOnly) {\n if (doc.cm && doc.cm.state.suppressEdits) return;\n\n var hist = doc.history, event, selAfter = doc.sel;\n var source = type == \"undo\" ? hist.done : hist.undone, dest = type == \"undo\" ? hist.undone : hist.done;\n\n // Verify that there is a useable event (so that ctrl-z won't\n // needlessly clear selection events)\n for (var i = 0; i < source.length; i++) {\n event = source[i];\n if (allowSelectionOnly ? event.ranges && !event.equals(doc.sel) : !event.ranges)\n break;\n }\n if (i == source.length) return;\n hist.lastOrigin = hist.lastSelOrigin = null;\n\n for (;;) {\n event = source.pop();\n if (event.ranges) {\n pushSelectionToHistory(event, dest);\n if (allowSelectionOnly && !event.equals(doc.sel)) {\n setSelection(doc, event, {clearRedo: false});\n return;\n }\n selAfter = event;\n }\n else break;\n }\n\n // Build up a reverse change object to add to the opposite history\n // stack (redo when undoing, and vice versa).\n var antiChanges = [];\n pushSelectionToHistory(selAfter, dest);\n dest.push({changes: antiChanges, generation: hist.generation});\n hist.generation = event.generation || ++hist.maxGeneration;\n\n var filter = hasHandler(doc, \"beforeChange\") || doc.cm && hasHandler(doc.cm, \"beforeChange\");\n\n for (var i = event.changes.length - 1; i >= 0; --i) {\n var change = event.changes[i];\n change.origin = type;\n if (filter && !filterChange(doc, change, false)) {\n source.length = 0;\n return;\n }\n\n antiChanges.push(historyChangeFromChange(doc, change));\n\n var after = i ? computeSelAfterChange(doc, change) : lst(source);\n makeChangeSingleDoc(doc, change, after, mergeOldSpans(doc, change));\n if (!i && doc.cm) doc.cm.scrollIntoView({from: change.from, to: changeEnd(change)});\n var rebased = [];\n\n // Propagate to the linked documents\n linkedDocs(doc, function(doc, sharedHist) {\n if (!sharedHist && indexOf(rebased, doc.history) == -1) {\n rebaseHist(doc.history, change);\n rebased.push(doc.history);\n }\n makeChangeSingleDoc(doc, change, null, mergeOldSpans(doc, change));\n });\n }\n }\n\n // Sub-views need their line numbers shifted when text is added\n // above or below them in the parent document.\n function shiftDoc(doc, distance) {\n if (distance == 0) return;\n doc.first += distance;\n doc.sel = new Selection(map(doc.sel.ranges, function(range) {\n return new Range(Pos(range.anchor.line + distance, range.anchor.ch),\n Pos(range.head.line + distance, range.head.ch));\n }), doc.sel.primIndex);\n if (doc.cm) {\n regChange(doc.cm, doc.first, doc.first - distance, distance);\n for (var d = doc.cm.display, l = d.viewFrom; l < d.viewTo; l++)\n regLineChange(doc.cm, l, \"gutter\");\n }\n }\n\n // More lower-level change function, handling only a single document\n // (not linked ones).\n function makeChangeSingleDoc(doc, change, selAfter, spans) {\n if (doc.cm && !doc.cm.curOp)\n return operation(doc.cm, makeChangeSingleDoc)(doc, change, selAfter, spans);\n\n if (change.to.line < doc.first) {\n shiftDoc(doc, change.text.length - 1 - (change.to.line - change.from.line));\n return;\n }\n if (change.from.line > doc.lastLine()) return;\n\n // Clip the change to the size of this doc\n if (change.from.line < doc.first) {\n var shift = change.text.length - 1 - (doc.first - change.from.line);\n shiftDoc(doc, shift);\n change = {from: Pos(doc.first, 0), to: Pos(change.to.line + shift, change.to.ch),\n text: [lst(change.text)], origin: change.origin};\n }\n var last = doc.lastLine();\n if (change.to.line > last) {\n change = {from: change.from, to: Pos(last, getLine(doc, last).text.length),\n text: [change.text[0]], origin: change.origin};\n }\n\n change.removed = getBetween(doc, change.from, change.to);\n\n if (!selAfter) selAfter = computeSelAfterChange(doc, change);\n if (doc.cm) makeChangeSingleDocInEditor(doc.cm, change, spans);\n else updateDoc(doc, change, spans);\n setSelectionNoUndo(doc, selAfter, sel_dontScroll);\n }\n\n // Handle the interaction of a change to a document with the editor\n // that this document is part of.\n function makeChangeSingleDocInEditor(cm, change, spans) {\n var doc = cm.doc, display = cm.display, from = change.from, to = change.to;\n\n var recomputeMaxLength = false, checkWidthStart = from.line;\n if (!cm.options.lineWrapping) {\n checkWidthStart = lineNo(visualLine(getLine(doc, from.line)));\n doc.iter(checkWidthStart, to.line + 1, function(line) {\n if (line == display.maxLine) {\n recomputeMaxLength = true;\n return true;\n }\n });\n }\n\n if (doc.sel.contains(change.from, change.to) > -1)\n signalCursorActivity(cm);\n\n updateDoc(doc, change, spans, estimateHeight(cm));\n\n if (!cm.options.lineWrapping) {\n doc.iter(checkWidthStart, from.line + change.text.length, function(line) {\n var len = lineLength(line);\n if (len > display.maxLineLength) {\n display.maxLine = line;\n display.maxLineLength = len;\n display.maxLineChanged = true;\n recomputeMaxLength = false;\n }\n });\n if (recomputeMaxLength) cm.curOp.updateMaxLine = true;\n }\n\n // Adjust frontier, schedule worker\n doc.frontier = Math.min(doc.frontier, from.line);\n startWorker(cm, 400);\n\n var lendiff = change.text.length - (to.line - from.line) - 1;\n // Remember that these lines changed, for updating the display\n if (change.full)\n regChange(cm);\n else if (from.line == to.line && change.text.length == 1 && !isWholeLineUpdate(cm.doc, change))\n regLineChange(cm, from.line, \"text\");\n else\n regChange(cm, from.line, to.line + 1, lendiff);\n\n var changesHandler = hasHandler(cm, \"changes\"), changeHandler = hasHandler(cm, \"change\");\n if (changeHandler || changesHandler) {\n var obj = {\n from: from, to: to,\n text: change.text,\n removed: change.removed,\n origin: change.origin\n };\n if (changeHandler) signalLater(cm, \"change\", cm, obj);\n if (changesHandler) (cm.curOp.changeObjs || (cm.curOp.changeObjs = [])).push(obj);\n }\n cm.display.selForContextMenu = null;\n }\n\n function replaceRange(doc, code, from, to, origin) {\n if (!to) to = from;\n if (cmp(to, from) < 0) { var tmp = to; to = from; from = tmp; }\n if (typeof code == \"string\") code = doc.splitLines(code);\n makeChange(doc, {from: from, to: to, text: code, origin: origin});\n }\n\n // SCROLLING THINGS INTO VIEW\n\n // If an editor sits on the top or bottom of the window, partially\n // scrolled out of view, this ensures that the cursor is visible.\n function maybeScrollWindow(cm, coords) {\n if (signalDOMEvent(cm, \"scrollCursorIntoView\")) return;\n\n var display = cm.display, box = display.sizer.getBoundingClientRect(), doScroll = null;\n if (coords.top + box.top < 0) doScroll = true;\n else if (coords.bottom + box.top > (window.innerHeight || document.documentElement.clientHeight)) doScroll = false;\n if (doScroll != null && !phantom) {\n var scrollNode = elt(\"div\", \"\\u200b\", null, \"position: absolute; top: \" +\n (coords.top - display.viewOffset - paddingTop(cm.display)) + \"px; height: \" +\n (coords.bottom - coords.top + scrollGap(cm) + display.barHeight) + \"px; left: \" +\n coords.left + \"px; width: 2px;\");\n cm.display.lineSpace.appendChild(scrollNode);\n scrollNode.scrollIntoView(doScroll);\n cm.display.lineSpace.removeChild(scrollNode);\n }\n }\n\n // Scroll a given position into view (immediately), verifying that\n // it actually became visible (as line heights are accurately\n // measured, the position of something may 'drift' during drawing).\n function scrollPosIntoView(cm, pos, end, margin) {\n if (margin == null) margin = 0;\n for (var limit = 0; limit < 5; limit++) {\n var changed = false, coords = cursorCoords(cm, pos);\n var endCoords = !end || end == pos ? coords : cursorCoords(cm, end);\n var scrollPos = calculateScrollPos(cm, Math.min(coords.left, endCoords.left),\n Math.min(coords.top, endCoords.top) - margin,\n Math.max(coords.left, endCoords.left),\n Math.max(coords.bottom, endCoords.bottom) + margin);\n var startTop = cm.doc.scrollTop, startLeft = cm.doc.scrollLeft;\n if (scrollPos.scrollTop != null) {\n setScrollTop(cm, scrollPos.scrollTop);\n if (Math.abs(cm.doc.scrollTop - startTop) > 1) changed = true;\n }\n if (scrollPos.scrollLeft != null) {\n setScrollLeft(cm, scrollPos.scrollLeft);\n if (Math.abs(cm.doc.scrollLeft - startLeft) > 1) changed = true;\n }\n if (!changed) break;\n }\n return coords;\n }\n\n // Scroll a given set of coordinates into view (immediately).\n function scrollIntoView(cm, x1, y1, x2, y2) {\n var scrollPos = calculateScrollPos(cm, x1, y1, x2, y2);\n if (scrollPos.scrollTop != null) setScrollTop(cm, scrollPos.scrollTop);\n if (scrollPos.scrollLeft != null) setScrollLeft(cm, scrollPos.scrollLeft);\n }\n\n // Calculate a new scroll position needed to scroll the given\n // rectangle into view. Returns an object with scrollTop and\n // scrollLeft properties. When these are undefined, the\n // vertical/horizontal position does not need to be adjusted.\n function calculateScrollPos(cm, x1, y1, x2, y2) {\n var display = cm.display, snapMargin = textHeight(cm.display);\n if (y1 < 0) y1 = 0;\n var screentop = cm.curOp && cm.curOp.scrollTop != null ? cm.curOp.scrollTop : display.scroller.scrollTop;\n var screen = displayHeight(cm), result = {};\n if (y2 - y1 > screen) y2 = y1 + screen;\n var docBottom = cm.doc.height + paddingVert(display);\n var atTop = y1 < snapMargin, atBottom = y2 > docBottom - snapMargin;\n if (y1 < screentop) {\n result.scrollTop = atTop ? 0 : y1;\n } else if (y2 > screentop + screen) {\n var newTop = Math.min(y1, (atBottom ? docBottom : y2) - screen);\n if (newTop != screentop) result.scrollTop = newTop;\n }\n\n var screenleft = cm.curOp && cm.curOp.scrollLeft != null ? cm.curOp.scrollLeft : display.scroller.scrollLeft;\n var screenw = displayWidth(cm) - (cm.options.fixedGutter ? display.gutters.offsetWidth : 0);\n var tooWide = x2 - x1 > screenw;\n if (tooWide) x2 = x1 + screenw;\n if (x1 < 10)\n result.scrollLeft = 0;\n else if (x1 < screenleft)\n result.scrollLeft = Math.max(0, x1 - (tooWide ? 0 : 10));\n else if (x2 > screenw + screenleft - 3)\n result.scrollLeft = x2 + (tooWide ? 0 : 10) - screenw;\n return result;\n }\n\n // Store a relative adjustment to the scroll position in the current\n // operation (to be applied when the operation finishes).\n function addToScrollPos(cm, left, top) {\n if (left != null || top != null) resolveScrollToPos(cm);\n if (left != null)\n cm.curOp.scrollLeft = (cm.curOp.scrollLeft == null ? cm.doc.scrollLeft : cm.curOp.scrollLeft) + left;\n if (top != null)\n cm.curOp.scrollTop = (cm.curOp.scrollTop == null ? cm.doc.scrollTop : cm.curOp.scrollTop) + top;\n }\n\n // Make sure that at the end of the operation the current cursor is\n // shown.\n function ensureCursorVisible(cm) {\n resolveScrollToPos(cm);\n var cur = cm.getCursor(), from = cur, to = cur;\n if (!cm.options.lineWrapping) {\n from = cur.ch ? Pos(cur.line, cur.ch - 1) : cur;\n to = Pos(cur.line, cur.ch + 1);\n }\n cm.curOp.scrollToPos = {from: from, to: to, margin: cm.options.cursorScrollMargin, isCursor: true};\n }\n\n // When an operation has its scrollToPos property set, and another\n // scroll action is applied before the end of the operation, this\n // 'simulates' scrolling that position into view in a cheap way, so\n // that the effect of intermediate scroll commands is not ignored.\n function resolveScrollToPos(cm) {\n var range = cm.curOp.scrollToPos;\n if (range) {\n cm.curOp.scrollToPos = null;\n var from = estimateCoords(cm, range.from), to = estimateCoords(cm, range.to);\n var sPos = calculateScrollPos(cm, Math.min(from.left, to.left),\n Math.min(from.top, to.top) - range.margin,\n Math.max(from.right, to.right),\n Math.max(from.bottom, to.bottom) + range.margin);\n cm.scrollTo(sPos.scrollLeft, sPos.scrollTop);\n }\n }\n\n // API UTILITIES\n\n // Indent the given line. The how parameter can be \"smart\",\n // \"add\"/null, \"subtract\", or \"prev\". When aggressive is false\n // (typically set to true for forced single-line indents), empty\n // lines are not indented, and places where the mode returns Pass\n // are left alone.\n function indentLine(cm, n, how, aggressive) {\n var doc = cm.doc, state;\n if (how == null) how = \"add\";\n if (how == \"smart\") {\n // Fall back to \"prev\" when the mode doesn't have an indentation\n // method.\n if (!doc.mode.indent) how = \"prev\";\n else state = getStateBefore(cm, n);\n }\n\n var tabSize = cm.options.tabSize;\n var line = getLine(doc, n), curSpace = countColumn(line.text, null, tabSize);\n if (line.stateAfter) line.stateAfter = null;\n var curSpaceString = line.text.match(/^\\s*/)[0], indentation;\n if (!aggressive && !/\\S/.test(line.text)) {\n indentation = 0;\n how = \"not\";\n } else if (how == \"smart\") {\n indentation = doc.mode.indent(state, line.text.slice(curSpaceString.length), line.text);\n if (indentation == Pass || indentation > 150) {\n if (!aggressive) return;\n how = \"prev\";\n }\n }\n if (how == \"prev\") {\n if (n > doc.first) indentation = countColumn(getLine(doc, n-1).text, null, tabSize);\n else indentation = 0;\n } else if (how == \"add\") {\n indentation = curSpace + cm.options.indentUnit;\n } else if (how == \"subtract\") {\n indentation = curSpace - cm.options.indentUnit;\n } else if (typeof how == \"number\") {\n indentation = curSpace + how;\n }\n indentation = Math.max(0, indentation);\n\n var indentString = \"\", pos = 0;\n if (cm.options.indentWithTabs)\n for (var i = Math.floor(indentation / tabSize); i; --i) {pos += tabSize; indentString += \"\\t\";}\n if (pos < indentation) indentString += spaceStr(indentation - pos);\n\n if (indentString != curSpaceString) {\n replaceRange(doc, indentString, Pos(n, 0), Pos(n, curSpaceString.length), \"+input\");\n line.stateAfter = null;\n return true;\n } else {\n // Ensure that, if the cursor was in the whitespace at the start\n // of the line, it is moved to the end of that space.\n for (var i = 0; i < doc.sel.ranges.length; i++) {\n var range = doc.sel.ranges[i];\n if (range.head.line == n && range.head.ch < curSpaceString.length) {\n var pos = Pos(n, curSpaceString.length);\n replaceOneSelection(doc, i, new Range(pos, pos));\n break;\n }\n }\n }\n }\n\n // Utility for applying a change to a line by handle or number,\n // returning the number and optionally registering the line as\n // changed.\n function changeLine(doc, handle, changeType, op) {\n var no = handle, line = handle;\n if (typeof handle == \"number\") line = getLine(doc, clipLine(doc, handle));\n else no = lineNo(handle);\n if (no == null) return null;\n if (op(line, no) && doc.cm) regLineChange(doc.cm, no, changeType);\n return line;\n }\n\n // Helper for deleting text near the selection(s), used to implement\n // backspace, delete, and similar functionality.\n function deleteNearSelection(cm, compute) {\n var ranges = cm.doc.sel.ranges, kill = [];\n // Build up a set of ranges to kill first, merging overlapping\n // ranges.\n for (var i = 0; i < ranges.length; i++) {\n var toKill = compute(ranges[i]);\n while (kill.length && cmp(toKill.from, lst(kill).to) <= 0) {\n var replaced = kill.pop();\n if (cmp(replaced.from, toKill.from) < 0) {\n toKill.from = replaced.from;\n break;\n }\n }\n kill.push(toKill);\n }\n // Next, remove those actual ranges.\n runInOp(cm, function() {\n for (var i = kill.length - 1; i >= 0; i--)\n replaceRange(cm.doc, \"\", kill[i].from, kill[i].to, \"+delete\");\n ensureCursorVisible(cm);\n });\n }\n\n // Used for horizontal relative motion. Dir is -1 or 1 (left or\n // right), unit can be \"char\", \"column\" (like char, but doesn't\n // cross line boundaries), \"word\" (across next word), or \"group\" (to\n // the start of next group of word or non-word-non-whitespace\n // chars). The visually param controls whether, in right-to-left\n // text, direction 1 means to move towards the next index in the\n // string, or towards the character to the right of the current\n // position. The resulting position will have a hitSide=true\n // property if it reached the end of the document.\n function findPosH(doc, pos, dir, unit, visually) {\n var line = pos.line, ch = pos.ch, origDir = dir;\n var lineObj = getLine(doc, line);\n function findNextLine() {\n var l = line + dir;\n if (l < doc.first || l >= doc.first + doc.size) return false\n line = l;\n return lineObj = getLine(doc, l);\n }\n function moveOnce(boundToLine) {\n var next = (visually ? moveVisually : moveLogically)(lineObj, ch, dir, true);\n if (next == null) {\n if (!boundToLine && findNextLine()) {\n if (visually) ch = (dir < 0 ? lineRight : lineLeft)(lineObj);\n else ch = dir < 0 ? lineObj.text.length : 0;\n } else return false\n } else ch = next;\n return true;\n }\n\n if (unit == \"char\") {\n moveOnce()\n } else if (unit == \"column\") {\n moveOnce(true)\n } else if (unit == \"word\" || unit == \"group\") {\n var sawType = null, group = unit == \"group\";\n var helper = doc.cm && doc.cm.getHelper(pos, \"wordChars\");\n for (var first = true;; first = false) {\n if (dir < 0 && !moveOnce(!first)) break;\n var cur = lineObj.text.charAt(ch) || \"\\n\";\n var type = isWordChar(cur, helper) ? \"w\"\n : group && cur == \"\\n\" ? \"n\"\n : !group || /\\s/.test(cur) ? null\n : \"p\";\n if (group && !first && !type) type = \"s\";\n if (sawType && sawType != type) {\n if (dir < 0) {dir = 1; moveOnce();}\n break;\n }\n\n if (type) sawType = type;\n if (dir > 0 && !moveOnce(!first)) break;\n }\n }\n var result = skipAtomic(doc, Pos(line, ch), pos, origDir, true);\n if (!cmp(pos, result)) result.hitSide = true;\n return result;\n }\n\n // For relative vertical movement. Dir may be -1 or 1. Unit can be\n // \"page\" or \"line\". The resulting position will have a hitSide=true\n // property if it reached the end of the document.\n function findPosV(cm, pos, dir, unit) {\n var doc = cm.doc, x = pos.left, y;\n if (unit == \"page\") {\n var pageSize = Math.min(cm.display.wrapper.clientHeight, window.innerHeight || document.documentElement.clientHeight);\n y = pos.top + dir * (pageSize - (dir < 0 ? 1.5 : .5) * textHeight(cm.display));\n } else if (unit == \"line\") {\n y = dir > 0 ? pos.bottom + 3 : pos.top - 3;\n }\n for (;;) {\n var target = coordsChar(cm, x, y);\n if (!target.outside) break;\n if (dir < 0 ? y <= 0 : y >= doc.height) { target.hitSide = true; break; }\n y += dir * 5;\n }\n return target;\n }\n\n // EDITOR METHODS\n\n // The publicly visible API. Note that methodOp(f) means\n // 'wrap f in an operation, performed on its `this` parameter'.\n\n // This is not the complete set of editor methods. Most of the\n // methods defined on the Doc type are also injected into\n // CodeMirror.prototype, for backwards compatibility and\n // convenience.\n\n CodeMirror.prototype = {\n constructor: CodeMirror,\n focus: function(){window.focus(); this.display.input.focus();},\n\n setOption: function(option, value) {\n var options = this.options, old = options[option];\n if (options[option] == value && option != \"mode\") return;\n options[option] = value;\n if (optionHandlers.hasOwnProperty(option))\n operation(this, optionHandlers[option])(this, value, old);\n },\n\n getOption: function(option) {return this.options[option];},\n getDoc: function() {return this.doc;},\n\n addKeyMap: function(map, bottom) {\n this.state.keyMaps[bottom ? \"push\" : \"unshift\"](getKeyMap(map));\n },\n removeKeyMap: function(map) {\n var maps = this.state.keyMaps;\n for (var i = 0; i < maps.length; ++i)\n if (maps[i] == map || maps[i].name == map) {\n maps.splice(i, 1);\n return true;\n }\n },\n\n addOverlay: methodOp(function(spec, options) {\n var mode = spec.token ? spec : CodeMirror.getMode(this.options, spec);\n if (mode.startState) throw new Error(\"Overlays may not be stateful.\");\n this.state.overlays.push({mode: mode, modeSpec: spec, opaque: options && options.opaque});\n this.state.modeGen++;\n regChange(this);\n }),\n removeOverlay: methodOp(function(spec) {\n var overlays = this.state.overlays;\n for (var i = 0; i < overlays.length; ++i) {\n var cur = overlays[i].modeSpec;\n if (cur == spec || typeof spec == \"string\" && cur.name == spec) {\n overlays.splice(i, 1);\n this.state.modeGen++;\n regChange(this);\n return;\n }\n }\n }),\n\n indentLine: methodOp(function(n, dir, aggressive) {\n if (typeof dir != \"string\" && typeof dir != \"number\") {\n if (dir == null) dir = this.options.smartIndent ? \"smart\" : \"prev\";\n else dir = dir ? \"add\" : \"subtract\";\n }\n if (isLine(this.doc, n)) indentLine(this, n, dir, aggressive);\n }),\n indentSelection: methodOp(function(how) {\n var ranges = this.doc.sel.ranges, end = -1;\n for (var i = 0; i < ranges.length; i++) {\n var range = ranges[i];\n if (!range.empty()) {\n var from = range.from(), to = range.to();\n var start = Math.max(end, from.line);\n end = Math.min(this.lastLine(), to.line - (to.ch ? 0 : 1)) + 1;\n for (var j = start; j < end; ++j)\n indentLine(this, j, how);\n var newRanges = this.doc.sel.ranges;\n if (from.ch == 0 && ranges.length == newRanges.length && newRanges[i].from().ch > 0)\n replaceOneSelection(this.doc, i, new Range(from, newRanges[i].to()), sel_dontScroll);\n } else if (range.head.line > end) {\n indentLine(this, range.head.line, how, true);\n end = range.head.line;\n if (i == this.doc.sel.primIndex) ensureCursorVisible(this);\n }\n }\n }),\n\n // Fetch the parser token for a given character. Useful for hacks\n // that want to inspect the mode state (say, for completion).\n getTokenAt: function(pos, precise) {\n return takeToken(this, pos, precise);\n },\n\n getLineTokens: function(line, precise) {\n return takeToken(this, Pos(line), precise, true);\n },\n\n getTokenTypeAt: function(pos) {\n pos = clipPos(this.doc, pos);\n var styles = getLineStyles(this, getLine(this.doc, pos.line));\n var before = 0, after = (styles.length - 1) / 2, ch = pos.ch;\n var type;\n if (ch == 0) type = styles[2];\n else for (;;) {\n var mid = (before + after) >> 1;\n if ((mid ? styles[mid * 2 - 1] : 0) >= ch) after = mid;\n else if (styles[mid * 2 + 1] < ch) before = mid + 1;\n else { type = styles[mid * 2 + 2]; break; }\n }\n var cut = type ? type.indexOf(\"cm-overlay \") : -1;\n return cut < 0 ? type : cut == 0 ? null : type.slice(0, cut - 1);\n },\n\n getModeAt: function(pos) {\n var mode = this.doc.mode;\n if (!mode.innerMode) return mode;\n return CodeMirror.innerMode(mode, this.getTokenAt(pos).state).mode;\n },\n\n getHelper: function(pos, type) {\n return this.getHelpers(pos, type)[0];\n },\n\n getHelpers: function(pos, type) {\n var found = [];\n if (!helpers.hasOwnProperty(type)) return found;\n var help = helpers[type], mode = this.getModeAt(pos);\n if (typeof mode[type] == \"string\") {\n if (help[mode[type]]) found.push(help[mode[type]]);\n } else if (mode[type]) {\n for (var i = 0; i < mode[type].length; i++) {\n var val = help[mode[type][i]];\n if (val) found.push(val);\n }\n } else if (mode.helperType && help[mode.helperType]) {\n found.push(help[mode.helperType]);\n } else if (help[mode.name]) {\n found.push(help[mode.name]);\n }\n for (var i = 0; i < help._global.length; i++) {\n var cur = help._global[i];\n if (cur.pred(mode, this) && indexOf(found, cur.val) == -1)\n found.push(cur.val);\n }\n return found;\n },\n\n getStateAfter: function(line, precise) {\n var doc = this.doc;\n line = clipLine(doc, line == null ? doc.first + doc.size - 1: line);\n return getStateBefore(this, line + 1, precise);\n },\n\n cursorCoords: function(start, mode) {\n var pos, range = this.doc.sel.primary();\n if (start == null) pos = range.head;\n else if (typeof start == \"object\") pos = clipPos(this.doc, start);\n else pos = start ? range.from() : range.to();\n return cursorCoords(this, pos, mode || \"page\");\n },\n\n charCoords: function(pos, mode) {\n return charCoords(this, clipPos(this.doc, pos), mode || \"page\");\n },\n\n coordsChar: function(coords, mode) {\n coords = fromCoordSystem(this, coords, mode || \"page\");\n return coordsChar(this, coords.left, coords.top);\n },\n\n lineAtHeight: function(height, mode) {\n height = fromCoordSystem(this, {top: height, left: 0}, mode || \"page\").top;\n return lineAtHeight(this.doc, height + this.display.viewOffset);\n },\n heightAtLine: function(line, mode) {\n var end = false, lineObj;\n if (typeof line == \"number\") {\n var last = this.doc.first + this.doc.size - 1;\n if (line < this.doc.first) line = this.doc.first;\n else if (line > last) { line = last; end = true; }\n lineObj = getLine(this.doc, line);\n } else {\n lineObj = line;\n }\n return intoCoordSystem(this, lineObj, {top: 0, left: 0}, mode || \"page\").top +\n (end ? this.doc.height - heightAtLine(lineObj) : 0);\n },\n\n defaultTextHeight: function() { return textHeight(this.display); },\n defaultCharWidth: function() { return charWidth(this.display); },\n\n setGutterMarker: methodOp(function(line, gutterID, value) {\n return changeLine(this.doc, line, \"gutter\", function(line) {\n var markers = line.gutterMarkers || (line.gutterMarkers = {});\n markers[gutterID] = value;\n if (!value && isEmpty(markers)) line.gutterMarkers = null;\n return true;\n });\n }),\n\n clearGutter: methodOp(function(gutterID) {\n var cm = this, doc = cm.doc, i = doc.first;\n doc.iter(function(line) {\n if (line.gutterMarkers && line.gutterMarkers[gutterID]) {\n line.gutterMarkers[gutterID] = null;\n regLineChange(cm, i, \"gutter\");\n if (isEmpty(line.gutterMarkers)) line.gutterMarkers = null;\n }\n ++i;\n });\n }),\n\n lineInfo: function(line) {\n if (typeof line == \"number\") {\n if (!isLine(this.doc, line)) return null;\n var n = line;\n line = getLine(this.doc, line);\n if (!line) return null;\n } else {\n var n = lineNo(line);\n if (n == null) return null;\n }\n return {line: n, handle: line, text: line.text, gutterMarkers: line.gutterMarkers,\n textClass: line.textClass, bgClass: line.bgClass, wrapClass: line.wrapClass,\n widgets: line.widgets};\n },\n\n getViewport: function() { return {from: this.display.viewFrom, to: this.display.viewTo};},\n\n addWidget: function(pos, node, scroll, vert, horiz) {\n var display = this.display;\n pos = cursorCoords(this, clipPos(this.doc, pos));\n var top = pos.bottom, left = pos.left;\n node.style.position = \"absolute\";\n node.setAttribute(\"cm-ignore-events\", \"true\");\n this.display.input.setUneditable(node);\n display.sizer.appendChild(node);\n if (vert == \"over\") {\n top = pos.top;\n } else if (vert == \"above\" || vert == \"near\") {\n var vspace = Math.max(display.wrapper.clientHeight, this.doc.height),\n hspace = Math.max(display.sizer.clientWidth, display.lineSpace.clientWidth);\n // Default to positioning above (if specified and possible); otherwise default to positioning below\n if ((vert == 'above' || pos.bottom + node.offsetHeight > vspace) && pos.top > node.offsetHeight)\n top = pos.top - node.offsetHeight;\n else if (pos.bottom + node.offsetHeight <= vspace)\n top = pos.bottom;\n if (left + node.offsetWidth > hspace)\n left = hspace - node.offsetWidth;\n }\n node.style.top = top + \"px\";\n node.style.left = node.style.right = \"\";\n if (horiz == \"right\") {\n left = display.sizer.clientWidth - node.offsetWidth;\n node.style.right = \"0px\";\n } else {\n if (horiz == \"left\") left = 0;\n else if (horiz == \"middle\") left = (display.sizer.clientWidth - node.offsetWidth) / 2;\n node.style.left = left + \"px\";\n }\n if (scroll)\n scrollIntoView(this, left, top, left + node.offsetWidth, top + node.offsetHeight);\n },\n\n triggerOnKeyDown: methodOp(onKeyDown),\n triggerOnKeyPress: methodOp(onKeyPress),\n triggerOnKeyUp: onKeyUp,\n\n execCommand: function(cmd) {\n if (commands.hasOwnProperty(cmd))\n return commands[cmd].call(null, this);\n },\n\n triggerElectric: methodOp(function(text) { triggerElectric(this, text); }),\n\n findPosH: function(from, amount, unit, visually) {\n var dir = 1;\n if (amount < 0) { dir = -1; amount = -amount; }\n for (var i = 0, cur = clipPos(this.doc, from); i < amount; ++i) {\n cur = findPosH(this.doc, cur, dir, unit, visually);\n if (cur.hitSide) break;\n }\n return cur;\n },\n\n moveH: methodOp(function(dir, unit) {\n var cm = this;\n cm.extendSelectionsBy(function(range) {\n if (cm.display.shift || cm.doc.extend || range.empty())\n return findPosH(cm.doc, range.head, dir, unit, cm.options.rtlMoveVisually);\n else\n return dir < 0 ? range.from() : range.to();\n }, sel_move);\n }),\n\n deleteH: methodOp(function(dir, unit) {\n var sel = this.doc.sel, doc = this.doc;\n if (sel.somethingSelected())\n doc.replaceSelection(\"\", null, \"+delete\");\n else\n deleteNearSelection(this, function(range) {\n var other = findPosH(doc, range.head, dir, unit, false);\n return dir < 0 ? {from: other, to: range.head} : {from: range.head, to: other};\n });\n }),\n\n findPosV: function(from, amount, unit, goalColumn) {\n var dir = 1, x = goalColumn;\n if (amount < 0) { dir = -1; amount = -amount; }\n for (var i = 0, cur = clipPos(this.doc, from); i < amount; ++i) {\n var coords = cursorCoords(this, cur, \"div\");\n if (x == null) x = coords.left;\n else coords.left = x;\n cur = findPosV(this, coords, dir, unit);\n if (cur.hitSide) break;\n }\n return cur;\n },\n\n moveV: methodOp(function(dir, unit) {\n var cm = this, doc = this.doc, goals = [];\n var collapse = !cm.display.shift && !doc.extend && doc.sel.somethingSelected();\n doc.extendSelectionsBy(function(range) {\n if (collapse)\n return dir < 0 ? range.from() : range.to();\n var headPos = cursorCoords(cm, range.head, \"div\");\n if (range.goalColumn != null) headPos.left = range.goalColumn;\n goals.push(headPos.left);\n var pos = findPosV(cm, headPos, dir, unit);\n if (unit == \"page\" && range == doc.sel.primary())\n addToScrollPos(cm, null, charCoords(cm, pos, \"div\").top - headPos.top);\n return pos;\n }, sel_move);\n if (goals.length) for (var i = 0; i < doc.sel.ranges.length; i++)\n doc.sel.ranges[i].goalColumn = goals[i];\n }),\n\n // Find the word at the given position (as returned by coordsChar).\n findWordAt: function(pos) {\n var doc = this.doc, line = getLine(doc, pos.line).text;\n var start = pos.ch, end = pos.ch;\n if (line) {\n var helper = this.getHelper(pos, \"wordChars\");\n if ((pos.xRel < 0 || end == line.length) && start) --start; else ++end;\n var startChar = line.charAt(start);\n var check = isWordChar(startChar, helper)\n ? function(ch) { return isWordChar(ch, helper); }\n : /\\s/.test(startChar) ? function(ch) {return /\\s/.test(ch);}\n : function(ch) {return !/\\s/.test(ch) && !isWordChar(ch);};\n while (start > 0 && check(line.charAt(start - 1))) --start;\n while (end < line.length && check(line.charAt(end))) ++end;\n }\n return new Range(Pos(pos.line, start), Pos(pos.line, end));\n },\n\n toggleOverwrite: function(value) {\n if (value != null && value == this.state.overwrite) return;\n if (this.state.overwrite = !this.state.overwrite)\n addClass(this.display.cursorDiv, \"CodeMirror-overwrite\");\n else\n rmClass(this.display.cursorDiv, \"CodeMirror-overwrite\");\n\n signal(this, \"overwriteToggle\", this, this.state.overwrite);\n },\n hasFocus: function() { return this.display.input.getField() == activeElt(); },\n isReadOnly: function() { return !!(this.options.readOnly || this.doc.cantEdit); },\n\n scrollTo: methodOp(function(x, y) {\n if (x != null || y != null) resolveScrollToPos(this);\n if (x != null) this.curOp.scrollLeft = x;\n if (y != null) this.curOp.scrollTop = y;\n }),\n getScrollInfo: function() {\n var scroller = this.display.scroller;\n return {left: scroller.scrollLeft, top: scroller.scrollTop,\n height: scroller.scrollHeight - scrollGap(this) - this.display.barHeight,\n width: scroller.scrollWidth - scrollGap(this) - this.display.barWidth,\n clientHeight: displayHeight(this), clientWidth: displayWidth(this)};\n },\n\n scrollIntoView: methodOp(function(range, margin) {\n if (range == null) {\n range = {from: this.doc.sel.primary().head, to: null};\n if (margin == null) margin = this.options.cursorScrollMargin;\n } else if (typeof range == \"number\") {\n range = {from: Pos(range, 0), to: null};\n } else if (range.from == null) {\n range = {from: range, to: null};\n }\n if (!range.to) range.to = range.from;\n range.margin = margin || 0;\n\n if (range.from.line != null) {\n resolveScrollToPos(this);\n this.curOp.scrollToPos = range;\n } else {\n var sPos = calculateScrollPos(this, Math.min(range.from.left, range.to.left),\n Math.min(range.from.top, range.to.top) - range.margin,\n Math.max(range.from.right, range.to.right),\n Math.max(range.from.bottom, range.to.bottom) + range.margin);\n this.scrollTo(sPos.scrollLeft, sPos.scrollTop);\n }\n }),\n\n setSize: methodOp(function(width, height) {\n var cm = this;\n function interpret(val) {\n return typeof val == \"number\" || /^\\d+$/.test(String(val)) ? val + \"px\" : val;\n }\n if (width != null) cm.display.wrapper.style.width = interpret(width);\n if (height != null) cm.display.wrapper.style.height = interpret(height);\n if (cm.options.lineWrapping) clearLineMeasurementCache(this);\n var lineNo = cm.display.viewFrom;\n cm.doc.iter(lineNo, cm.display.viewTo, function(line) {\n if (line.widgets) for (var i = 0; i < line.widgets.length; i++)\n if (line.widgets[i].noHScroll) { regLineChange(cm, lineNo, \"widget\"); break; }\n ++lineNo;\n });\n cm.curOp.forceUpdate = true;\n signal(cm, \"refresh\", this);\n }),\n\n operation: function(f){return runInOp(this, f);},\n\n refresh: methodOp(function() {\n var oldHeight = this.display.cachedTextHeight;\n regChange(this);\n this.curOp.forceUpdate = true;\n clearCaches(this);\n this.scrollTo(this.doc.scrollLeft, this.doc.scrollTop);\n updateGutterSpace(this);\n if (oldHeight == null || Math.abs(oldHeight - textHeight(this.display)) > .5)\n estimateLineHeights(this);\n signal(this, \"refresh\", this);\n }),\n\n swapDoc: methodOp(function(doc) {\n var old = this.doc;\n old.cm = null;\n attachDoc(this, doc);\n clearCaches(this);\n this.display.input.reset();\n this.scrollTo(doc.scrollLeft, doc.scrollTop);\n this.curOp.forceScroll = true;\n signalLater(this, \"swapDoc\", this, old);\n return old;\n }),\n\n getInputField: function(){return this.display.input.getField();},\n getWrapperElement: function(){return this.display.wrapper;},\n getScrollerElement: function(){return this.display.scroller;},\n getGutterElement: function(){return this.display.gutters;}\n };\n eventMixin(CodeMirror);\n\n // OPTION DEFAULTS\n\n // The default configuration options.\n var defaults = CodeMirror.defaults = {};\n // Functions to run when options are changed.\n var optionHandlers = CodeMirror.optionHandlers = {};\n\n function option(name, deflt, handle, notOnInit) {\n CodeMirror.defaults[name] = deflt;\n if (handle) optionHandlers[name] =\n notOnInit ? function(cm, val, old) {if (old != Init) handle(cm, val, old);} : handle;\n }\n\n // Passed to option handlers when there is no old value.\n var Init = CodeMirror.Init = {toString: function(){return \"CodeMirror.Init\";}};\n\n // These two are, on init, called from the constructor because they\n // have to be initialized before the editor can start at all.\n option(\"value\", \"\", function(cm, val) {\n cm.setValue(val);\n }, true);\n option(\"mode\", null, function(cm, val) {\n cm.doc.modeOption = val;\n loadMode(cm);\n }, true);\n\n option(\"indentUnit\", 2, loadMode, true);\n option(\"indentWithTabs\", false);\n option(\"smartIndent\", true);\n option(\"tabSize\", 4, function(cm) {\n resetModeState(cm);\n clearCaches(cm);\n regChange(cm);\n }, true);\n option(\"lineSeparator\", null, function(cm, val) {\n cm.doc.lineSep = val;\n if (!val) return;\n var newBreaks = [], lineNo = cm.doc.first;\n cm.doc.iter(function(line) {\n for (var pos = 0;;) {\n var found = line.text.indexOf(val, pos);\n if (found == -1) break;\n pos = found + val.length;\n newBreaks.push(Pos(lineNo, found));\n }\n lineNo++;\n });\n for (var i = newBreaks.length - 1; i >= 0; i--)\n replaceRange(cm.doc, val, newBreaks[i], Pos(newBreaks[i].line, newBreaks[i].ch + val.length))\n });\n option(\"specialChars\", /[\\t\\u0000-\\u0019\\u00ad\\u200b-\\u200f\\u2028\\u2029\\ufeff]/g, function(cm, val, old) {\n cm.state.specialChars = new RegExp(val.source + (val.test(\"\\t\") ? \"\" : \"|\\t\"), \"g\");\n if (old != CodeMirror.Init) cm.refresh();\n });\n option(\"specialCharPlaceholder\", defaultSpecialCharPlaceholder, function(cm) {cm.refresh();}, true);\n option(\"electricChars\", true);\n option(\"inputStyle\", mobile ? \"contenteditable\" : \"textarea\", function() {\n throw new Error(\"inputStyle can not (yet) be changed in a running editor\"); // FIXME\n }, true);\n option(\"rtlMoveVisually\", !windows);\n option(\"wholeLineUpdateBefore\", true);\n\n option(\"theme\", \"default\", function(cm) {\n themeChanged(cm);\n guttersChanged(cm);\n }, true);\n option(\"keyMap\", \"default\", function(cm, val, old) {\n var next = getKeyMap(val);\n var prev = old != CodeMirror.Init && getKeyMap(old);\n if (prev && prev.detach) prev.detach(cm, next);\n if (next.attach) next.attach(cm, prev || null);\n });\n option(\"extraKeys\", null);\n\n option(\"lineWrapping\", false, wrappingChanged, true);\n option(\"gutters\", [], function(cm) {\n setGuttersForLineNumbers(cm.options);\n guttersChanged(cm);\n }, true);\n option(\"fixedGutter\", true, function(cm, val) {\n cm.display.gutters.style.left = val ? compensateForHScroll(cm.display) + \"px\" : \"0\";\n cm.refresh();\n }, true);\n option(\"coverGutterNextToScrollbar\", false, function(cm) {updateScrollbars(cm);}, true);\n option(\"scrollbarStyle\", \"native\", function(cm) {\n initScrollbars(cm);\n updateScrollbars(cm);\n cm.display.scrollbars.setScrollTop(cm.doc.scrollTop);\n cm.display.scrollbars.setScrollLeft(cm.doc.scrollLeft);\n }, true);\n option(\"lineNumbers\", false, function(cm) {\n setGuttersForLineNumbers(cm.options);\n guttersChanged(cm);\n }, true);\n option(\"firstLineNumber\", 1, guttersChanged, true);\n option(\"lineNumberFormatter\", function(integer) {return integer;}, guttersChanged, true);\n option(\"showCursorWhenSelecting\", false, updateSelection, true);\n\n option(\"resetSelectionOnContextMenu\", true);\n option(\"lineWiseCopyCut\", true);\n\n option(\"readOnly\", false, function(cm, val) {\n if (val == \"nocursor\") {\n onBlur(cm);\n cm.display.input.blur();\n cm.display.disabled = true;\n } else {\n cm.display.disabled = false;\n }\n cm.display.input.readOnlyChanged(val)\n });\n option(\"disableInput\", false, function(cm, val) {if (!val) cm.display.input.reset();}, true);\n option(\"dragDrop\", true, dragDropChanged);\n option(\"allowDropFileTypes\", null);\n\n option(\"cursorBlinkRate\", 530);\n option(\"cursorScrollMargin\", 0);\n option(\"cursorHeight\", 1, updateSelection, true);\n option(\"singleCursorHeightPerLine\", true, updateSelection, true);\n option(\"workTime\", 100);\n option(\"workDelay\", 100);\n option(\"flattenSpans\", true, resetModeState, true);\n option(\"addModeClass\", false, resetModeState, true);\n option(\"pollInterval\", 100);\n option(\"undoDepth\", 200, function(cm, val){cm.doc.history.undoDepth = val;});\n option(\"historyEventDelay\", 1250);\n option(\"viewportMargin\", 10, function(cm){cm.refresh();}, true);\n option(\"maxHighlightLength\", 10000, resetModeState, true);\n option(\"moveInputWithCursor\", true, function(cm, val) {\n if (!val) cm.display.input.resetPosition();\n });\n\n option(\"tabindex\", null, function(cm, val) {\n cm.display.input.getField().tabIndex = val || \"\";\n });\n option(\"autofocus\", null);\n\n // MODE DEFINITION AND QUERYING\n\n // Known modes, by name and by MIME\n var modes = CodeMirror.modes = {}, mimeModes = CodeMirror.mimeModes = {};\n\n // Extra arguments are stored as the mode's dependencies, which is\n // used by (legacy) mechanisms like loadmode.js to automatically\n // load a mode. (Preferred mechanism is the require/define calls.)\n CodeMirror.defineMode = function(name, mode) {\n if (!CodeMirror.defaults.mode && name != \"null\") CodeMirror.defaults.mode = name;\n if (arguments.length > 2)\n mode.dependencies = Array.prototype.slice.call(arguments, 2);\n modes[name] = mode;\n };\n\n CodeMirror.defineMIME = function(mime, spec) {\n mimeModes[mime] = spec;\n };\n\n // Given a MIME type, a {name, ...options} config object, or a name\n // string, return a mode config object.\n CodeMirror.resolveMode = function(spec) {\n if (typeof spec == \"string\" && mimeModes.hasOwnProperty(spec)) {\n spec = mimeModes[spec];\n } else if (spec && typeof spec.name == \"string\" && mimeModes.hasOwnProperty(spec.name)) {\n var found = mimeModes[spec.name];\n if (typeof found == \"string\") found = {name: found};\n spec = createObj(found, spec);\n spec.name = found.name;\n } else if (typeof spec == \"string\" && /^[\\w\\-]+\\/[\\w\\-]+\\+xml$/.test(spec)) {\n return CodeMirror.resolveMode(\"application/xml\");\n }\n if (typeof spec == \"string\") return {name: spec};\n else return spec || {name: \"null\"};\n };\n\n // Given a mode spec (anything that resolveMode accepts), find and\n // initialize an actual mode object.\n CodeMirror.getMode = function(options, spec) {\n var spec = CodeMirror.resolveMode(spec);\n var mfactory = modes[spec.name];\n if (!mfactory) return CodeMirror.getMode(options, \"text/plain\");\n var modeObj = mfactory(options, spec);\n if (modeExtensions.hasOwnProperty(spec.name)) {\n var exts = modeExtensions[spec.name];\n for (var prop in exts) {\n if (!exts.hasOwnProperty(prop)) continue;\n if (modeObj.hasOwnProperty(prop)) modeObj[\"_\" + prop] = modeObj[prop];\n modeObj[prop] = exts[prop];\n }\n }\n modeObj.name = spec.name;\n if (spec.helperType) modeObj.helperType = spec.helperType;\n if (spec.modeProps) for (var prop in spec.modeProps)\n modeObj[prop] = spec.modeProps[prop];\n\n return modeObj;\n };\n\n // Minimal default mode.\n CodeMirror.defineMode(\"null\", function() {\n return {token: function(stream) {stream.skipToEnd();}};\n });\n CodeMirror.defineMIME(\"text/plain\", \"null\");\n\n // This can be used to attach properties to mode objects from\n // outside the actual mode definition.\n var modeExtensions = CodeMirror.modeExtensions = {};\n CodeMirror.extendMode = function(mode, properties) {\n var exts = modeExtensions.hasOwnProperty(mode) ? modeExtensions[mode] : (modeExtensions[mode] = {});\n copyObj(properties, exts);\n };\n\n // EXTENSIONS\n\n CodeMirror.defineExtension = function(name, func) {\n CodeMirror.prototype[name] = func;\n };\n CodeMirror.defineDocExtension = function(name, func) {\n Doc.prototype[name] = func;\n };\n CodeMirror.defineOption = option;\n\n var initHooks = [];\n CodeMirror.defineInitHook = function(f) {initHooks.push(f);};\n\n var helpers = CodeMirror.helpers = {};\n CodeMirror.registerHelper = function(type, name, value) {\n if (!helpers.hasOwnProperty(type)) helpers[type] = CodeMirror[type] = {_global: []};\n helpers[type][name] = value;\n };\n CodeMirror.registerGlobalHelper = function(type, name, predicate, value) {\n CodeMirror.registerHelper(type, name, value);\n helpers[type]._global.push({pred: predicate, val: value});\n };\n\n // MODE STATE HANDLING\n\n // Utility functions for working with state. Exported because nested\n // modes need to do this for their inner modes.\n\n var copyState = CodeMirror.copyState = function(mode, state) {\n if (state === true) return state;\n if (mode.copyState) return mode.copyState(state);\n var nstate = {};\n for (var n in state) {\n var val = state[n];\n if (val instanceof Array) val = val.concat([]);\n nstate[n] = val;\n }\n return nstate;\n };\n\n var startState = CodeMirror.startState = function(mode, a1, a2) {\n return mode.startState ? mode.startState(a1, a2) : true;\n };\n\n // Given a mode and a state (for that mode), find the inner mode and\n // state at the position that the state refers to.\n CodeMirror.innerMode = function(mode, state) {\n while (mode.innerMode) {\n var info = mode.innerMode(state);\n if (!info || info.mode == mode) break;\n state = info.state;\n mode = info.mode;\n }\n return info || {mode: mode, state: state};\n };\n\n // STANDARD COMMANDS\n\n // Commands are parameter-less actions that can be performed on an\n // editor, mostly used for keybindings.\n var commands = CodeMirror.commands = {\n selectAll: function(cm) {cm.setSelection(Pos(cm.firstLine(), 0), Pos(cm.lastLine()), sel_dontScroll);},\n singleSelection: function(cm) {\n cm.setSelection(cm.getCursor(\"anchor\"), cm.getCursor(\"head\"), sel_dontScroll);\n },\n killLine: function(cm) {\n deleteNearSelection(cm, function(range) {\n if (range.empty()) {\n var len = getLine(cm.doc, range.head.line).text.length;\n if (range.head.ch == len && range.head.line < cm.lastLine())\n return {from: range.head, to: Pos(range.head.line + 1, 0)};\n else\n return {from: range.head, to: Pos(range.head.line, len)};\n } else {\n return {from: range.from(), to: range.to()};\n }\n });\n },\n deleteLine: function(cm) {\n deleteNearSelection(cm, function(range) {\n return {from: Pos(range.from().line, 0),\n to: clipPos(cm.doc, Pos(range.to().line + 1, 0))};\n });\n },\n delLineLeft: function(cm) {\n deleteNearSelection(cm, function(range) {\n return {from: Pos(range.from().line, 0), to: range.from()};\n });\n },\n delWrappedLineLeft: function(cm) {\n deleteNearSelection(cm, function(range) {\n var top = cm.charCoords(range.head, \"div\").top + 5;\n var leftPos = cm.coordsChar({left: 0, top: top}, \"div\");\n return {from: leftPos, to: range.from()};\n });\n },\n delWrappedLineRight: function(cm) {\n deleteNearSelection(cm, function(range) {\n var top = cm.charCoords(range.head, \"div\").top + 5;\n var rightPos = cm.coordsChar({left: cm.display.lineDiv.offsetWidth + 100, top: top}, \"div\");\n return {from: range.from(), to: rightPos };\n });\n },\n undo: function(cm) {cm.undo();},\n redo: function(cm) {cm.redo();},\n undoSelection: function(cm) {cm.undoSelection();},\n redoSelection: function(cm) {cm.redoSelection();},\n goDocStart: function(cm) {cm.extendSelection(Pos(cm.firstLine(), 0));},\n goDocEnd: function(cm) {cm.extendSelection(Pos(cm.lastLine()));},\n goLineStart: function(cm) {\n cm.extendSelectionsBy(function(range) { return lineStart(cm, range.head.line); },\n {origin: \"+move\", bias: 1});\n },\n goLineStartSmart: function(cm) {\n cm.extendSelectionsBy(function(range) {\n return lineStartSmart(cm, range.head);\n }, {origin: \"+move\", bias: 1});\n },\n goLineEnd: function(cm) {\n cm.extendSelectionsBy(function(range) { return lineEnd(cm, range.head.line); },\n {origin: \"+move\", bias: -1});\n },\n goLineRight: function(cm) {\n cm.extendSelectionsBy(function(range) {\n var top = cm.charCoords(range.head, \"div\").top + 5;\n return cm.coordsChar({left: cm.display.lineDiv.offsetWidth + 100, top: top}, \"div\");\n }, sel_move);\n },\n goLineLeft: function(cm) {\n cm.extendSelectionsBy(function(range) {\n var top = cm.charCoords(range.head, \"div\").top + 5;\n return cm.coordsChar({left: 0, top: top}, \"div\");\n }, sel_move);\n },\n goLineLeftSmart: function(cm) {\n cm.extendSelectionsBy(function(range) {\n var top = cm.charCoords(range.head, \"div\").top + 5;\n var pos = cm.coordsChar({left: 0, top: top}, \"div\");\n if (pos.ch < cm.getLine(pos.line).search(/\\S/)) return lineStartSmart(cm, range.head);\n return pos;\n }, sel_move);\n },\n goLineUp: function(cm) {cm.moveV(-1, \"line\");},\n goLineDown: function(cm) {cm.moveV(1, \"line\");},\n goPageUp: function(cm) {cm.moveV(-1, \"page\");},\n goPageDown: function(cm) {cm.moveV(1, \"page\");},\n goCharLeft: function(cm) {cm.moveH(-1, \"char\");},\n goCharRight: function(cm) {cm.moveH(1, \"char\");},\n goColumnLeft: function(cm) {cm.moveH(-1, \"column\");},\n goColumnRight: function(cm) {cm.moveH(1, \"column\");},\n goWordLeft: function(cm) {cm.moveH(-1, \"word\");},\n goGroupRight: function(cm) {cm.moveH(1, \"group\");},\n goGroupLeft: function(cm) {cm.moveH(-1, \"group\");},\n goWordRight: function(cm) {cm.moveH(1, \"word\");},\n delCharBefore: function(cm) {cm.deleteH(-1, \"char\");},\n delCharAfter: function(cm) {cm.deleteH(1, \"char\");},\n delWordBefore: function(cm) {cm.deleteH(-1, \"word\");},\n delWordAfter: function(cm) {cm.deleteH(1, \"word\");},\n delGroupBefore: function(cm) {cm.deleteH(-1, \"group\");},\n delGroupAfter: function(cm) {cm.deleteH(1, \"group\");},\n indentAuto: function(cm) {cm.indentSelection(\"smart\");},\n indentMore: function(cm) {cm.indentSelection(\"add\");},\n indentLess: function(cm) {cm.indentSelection(\"subtract\");},\n insertTab: function(cm) {cm.replaceSelection(\"\\t\");},\n insertSoftTab: function(cm) {\n var spaces = [], ranges = cm.listSelections(), tabSize = cm.options.tabSize;\n for (var i = 0; i < ranges.length; i++) {\n var pos = ranges[i].from();\n var col = countColumn(cm.getLine(pos.line), pos.ch, tabSize);\n spaces.push(new Array(tabSize - col % tabSize + 1).join(\" \"));\n }\n cm.replaceSelections(spaces);\n },\n defaultTab: function(cm) {\n if (cm.somethingSelected()) cm.indentSelection(\"add\");\n else cm.execCommand(\"insertTab\");\n },\n transposeChars: function(cm) {\n runInOp(cm, function() {\n var ranges = cm.listSelections(), newSel = [];\n for (var i = 0; i < ranges.length; i++) {\n var cur = ranges[i].head, line = getLine(cm.doc, cur.line).text;\n if (line) {\n if (cur.ch == line.length) cur = new Pos(cur.line, cur.ch - 1);\n if (cur.ch > 0) {\n cur = new Pos(cur.line, cur.ch + 1);\n cm.replaceRange(line.charAt(cur.ch - 1) + line.charAt(cur.ch - 2),\n Pos(cur.line, cur.ch - 2), cur, \"+transpose\");\n } else if (cur.line > cm.doc.first) {\n var prev = getLine(cm.doc, cur.line - 1).text;\n if (prev)\n cm.replaceRange(line.charAt(0) + cm.doc.lineSeparator() +\n prev.charAt(prev.length - 1),\n Pos(cur.line - 1, prev.length - 1), Pos(cur.line, 1), \"+transpose\");\n }\n }\n newSel.push(new Range(cur, cur));\n }\n cm.setSelections(newSel);\n });\n },\n newlineAndIndent: function(cm) {\n runInOp(cm, function() {\n var len = cm.listSelections().length;\n for (var i = 0; i < len; i++) {\n var range = cm.listSelections()[i];\n cm.replaceRange(cm.doc.lineSeparator(), range.anchor, range.head, \"+input\");\n cm.indentLine(range.from().line + 1, null, true);\n }\n ensureCursorVisible(cm);\n });\n },\n toggleOverwrite: function(cm) {cm.toggleOverwrite();}\n };\n\n\n // STANDARD KEYMAPS\n\n var keyMap = CodeMirror.keyMap = {};\n\n keyMap.basic = {\n \"Left\": \"goCharLeft\", \"Right\": \"goCharRight\", \"Up\": \"goLineUp\", \"Down\": \"goLineDown\",\n \"End\": \"goLineEnd\", \"Home\": \"goLineStartSmart\", \"PageUp\": \"goPageUp\", \"PageDown\": \"goPageDown\",\n \"Delete\": \"delCharAfter\", \"Backspace\": \"delCharBefore\", \"Shift-Backspace\": \"delCharBefore\",\n \"Tab\": \"defaultTab\", \"Shift-Tab\": \"indentAuto\",\n \"Enter\": \"newlineAndIndent\", \"Insert\": \"toggleOverwrite\",\n \"Esc\": \"singleSelection\"\n };\n // Note that the save and find-related commands aren't defined by\n // default. User code or addons can define them. Unknown commands\n // are simply ignored.\n keyMap.pcDefault = {\n \"Ctrl-A\": \"selectAll\", \"Ctrl-D\": \"deleteLine\", \"Ctrl-Z\": \"undo\", \"Shift-Ctrl-Z\": \"redo\", \"Ctrl-Y\": \"redo\",\n \"Ctrl-Home\": \"goDocStart\", \"Ctrl-End\": \"goDocEnd\", \"Ctrl-Up\": \"goLineUp\", \"Ctrl-Down\": \"goLineDown\",\n \"Ctrl-Left\": \"goGroupLeft\", \"Ctrl-Right\": \"goGroupRight\", \"Alt-Left\": \"goLineStart\", \"Alt-Right\": \"goLineEnd\",\n \"Ctrl-Backspace\": \"delGroupBefore\", \"Ctrl-Delete\": \"delGroupAfter\", \"Ctrl-S\": \"save\", \"Ctrl-F\": \"find\",\n \"Ctrl-G\": \"findNext\", \"Shift-Ctrl-G\": \"findPrev\", \"Shift-Ctrl-F\": \"replace\", \"Shift-Ctrl-R\": \"replaceAll\",\n \"Ctrl-[\": \"indentLess\", \"Ctrl-]\": \"indentMore\",\n \"Ctrl-U\": \"undoSelection\", \"Shift-Ctrl-U\": \"redoSelection\", \"Alt-U\": \"redoSelection\",\n fallthrough: \"basic\"\n };\n // Very basic readline/emacs-style bindings, which are standard on Mac.\n keyMap.emacsy = {\n \"Ctrl-F\": \"goCharRight\", \"Ctrl-B\": \"goCharLeft\", \"Ctrl-P\": \"goLineUp\", \"Ctrl-N\": \"goLineDown\",\n \"Alt-F\": \"goWordRight\", \"Alt-B\": \"goWordLeft\", \"Ctrl-A\": \"goLineStart\", \"Ctrl-E\": \"goLineEnd\",\n \"Ctrl-V\": \"goPageDown\", \"Shift-Ctrl-V\": \"goPageUp\", \"Ctrl-D\": \"delCharAfter\", \"Ctrl-H\": \"delCharBefore\",\n \"Alt-D\": \"delWordAfter\", \"Alt-Backspace\": \"delWordBefore\", \"Ctrl-K\": \"killLine\", \"Ctrl-T\": \"transposeChars\"\n };\n keyMap.macDefault = {\n \"Cmd-A\": \"selectAll\", \"Cmd-D\": \"deleteLine\", \"Cmd-Z\": \"undo\", \"Shift-Cmd-Z\": \"redo\", \"Cmd-Y\": \"redo\",\n \"Cmd-Home\": \"goDocStart\", \"Cmd-Up\": \"goDocStart\", \"Cmd-End\": \"goDocEnd\", \"Cmd-Down\": \"goDocEnd\", \"Alt-Left\": \"goGroupLeft\",\n \"Alt-Right\": \"goGroupRight\", \"Cmd-Left\": \"goLineLeft\", \"Cmd-Right\": \"goLineRight\", \"Alt-Backspace\": \"delGroupBefore\",\n \"Ctrl-Alt-Backspace\": \"delGroupAfter\", \"Alt-Delete\": \"delGroupAfter\", \"Cmd-S\": \"save\", \"Cmd-F\": \"find\",\n \"Cmd-G\": \"findNext\", \"Shift-Cmd-G\": \"findPrev\", \"Cmd-Alt-F\": \"replace\", \"Shift-Cmd-Alt-F\": \"replaceAll\",\n \"Cmd-[\": \"indentLess\", \"Cmd-]\": \"indentMore\", \"Cmd-Backspace\": \"delWrappedLineLeft\", \"Cmd-Delete\": \"delWrappedLineRight\",\n \"Cmd-U\": \"undoSelection\", \"Shift-Cmd-U\": \"redoSelection\", \"Ctrl-Up\": \"goDocStart\", \"Ctrl-Down\": \"goDocEnd\",\n fallthrough: [\"basic\", \"emacsy\"]\n };\n keyMap[\"default\"] = mac ? keyMap.macDefault : keyMap.pcDefault;\n\n // KEYMAP DISPATCH\n\n function normalizeKeyName(name) {\n var parts = name.split(/-(?!$)/), name = parts[parts.length - 1];\n var alt, ctrl, shift, cmd;\n for (var i = 0; i < parts.length - 1; i++) {\n var mod = parts[i];\n if (/^(cmd|meta|m)$/i.test(mod)) cmd = true;\n else if (/^a(lt)?$/i.test(mod)) alt = true;\n else if (/^(c|ctrl|control)$/i.test(mod)) ctrl = true;\n else if (/^s(hift)$/i.test(mod)) shift = true;\n else throw new Error(\"Unrecognized modifier name: \" + mod);\n }\n if (alt) name = \"Alt-\" + name;\n if (ctrl) name = \"Ctrl-\" + name;\n if (cmd) name = \"Cmd-\" + name;\n if (shift) name = \"Shift-\" + name;\n return name;\n }\n\n // This is a kludge to keep keymaps mostly working as raw objects\n // (backwards compatibility) while at the same time support features\n // like normalization and multi-stroke key bindings. It compiles a\n // new normalized keymap, and then updates the old object to reflect\n // this.\n CodeMirror.normalizeKeyMap = function(keymap) {\n var copy = {};\n for (var keyname in keymap) if (keymap.hasOwnProperty(keyname)) {\n var value = keymap[keyname];\n if (/^(name|fallthrough|(de|at)tach)$/.test(keyname)) continue;\n if (value == \"...\") { delete keymap[keyname]; continue; }\n\n var keys = map(keyname.split(\" \"), normalizeKeyName);\n for (var i = 0; i < keys.length; i++) {\n var val, name;\n if (i == keys.length - 1) {\n name = keys.join(\" \");\n val = value;\n } else {\n name = keys.slice(0, i + 1).join(\" \");\n val = \"...\";\n }\n var prev = copy[name];\n if (!prev) copy[name] = val;\n else if (prev != val) throw new Error(\"Inconsistent bindings for \" + name);\n }\n delete keymap[keyname];\n }\n for (var prop in copy) keymap[prop] = copy[prop];\n return keymap;\n };\n\n var lookupKey = CodeMirror.lookupKey = function(key, map, handle, context) {\n map = getKeyMap(map);\n var found = map.call ? map.call(key, context) : map[key];\n if (found === false) return \"nothing\";\n if (found === \"...\") return \"multi\";\n if (found != null && handle(found)) return \"handled\";\n\n if (map.fallthrough) {\n if (Object.prototype.toString.call(map.fallthrough) != \"[object Array]\")\n return lookupKey(key, map.fallthrough, handle, context);\n for (var i = 0; i < map.fallthrough.length; i++) {\n var result = lookupKey(key, map.fallthrough[i], handle, context);\n if (result) return result;\n }\n }\n };\n\n // Modifier key presses don't count as 'real' key presses for the\n // purpose of keymap fallthrough.\n var isModifierKey = CodeMirror.isModifierKey = function(value) {\n var name = typeof value == \"string\" ? value : keyNames[value.keyCode];\n return name == \"Ctrl\" || name == \"Alt\" || name == \"Shift\" || name == \"Mod\";\n };\n\n // Look up the name of a key as indicated by an event object.\n var keyName = CodeMirror.keyName = function(event, noShift) {\n if (presto && event.keyCode == 34 && event[\"char\"]) return false;\n var base = keyNames[event.keyCode], name = base;\n if (name == null || event.altGraphKey) return false;\n if (event.altKey && base != \"Alt\") name = \"Alt-\" + name;\n if ((flipCtrlCmd ? event.metaKey : event.ctrlKey) && base != \"Ctrl\") name = \"Ctrl-\" + name;\n if ((flipCtrlCmd ? event.ctrlKey : event.metaKey) && base != \"Cmd\") name = \"Cmd-\" + name;\n if (!noShift && event.shiftKey && base != \"Shift\") name = \"Shift-\" + name;\n return name;\n };\n\n function getKeyMap(val) {\n return typeof val == \"string\" ? keyMap[val] : val;\n }\n\n // FROMTEXTAREA\n\n CodeMirror.fromTextArea = function(textarea, options) {\n options = options ? copyObj(options) : {};\n options.value = textarea.value;\n if (!options.tabindex && textarea.tabIndex)\n options.tabindex = textarea.tabIndex;\n if (!options.placeholder && textarea.placeholder)\n options.placeholder = textarea.placeholder;\n // Set autofocus to true if this textarea is focused, or if it has\n // autofocus and no other element is focused.\n if (options.autofocus == null) {\n var hasFocus = activeElt();\n options.autofocus = hasFocus == textarea ||\n textarea.getAttribute(\"autofocus\") != null && hasFocus == document.body;\n }\n\n function save() {textarea.value = cm.getValue();}\n if (textarea.form) {\n on(textarea.form, \"submit\", save);\n // Deplorable hack to make the submit method do the right thing.\n if (!options.leaveSubmitMethodAlone) {\n var form = textarea.form, realSubmit = form.submit;\n try {\n var wrappedSubmit = form.submit = function() {\n save();\n form.submit = realSubmit;\n form.submit();\n form.submit = wrappedSubmit;\n };\n } catch(e) {}\n }\n }\n\n options.finishInit = function(cm) {\n cm.save = save;\n cm.getTextArea = function() { return textarea; };\n cm.toTextArea = function() {\n cm.toTextArea = isNaN; // Prevent this from being ran twice\n save();\n textarea.parentNode.removeChild(cm.getWrapperElement());\n textarea.style.display = \"\";\n if (textarea.form) {\n off(textarea.form, \"submit\", save);\n if (typeof textarea.form.submit == \"function\")\n textarea.form.submit = realSubmit;\n }\n };\n };\n\n textarea.style.display = \"none\";\n var cm = CodeMirror(function(node) {\n textarea.parentNode.insertBefore(node, textarea.nextSibling);\n }, options);\n return cm;\n };\n\n // STRING STREAM\n\n // Fed to the mode parsers, provides helper functions to make\n // parsers more succinct.\n\n var StringStream = CodeMirror.StringStream = function(string, tabSize) {\n this.pos = this.start = 0;\n this.string = string;\n this.tabSize = tabSize || 8;\n this.lastColumnPos = this.lastColumnValue = 0;\n this.lineStart = 0;\n };\n\n StringStream.prototype = {\n eol: function() {return this.pos >= this.string.length;},\n sol: function() {return this.pos == this.lineStart;},\n peek: function() {return this.string.charAt(this.pos) || undefined;},\n next: function() {\n if (this.pos < this.string.length)\n return this.string.charAt(this.pos++);\n },\n eat: function(match) {\n var ch = this.string.charAt(this.pos);\n if (typeof match == \"string\") var ok = ch == match;\n else var ok = ch && (match.test ? match.test(ch) : match(ch));\n if (ok) {++this.pos; return ch;}\n },\n eatWhile: function(match) {\n var start = this.pos;\n while (this.eat(match)){}\n return this.pos > start;\n },\n eatSpace: function() {\n var start = this.pos;\n while (/[\\s\\u00a0]/.test(this.string.charAt(this.pos))) ++this.pos;\n return this.pos > start;\n },\n skipToEnd: function() {this.pos = this.string.length;},\n skipTo: function(ch) {\n var found = this.string.indexOf(ch, this.pos);\n if (found > -1) {this.pos = found; return true;}\n },\n backUp: function(n) {this.pos -= n;},\n column: function() {\n if (this.lastColumnPos < this.start) {\n this.lastColumnValue = countColumn(this.string, this.start, this.tabSize, this.lastColumnPos, this.lastColumnValue);\n this.lastColumnPos = this.start;\n }\n return this.lastColumnValue - (this.lineStart ? countColumn(this.string, this.lineStart, this.tabSize) : 0);\n },\n indentation: function() {\n return countColumn(this.string, null, this.tabSize) -\n (this.lineStart ? countColumn(this.string, this.lineStart, this.tabSize) : 0);\n },\n match: function(pattern, consume, caseInsensitive) {\n if (typeof pattern == \"string\") {\n var cased = function(str) {return caseInsensitive ? str.toLowerCase() : str;};\n var substr = this.string.substr(this.pos, pattern.length);\n if (cased(substr) == cased(pattern)) {\n if (consume !== false) this.pos += pattern.length;\n return true;\n }\n } else {\n var match = this.string.slice(this.pos).match(pattern);\n if (match && match.index > 0) return null;\n if (match && consume !== false) this.pos += match[0].length;\n return match;\n }\n },\n current: function(){return this.string.slice(this.start, this.pos);},\n hideFirstChars: function(n, inner) {\n this.lineStart += n;\n try { return inner(); }\n finally { this.lineStart -= n; }\n }\n };\n\n // TEXTMARKERS\n\n // Created with markText and setBookmark methods. A TextMarker is a\n // handle that can be used to clear or find a marked position in the\n // document. Line objects hold arrays (markedSpans) containing\n // {from, to, marker} object pointing to such marker objects, and\n // indicating that such a marker is present on that line. Multiple\n // lines may point to the same marker when it spans across lines.\n // The spans will have null for their from/to properties when the\n // marker continues beyond the start/end of the line. Markers have\n // links back to the lines they currently touch.\n\n var nextMarkerId = 0;\n\n var TextMarker = CodeMirror.TextMarker = function(doc, type) {\n this.lines = [];\n this.type = type;\n this.doc = doc;\n this.id = ++nextMarkerId;\n };\n eventMixin(TextMarker);\n\n // Clear the marker.\n TextMarker.prototype.clear = function() {\n if (this.explicitlyCleared) return;\n var cm = this.doc.cm, withOp = cm && !cm.curOp;\n if (withOp) startOperation(cm);\n if (hasHandler(this, \"clear\")) {\n var found = this.find();\n if (found) signalLater(this, \"clear\", found.from, found.to);\n }\n var min = null, max = null;\n for (var i = 0; i < this.lines.length; ++i) {\n var line = this.lines[i];\n var span = getMarkedSpanFor(line.markedSpans, this);\n if (cm && !this.collapsed) regLineChange(cm, lineNo(line), \"text\");\n else if (cm) {\n if (span.to != null) max = lineNo(line);\n if (span.from != null) min = lineNo(line);\n }\n line.markedSpans = removeMarkedSpan(line.markedSpans, span);\n if (span.from == null && this.collapsed && !lineIsHidden(this.doc, line) && cm)\n updateLineHeight(line, textHeight(cm.display));\n }\n if (cm && this.collapsed && !cm.options.lineWrapping) for (var i = 0; i < this.lines.length; ++i) {\n var visual = visualLine(this.lines[i]), len = lineLength(visual);\n if (len > cm.display.maxLineLength) {\n cm.display.maxLine = visual;\n cm.display.maxLineLength = len;\n cm.display.maxLineChanged = true;\n }\n }\n\n if (min != null && cm && this.collapsed) regChange(cm, min, max + 1);\n this.lines.length = 0;\n this.explicitlyCleared = true;\n if (this.atomic && this.doc.cantEdit) {\n this.doc.cantEdit = false;\n if (cm) reCheckSelection(cm.doc);\n }\n if (cm) signalLater(cm, \"markerCleared\", cm, this);\n if (withOp) endOperation(cm);\n if (this.parent) this.parent.clear();\n };\n\n // Find the position of the marker in the document. Returns a {from,\n // to} object by default. Side can be passed to get a specific side\n // -- 0 (both), -1 (left), or 1 (right). When lineObj is true, the\n // Pos objects returned contain a line object, rather than a line\n // number (used to prevent looking up the same line twice).\n TextMarker.prototype.find = function(side, lineObj) {\n if (side == null && this.type == \"bookmark\") side = 1;\n var from, to;\n for (var i = 0; i < this.lines.length; ++i) {\n var line = this.lines[i];\n var span = getMarkedSpanFor(line.markedSpans, this);\n if (span.from != null) {\n from = Pos(lineObj ? line : lineNo(line), span.from);\n if (side == -1) return from;\n }\n if (span.to != null) {\n to = Pos(lineObj ? line : lineNo(line), span.to);\n if (side == 1) return to;\n }\n }\n return from && {from: from, to: to};\n };\n\n // Signals that the marker's widget changed, and surrounding layout\n // should be recomputed.\n TextMarker.prototype.changed = function() {\n var pos = this.find(-1, true), widget = this, cm = this.doc.cm;\n if (!pos || !cm) return;\n runInOp(cm, function() {\n var line = pos.line, lineN = lineNo(pos.line);\n var view = findViewForLine(cm, lineN);\n if (view) {\n clearLineMeasurementCacheFor(view);\n cm.curOp.selectionChanged = cm.curOp.forceUpdate = true;\n }\n cm.curOp.updateMaxLine = true;\n if (!lineIsHidden(widget.doc, line) && widget.height != null) {\n var oldHeight = widget.height;\n widget.height = null;\n var dHeight = widgetHeight(widget) - oldHeight;\n if (dHeight)\n updateLineHeight(line, line.height + dHeight);\n }\n });\n };\n\n TextMarker.prototype.attachLine = function(line) {\n if (!this.lines.length && this.doc.cm) {\n var op = this.doc.cm.curOp;\n if (!op.maybeHiddenMarkers || indexOf(op.maybeHiddenMarkers, this) == -1)\n (op.maybeUnhiddenMarkers || (op.maybeUnhiddenMarkers = [])).push(this);\n }\n this.lines.push(line);\n };\n TextMarker.prototype.detachLine = function(line) {\n this.lines.splice(indexOf(this.lines, line), 1);\n if (!this.lines.length && this.doc.cm) {\n var op = this.doc.cm.curOp;\n (op.maybeHiddenMarkers || (op.maybeHiddenMarkers = [])).push(this);\n }\n };\n\n // Collapsed markers have unique ids, in order to be able to order\n // them, which is needed for uniquely determining an outer marker\n // when they overlap (they may nest, but not partially overlap).\n var nextMarkerId = 0;\n\n // Create a marker, wire it up to the right lines, and\n function markText(doc, from, to, options, type) {\n // Shared markers (across linked documents) are handled separately\n // (markTextShared will call out to this again, once per\n // document).\n if (options && options.shared) return markTextShared(doc, from, to, options, type);\n // Ensure we are in an operation.\n if (doc.cm && !doc.cm.curOp) return operation(doc.cm, markText)(doc, from, to, options, type);\n\n var marker = new TextMarker(doc, type), diff = cmp(from, to);\n if (options) copyObj(options, marker, false);\n // Don't connect empty markers unless clearWhenEmpty is false\n if (diff > 0 || diff == 0 && marker.clearWhenEmpty !== false)\n return marker;\n if (marker.replacedWith) {\n // Showing up as a widget implies collapsed (widget replaces text)\n marker.collapsed = true;\n marker.widgetNode = elt(\"span\", [marker.replacedWith], \"CodeMirror-widget\");\n if (!options.handleMouseEvents) marker.widgetNode.setAttribute(\"cm-ignore-events\", \"true\");\n if (options.insertLeft) marker.widgetNode.insertLeft = true;\n }\n if (marker.collapsed) {\n if (conflictingCollapsedRange(doc, from.line, from, to, marker) ||\n from.line != to.line && conflictingCollapsedRange(doc, to.line, from, to, marker))\n throw new Error(\"Inserting collapsed marker partially overlapping an existing one\");\n sawCollapsedSpans = true;\n }\n\n if (marker.addToHistory)\n addChangeToHistory(doc, {from: from, to: to, origin: \"markText\"}, doc.sel, NaN);\n\n var curLine = from.line, cm = doc.cm, updateMaxLine;\n doc.iter(curLine, to.line + 1, function(line) {\n if (cm && marker.collapsed && !cm.options.lineWrapping && visualLine(line) == cm.display.maxLine)\n updateMaxLine = true;\n if (marker.collapsed && curLine != from.line) updateLineHeight(line, 0);\n addMarkedSpan(line, new MarkedSpan(marker,\n curLine == from.line ? from.ch : null,\n curLine == to.line ? to.ch : null));\n ++curLine;\n });\n // lineIsHidden depends on the presence of the spans, so needs a second pass\n if (marker.collapsed) doc.iter(from.line, to.line + 1, function(line) {\n if (lineIsHidden(doc, line)) updateLineHeight(line, 0);\n });\n\n if (marker.clearOnEnter) on(marker, \"beforeCursorEnter\", function() { marker.clear(); });\n\n if (marker.readOnly) {\n sawReadOnlySpans = true;\n if (doc.history.done.length || doc.history.undone.length)\n doc.clearHistory();\n }\n if (marker.collapsed) {\n marker.id = ++nextMarkerId;\n marker.atomic = true;\n }\n if (cm) {\n // Sync editor state\n if (updateMaxLine) cm.curOp.updateMaxLine = true;\n if (marker.collapsed)\n regChange(cm, from.line, to.line + 1);\n else if (marker.className || marker.title || marker.startStyle || marker.endStyle || marker.css)\n for (var i = from.line; i <= to.line; i++) regLineChange(cm, i, \"text\");\n if (marker.atomic) reCheckSelection(cm.doc);\n signalLater(cm, \"markerAdded\", cm, marker);\n }\n return marker;\n }\n\n // SHARED TEXTMARKERS\n\n // A shared marker spans multiple linked documents. It is\n // implemented as a meta-marker-object controlling multiple normal\n // markers.\n var SharedTextMarker = CodeMirror.SharedTextMarker = function(markers, primary) {\n this.markers = markers;\n this.primary = primary;\n for (var i = 0; i < markers.length; ++i)\n markers[i].parent = this;\n };\n eventMixin(SharedTextMarker);\n\n SharedTextMarker.prototype.clear = function() {\n if (this.explicitlyCleared) return;\n this.explicitlyCleared = true;\n for (var i = 0; i < this.markers.length; ++i)\n this.markers[i].clear();\n signalLater(this, \"clear\");\n };\n SharedTextMarker.prototype.find = function(side, lineObj) {\n return this.primary.find(side, lineObj);\n };\n\n function markTextShared(doc, from, to, options, type) {\n options = copyObj(options);\n options.shared = false;\n var markers = [markText(doc, from, to, options, type)], primary = markers[0];\n var widget = options.widgetNode;\n linkedDocs(doc, function(doc) {\n if (widget) options.widgetNode = widget.cloneNode(true);\n markers.push(markText(doc, clipPos(doc, from), clipPos(doc, to), options, type));\n for (var i = 0; i < doc.linked.length; ++i)\n if (doc.linked[i].isParent) return;\n primary = lst(markers);\n });\n return new SharedTextMarker(markers, primary);\n }\n\n function findSharedMarkers(doc) {\n return doc.findMarks(Pos(doc.first, 0), doc.clipPos(Pos(doc.lastLine())),\n function(m) { return m.parent; });\n }\n\n function copySharedMarkers(doc, markers) {\n for (var i = 0; i < markers.length; i++) {\n var marker = markers[i], pos = marker.find();\n var mFrom = doc.clipPos(pos.from), mTo = doc.clipPos(pos.to);\n if (cmp(mFrom, mTo)) {\n var subMark = markText(doc, mFrom, mTo, marker.primary, marker.primary.type);\n marker.markers.push(subMark);\n subMark.parent = marker;\n }\n }\n }\n\n function detachSharedMarkers(markers) {\n for (var i = 0; i < markers.length; i++) {\n var marker = markers[i], linked = [marker.primary.doc];;\n linkedDocs(marker.primary.doc, function(d) { linked.push(d); });\n for (var j = 0; j < marker.markers.length; j++) {\n var subMarker = marker.markers[j];\n if (indexOf(linked, subMarker.doc) == -1) {\n subMarker.parent = null;\n marker.markers.splice(j--, 1);\n }\n }\n }\n }\n\n // TEXTMARKER SPANS\n\n function MarkedSpan(marker, from, to) {\n this.marker = marker;\n this.from = from; this.to = to;\n }\n\n // Search an array of spans for a span matching the given marker.\n function getMarkedSpanFor(spans, marker) {\n if (spans) for (var i = 0; i < spans.length; ++i) {\n var span = spans[i];\n if (span.marker == marker) return span;\n }\n }\n // Remove a span from an array, returning undefined if no spans are\n // left (we don't store arrays for lines without spans).\n function removeMarkedSpan(spans, span) {\n for (var r, i = 0; i < spans.length; ++i)\n if (spans[i] != span) (r || (r = [])).push(spans[i]);\n return r;\n }\n // Add a span to a line.\n function addMarkedSpan(line, span) {\n line.markedSpans = line.markedSpans ? line.markedSpans.concat([span]) : [span];\n span.marker.attachLine(line);\n }\n\n // Used for the algorithm that adjusts markers for a change in the\n // document. These functions cut an array of spans at a given\n // character position, returning an array of remaining chunks (or\n // undefined if nothing remains).\n function markedSpansBefore(old, startCh, isInsert) {\n if (old) for (var i = 0, nw; i < old.length; ++i) {\n var span = old[i], marker = span.marker;\n var startsBefore = span.from == null || (marker.inclusiveLeft ? span.from <= startCh : span.from < startCh);\n if (startsBefore || span.from == startCh && marker.type == \"bookmark\" && (!isInsert || !span.marker.insertLeft)) {\n var endsAfter = span.to == null || (marker.inclusiveRight ? span.to >= startCh : span.to > startCh);\n (nw || (nw = [])).push(new MarkedSpan(marker, span.from, endsAfter ? null : span.to));\n }\n }\n return nw;\n }\n function markedSpansAfter(old, endCh, isInsert) {\n if (old) for (var i = 0, nw; i < old.length; ++i) {\n var span = old[i], marker = span.marker;\n var endsAfter = span.to == null || (marker.inclusiveRight ? span.to >= endCh : span.to > endCh);\n if (endsAfter || span.from == endCh && marker.type == \"bookmark\" && (!isInsert || span.marker.insertLeft)) {\n var startsBefore = span.from == null || (marker.inclusiveLeft ? span.from <= endCh : span.from < endCh);\n (nw || (nw = [])).push(new MarkedSpan(marker, startsBefore ? null : span.from - endCh,\n span.to == null ? null : span.to - endCh));\n }\n }\n return nw;\n }\n\n // Given a change object, compute the new set of marker spans that\n // cover the line in which the change took place. Removes spans\n // entirely within the change, reconnects spans belonging to the\n // same marker that appear on both sides of the change, and cuts off\n // spans partially within the change. Returns an array of span\n // arrays with one element for each line in (after) the change.\n function stretchSpansOverChange(doc, change) {\n if (change.full) return null;\n var oldFirst = isLine(doc, change.from.line) && getLine(doc, change.from.line).markedSpans;\n var oldLast = isLine(doc, change.to.line) && getLine(doc, change.to.line).markedSpans;\n if (!oldFirst && !oldLast) return null;\n\n var startCh = change.from.ch, endCh = change.to.ch, isInsert = cmp(change.from, change.to) == 0;\n // Get the spans that 'stick out' on both sides\n var first = markedSpansBefore(oldFirst, startCh, isInsert);\n var last = markedSpansAfter(oldLast, endCh, isInsert);\n\n // Next, merge those two ends\n var sameLine = change.text.length == 1, offset = lst(change.text).length + (sameLine ? startCh : 0);\n if (first) {\n // Fix up .to properties of first\n for (var i = 0; i < first.length; ++i) {\n var span = first[i];\n if (span.to == null) {\n var found = getMarkedSpanFor(last, span.marker);\n if (!found) span.to = startCh;\n else if (sameLine) span.to = found.to == null ? null : found.to + offset;\n }\n }\n }\n if (last) {\n // Fix up .from in last (or move them into first in case of sameLine)\n for (var i = 0; i < last.length; ++i) {\n var span = last[i];\n if (span.to != null) span.to += offset;\n if (span.from == null) {\n var found = getMarkedSpanFor(first, span.marker);\n if (!found) {\n span.from = offset;\n if (sameLine) (first || (first = [])).push(span);\n }\n } else {\n span.from += offset;\n if (sameLine) (first || (first = [])).push(span);\n }\n }\n }\n // Make sure we didn't create any zero-length spans\n if (first) first = clearEmptySpans(first);\n if (last && last != first) last = clearEmptySpans(last);\n\n var newMarkers = [first];\n if (!sameLine) {\n // Fill gap with whole-line-spans\n var gap = change.text.length - 2, gapMarkers;\n if (gap > 0 && first)\n for (var i = 0; i < first.length; ++i)\n if (first[i].to == null)\n (gapMarkers || (gapMarkers = [])).push(new MarkedSpan(first[i].marker, null, null));\n for (var i = 0; i < gap; ++i)\n newMarkers.push(gapMarkers);\n newMarkers.push(last);\n }\n return newMarkers;\n }\n\n // Remove spans that are empty and don't have a clearWhenEmpty\n // option of false.\n function clearEmptySpans(spans) {\n for (var i = 0; i < spans.length; ++i) {\n var span = spans[i];\n if (span.from != null && span.from == span.to && span.marker.clearWhenEmpty !== false)\n spans.splice(i--, 1);\n }\n if (!spans.length) return null;\n return spans;\n }\n\n // Used for un/re-doing changes from the history. Combines the\n // result of computing the existing spans with the set of spans that\n // existed in the history (so that deleting around a span and then\n // undoing brings back the span).\n function mergeOldSpans(doc, change) {\n var old = getOldSpans(doc, change);\n var stretched = stretchSpansOverChange(doc, change);\n if (!old) return stretched;\n if (!stretched) return old;\n\n for (var i = 0; i < old.length; ++i) {\n var oldCur = old[i], stretchCur = stretched[i];\n if (oldCur && stretchCur) {\n spans: for (var j = 0; j < stretchCur.length; ++j) {\n var span = stretchCur[j];\n for (var k = 0; k < oldCur.length; ++k)\n if (oldCur[k].marker == span.marker) continue spans;\n oldCur.push(span);\n }\n } else if (stretchCur) {\n old[i] = stretchCur;\n }\n }\n return old;\n }\n\n // Used to 'clip' out readOnly ranges when making a change.\n function removeReadOnlyRanges(doc, from, to) {\n var markers = null;\n doc.iter(from.line, to.line + 1, function(line) {\n if (line.markedSpans) for (var i = 0; i < line.markedSpans.length; ++i) {\n var mark = line.markedSpans[i].marker;\n if (mark.readOnly && (!markers || indexOf(markers, mark) == -1))\n (markers || (markers = [])).push(mark);\n }\n });\n if (!markers) return null;\n var parts = [{from: from, to: to}];\n for (var i = 0; i < markers.length; ++i) {\n var mk = markers[i], m = mk.find(0);\n for (var j = 0; j < parts.length; ++j) {\n var p = parts[j];\n if (cmp(p.to, m.from) < 0 || cmp(p.from, m.to) > 0) continue;\n var newParts = [j, 1], dfrom = cmp(p.from, m.from), dto = cmp(p.to, m.to);\n if (dfrom < 0 || !mk.inclusiveLeft && !dfrom)\n newParts.push({from: p.from, to: m.from});\n if (dto > 0 || !mk.inclusiveRight && !dto)\n newParts.push({from: m.to, to: p.to});\n parts.splice.apply(parts, newParts);\n j += newParts.length - 1;\n }\n }\n return parts;\n }\n\n // Connect or disconnect spans from a line.\n function detachMarkedSpans(line) {\n var spans = line.markedSpans;\n if (!spans) return;\n for (var i = 0; i < spans.length; ++i)\n spans[i].marker.detachLine(line);\n line.markedSpans = null;\n }\n function attachMarkedSpans(line, spans) {\n if (!spans) return;\n for (var i = 0; i < spans.length; ++i)\n spans[i].marker.attachLine(line);\n line.markedSpans = spans;\n }\n\n // Helpers used when computing which overlapping collapsed span\n // counts as the larger one.\n function extraLeft(marker) { return marker.inclusiveLeft ? -1 : 0; }\n function extraRight(marker) { return marker.inclusiveRight ? 1 : 0; }\n\n // Returns a number indicating which of two overlapping collapsed\n // spans is larger (and thus includes the other). Falls back to\n // comparing ids when the spans cover exactly the same range.\n function compareCollapsedMarkers(a, b) {\n var lenDiff = a.lines.length - b.lines.length;\n if (lenDiff != 0) return lenDiff;\n var aPos = a.find(), bPos = b.find();\n var fromCmp = cmp(aPos.from, bPos.from) || extraLeft(a) - extraLeft(b);\n if (fromCmp) return -fromCmp;\n var toCmp = cmp(aPos.to, bPos.to) || extraRight(a) - extraRight(b);\n if (toCmp) return toCmp;\n return b.id - a.id;\n }\n\n // Find out whether a line ends or starts in a collapsed span. If\n // so, return the marker for that span.\n function collapsedSpanAtSide(line, start) {\n var sps = sawCollapsedSpans && line.markedSpans, found;\n if (sps) for (var sp, i = 0; i < sps.length; ++i) {\n sp = sps[i];\n if (sp.marker.collapsed && (start ? sp.from : sp.to) == null &&\n (!found || compareCollapsedMarkers(found, sp.marker) < 0))\n found = sp.marker;\n }\n return found;\n }\n function collapsedSpanAtStart(line) { return collapsedSpanAtSide(line, true); }\n function collapsedSpanAtEnd(line) { return collapsedSpanAtSide(line, false); }\n\n // Test whether there exists a collapsed span that partially\n // overlaps (covers the start or end, but not both) of a new span.\n // Such overlap is not allowed.\n function conflictingCollapsedRange(doc, lineNo, from, to, marker) {\n var line = getLine(doc, lineNo);\n var sps = sawCollapsedSpans && line.markedSpans;\n if (sps) for (var i = 0; i < sps.length; ++i) {\n var sp = sps[i];\n if (!sp.marker.collapsed) continue;\n var found = sp.marker.find(0);\n var fromCmp = cmp(found.from, from) || extraLeft(sp.marker) - extraLeft(marker);\n var toCmp = cmp(found.to, to) || extraRight(sp.marker) - extraRight(marker);\n if (fromCmp >= 0 && toCmp <= 0 || fromCmp <= 0 && toCmp >= 0) continue;\n if (fromCmp <= 0 && (cmp(found.to, from) > 0 || (sp.marker.inclusiveRight && marker.inclusiveLeft)) ||\n fromCmp >= 0 && (cmp(found.from, to) < 0 || (sp.marker.inclusiveLeft && marker.inclusiveRight)))\n return true;\n }\n }\n\n // A visual line is a line as drawn on the screen. Folding, for\n // example, can cause multiple logical lines to appear on the same\n // visual line. This finds the start of the visual line that the\n // given line is part of (usually that is the line itself).\n function visualLine(line) {\n var merged;\n while (merged = collapsedSpanAtStart(line))\n line = merged.find(-1, true).line;\n return line;\n }\n\n // Returns an array of logical lines that continue the visual line\n // started by the argument, or undefined if there are no such lines.\n function visualLineContinued(line) {\n var merged, lines;\n while (merged = collapsedSpanAtEnd(line)) {\n line = merged.find(1, true).line;\n (lines || (lines = [])).push(line);\n }\n return lines;\n }\n\n // Get the line number of the start of the visual line that the\n // given line number is part of.\n function visualLineNo(doc, lineN) {\n var line = getLine(doc, lineN), vis = visualLine(line);\n if (line == vis) return lineN;\n return lineNo(vis);\n }\n // Get the line number of the start of the next visual line after\n // the given line.\n function visualLineEndNo(doc, lineN) {\n if (lineN > doc.lastLine()) return lineN;\n var line = getLine(doc, lineN), merged;\n if (!lineIsHidden(doc, line)) return lineN;\n while (merged = collapsedSpanAtEnd(line))\n line = merged.find(1, true).line;\n return lineNo(line) + 1;\n }\n\n // Compute whether a line is hidden. Lines count as hidden when they\n // are part of a visual line that starts with another line, or when\n // they are entirely covered by collapsed, non-widget span.\n function lineIsHidden(doc, line) {\n var sps = sawCollapsedSpans && line.markedSpans;\n if (sps) for (var sp, i = 0; i < sps.length; ++i) {\n sp = sps[i];\n if (!sp.marker.collapsed) continue;\n if (sp.from == null) return true;\n if (sp.marker.widgetNode) continue;\n if (sp.from == 0 && sp.marker.inclusiveLeft && lineIsHiddenInner(doc, line, sp))\n return true;\n }\n }\n function lineIsHiddenInner(doc, line, span) {\n if (span.to == null) {\n var end = span.marker.find(1, true);\n return lineIsHiddenInner(doc, end.line, getMarkedSpanFor(end.line.markedSpans, span.marker));\n }\n if (span.marker.inclusiveRight && span.to == line.text.length)\n return true;\n for (var sp, i = 0; i < line.markedSpans.length; ++i) {\n sp = line.markedSpans[i];\n if (sp.marker.collapsed && !sp.marker.widgetNode && sp.from == span.to &&\n (sp.to == null || sp.to != span.from) &&\n (sp.marker.inclusiveLeft || span.marker.inclusiveRight) &&\n lineIsHiddenInner(doc, line, sp)) return true;\n }\n }\n\n // LINE WIDGETS\n\n // Line widgets are block elements displayed above or below a line.\n\n var LineWidget = CodeMirror.LineWidget = function(doc, node, options) {\n if (options) for (var opt in options) if (options.hasOwnProperty(opt))\n this[opt] = options[opt];\n this.doc = doc;\n this.node = node;\n };\n eventMixin(LineWidget);\n\n function adjustScrollWhenAboveVisible(cm, line, diff) {\n if (heightAtLine(line) < ((cm.curOp && cm.curOp.scrollTop) || cm.doc.scrollTop))\n addToScrollPos(cm, null, diff);\n }\n\n LineWidget.prototype.clear = function() {\n var cm = this.doc.cm, ws = this.line.widgets, line = this.line, no = lineNo(line);\n if (no == null || !ws) return;\n for (var i = 0; i < ws.length; ++i) if (ws[i] == this) ws.splice(i--, 1);\n if (!ws.length) line.widgets = null;\n var height = widgetHeight(this);\n updateLineHeight(line, Math.max(0, line.height - height));\n if (cm) runInOp(cm, function() {\n adjustScrollWhenAboveVisible(cm, line, -height);\n regLineChange(cm, no, \"widget\");\n });\n };\n LineWidget.prototype.changed = function() {\n var oldH = this.height, cm = this.doc.cm, line = this.line;\n this.height = null;\n var diff = widgetHeight(this) - oldH;\n if (!diff) return;\n updateLineHeight(line, line.height + diff);\n if (cm) runInOp(cm, function() {\n cm.curOp.forceUpdate = true;\n adjustScrollWhenAboveVisible(cm, line, diff);\n });\n };\n\n function widgetHeight(widget) {\n if (widget.height != null) return widget.height;\n var cm = widget.doc.cm;\n if (!cm) return 0;\n if (!contains(document.body, widget.node)) {\n var parentStyle = \"position: relative;\";\n if (widget.coverGutter)\n parentStyle += \"margin-left: -\" + cm.display.gutters.offsetWidth + \"px;\";\n if (widget.noHScroll)\n parentStyle += \"width: \" + cm.display.wrapper.clientWidth + \"px;\";\n removeChildrenAndAdd(cm.display.measure, elt(\"div\", [widget.node], null, parentStyle));\n }\n return widget.height = widget.node.parentNode.offsetHeight;\n }\n\n function addLineWidget(doc, handle, node, options) {\n var widget = new LineWidget(doc, node, options);\n var cm = doc.cm;\n if (cm && widget.noHScroll) cm.display.alignWidgets = true;\n changeLine(doc, handle, \"widget\", function(line) {\n var widgets = line.widgets || (line.widgets = []);\n if (widget.insertAt == null) widgets.push(widget);\n else widgets.splice(Math.min(widgets.length - 1, Math.max(0, widget.insertAt)), 0, widget);\n widget.line = line;\n if (cm && !lineIsHidden(doc, line)) {\n var aboveVisible = heightAtLine(line) < doc.scrollTop;\n updateLineHeight(line, line.height + widgetHeight(widget));\n if (aboveVisible) addToScrollPos(cm, null, widget.height);\n cm.curOp.forceUpdate = true;\n }\n return true;\n });\n return widget;\n }\n\n // LINE DATA STRUCTURE\n\n // Line objects. These hold state related to a line, including\n // highlighting info (the styles array).\n var Line = CodeMirror.Line = function(text, markedSpans, estimateHeight) {\n this.text = text;\n attachMarkedSpans(this, markedSpans);\n this.height = estimateHeight ? estimateHeight(this) : 1;\n };\n eventMixin(Line);\n Line.prototype.lineNo = function() { return lineNo(this); };\n\n // Change the content (text, markers) of a line. Automatically\n // invalidates cached information and tries to re-estimate the\n // line's height.\n function updateLine(line, text, markedSpans, estimateHeight) {\n line.text = text;\n if (line.stateAfter) line.stateAfter = null;\n if (line.styles) line.styles = null;\n if (line.order != null) line.order = null;\n detachMarkedSpans(line);\n attachMarkedSpans(line, markedSpans);\n var estHeight = estimateHeight ? estimateHeight(line) : 1;\n if (estHeight != line.height) updateLineHeight(line, estHeight);\n }\n\n // Detach a line from the document tree and its markers.\n function cleanUpLine(line) {\n line.parent = null;\n detachMarkedSpans(line);\n }\n\n function extractLineClasses(type, output) {\n if (type) for (;;) {\n var lineClass = type.match(/(?:^|\\s+)line-(background-)?(\\S+)/);\n if (!lineClass) break;\n type = type.slice(0, lineClass.index) + type.slice(lineClass.index + lineClass[0].length);\n var prop = lineClass[1] ? \"bgClass\" : \"textClass\";\n if (output[prop] == null)\n output[prop] = lineClass[2];\n else if (!(new RegExp(\"(?:^|\\s)\" + lineClass[2] + \"(?:$|\\s)\")).test(output[prop]))\n output[prop] += \" \" + lineClass[2];\n }\n return type;\n }\n\n function callBlankLine(mode, state) {\n if (mode.blankLine) return mode.blankLine(state);\n if (!mode.innerMode) return;\n var inner = CodeMirror.innerMode(mode, state);\n if (inner.mode.blankLine) return inner.mode.blankLine(inner.state);\n }\n\n function readToken(mode, stream, state, inner) {\n for (var i = 0; i < 10; i++) {\n if (inner) inner[0] = CodeMirror.innerMode(mode, state).mode;\n var style = mode.token(stream, state);\n if (stream.pos > stream.start) return style;\n }\n throw new Error(\"Mode \" + mode.name + \" failed to advance stream.\");\n }\n\n // Utility for getTokenAt and getLineTokens\n function takeToken(cm, pos, precise, asArray) {\n function getObj(copy) {\n return {start: stream.start, end: stream.pos,\n string: stream.current(),\n type: style || null,\n state: copy ? copyState(doc.mode, state) : state};\n }\n\n var doc = cm.doc, mode = doc.mode, style;\n pos = clipPos(doc, pos);\n var line = getLine(doc, pos.line), state = getStateBefore(cm, pos.line, precise);\n var stream = new StringStream(line.text, cm.options.tabSize), tokens;\n if (asArray) tokens = [];\n while ((asArray || stream.pos < pos.ch) && !stream.eol()) {\n stream.start = stream.pos;\n style = readToken(mode, stream, state);\n if (asArray) tokens.push(getObj(true));\n }\n return asArray ? tokens : getObj();\n }\n\n // Run the given mode's parser over a line, calling f for each token.\n function runMode(cm, text, mode, state, f, lineClasses, forceToEnd) {\n var flattenSpans = mode.flattenSpans;\n if (flattenSpans == null) flattenSpans = cm.options.flattenSpans;\n var curStart = 0, curStyle = null;\n var stream = new StringStream(text, cm.options.tabSize), style;\n var inner = cm.options.addModeClass && [null];\n if (text == \"\") extractLineClasses(callBlankLine(mode, state), lineClasses);\n while (!stream.eol()) {\n if (stream.pos > cm.options.maxHighlightLength) {\n flattenSpans = false;\n if (forceToEnd) processLine(cm, text, state, stream.pos);\n stream.pos = text.length;\n style = null;\n } else {\n style = extractLineClasses(readToken(mode, stream, state, inner), lineClasses);\n }\n if (inner) {\n var mName = inner[0].name;\n if (mName) style = \"m-\" + (style ? mName + \" \" + style : mName);\n }\n if (!flattenSpans || curStyle != style) {\n while (curStart < stream.start) {\n curStart = Math.min(stream.start, curStart + 50000);\n f(curStart, curStyle);\n }\n curStyle = style;\n }\n stream.start = stream.pos;\n }\n while (curStart < stream.pos) {\n // Webkit seems to refuse to render text nodes longer than 57444 characters\n var pos = Math.min(stream.pos, curStart + 50000);\n f(pos, curStyle);\n curStart = pos;\n }\n }\n\n // Compute a style array (an array starting with a mode generation\n // -- for invalidation -- followed by pairs of end positions and\n // style strings), which is used to highlight the tokens on the\n // line.\n function highlightLine(cm, line, state, forceToEnd) {\n // A styles array always starts with a number identifying the\n // mode/overlays that it is based on (for easy invalidation).\n var st = [cm.state.modeGen], lineClasses = {};\n // Compute the base array of styles\n runMode(cm, line.text, cm.doc.mode, state, function(end, style) {\n st.push(end, style);\n }, lineClasses, forceToEnd);\n\n // Run overlays, adjust style array.\n for (var o = 0; o < cm.state.overlays.length; ++o) {\n var overlay = cm.state.overlays[o], i = 1, at = 0;\n runMode(cm, line.text, overlay.mode, true, function(end, style) {\n var start = i;\n // Ensure there's a token end at the current position, and that i points at it\n while (at < end) {\n var i_end = st[i];\n if (i_end > end)\n st.splice(i, 1, end, st[i+1], i_end);\n i += 2;\n at = Math.min(end, i_end);\n }\n if (!style) return;\n if (overlay.opaque) {\n st.splice(start, i - start, end, \"cm-overlay \" + style);\n i = start + 2;\n } else {\n for (; start < i; start += 2) {\n var cur = st[start+1];\n st[start+1] = (cur ? cur + \" \" : \"\") + \"cm-overlay \" + style;\n }\n }\n }, lineClasses);\n }\n\n return {styles: st, classes: lineClasses.bgClass || lineClasses.textClass ? lineClasses : null};\n }\n\n function getLineStyles(cm, line, updateFrontier) {\n if (!line.styles || line.styles[0] != cm.state.modeGen) {\n var state = getStateBefore(cm, lineNo(line));\n var result = highlightLine(cm, line, line.text.length > cm.options.maxHighlightLength ? copyState(cm.doc.mode, state) : state);\n line.stateAfter = state;\n line.styles = result.styles;\n if (result.classes) line.styleClasses = result.classes;\n else if (line.styleClasses) line.styleClasses = null;\n if (updateFrontier === cm.doc.frontier) cm.doc.frontier++;\n }\n return line.styles;\n }\n\n // Lightweight form of highlight -- proceed over this line and\n // update state, but don't save a style array. Used for lines that\n // aren't currently visible.\n function processLine(cm, text, state, startAt) {\n var mode = cm.doc.mode;\n var stream = new StringStream(text, cm.options.tabSize);\n stream.start = stream.pos = startAt || 0;\n if (text == \"\") callBlankLine(mode, state);\n while (!stream.eol()) {\n readToken(mode, stream, state);\n stream.start = stream.pos;\n }\n }\n\n // Convert a style as returned by a mode (either null, or a string\n // containing one or more styles) to a CSS style. This is cached,\n // and also looks for line-wide styles.\n var styleToClassCache = {}, styleToClassCacheWithMode = {};\n function interpretTokenStyle(style, options) {\n if (!style || /^\\s*$/.test(style)) return null;\n var cache = options.addModeClass ? styleToClassCacheWithMode : styleToClassCache;\n return cache[style] ||\n (cache[style] = style.replace(/\\S+/g, \"cm-$&\"));\n }\n\n // Render the DOM representation of the text of a line. Also builds\n // up a 'line map', which points at the DOM nodes that represent\n // specific stretches of text, and is used by the measuring code.\n // The returned object contains the DOM node, this map, and\n // information about line-wide styles that were set by the mode.\n function buildLineContent(cm, lineView) {\n // The padding-right forces the element to have a 'border', which\n // is needed on Webkit to be able to get line-level bounding\n // rectangles for it (in measureChar).\n var content = elt(\"span\", null, null, webkit ? \"padding-right: .1px\" : null);\n var builder = {pre: elt(\"pre\", [content], \"CodeMirror-line\"), content: content,\n col: 0, pos: 0, cm: cm,\n splitSpaces: (ie || webkit) && cm.getOption(\"lineWrapping\")};\n lineView.measure = {};\n\n // Iterate over the logical lines that make up this visual line.\n for (var i = 0; i <= (lineView.rest ? lineView.rest.length : 0); i++) {\n var line = i ? lineView.rest[i - 1] : lineView.line, order;\n builder.pos = 0;\n builder.addToken = buildToken;\n // Optionally wire in some hacks into the token-rendering\n // algorithm, to deal with browser quirks.\n if (hasBadBidiRects(cm.display.measure) && (order = getOrder(line)))\n builder.addToken = buildTokenBadBidi(builder.addToken, order);\n builder.map = [];\n var allowFrontierUpdate = lineView != cm.display.externalMeasured && lineNo(line);\n insertLineContent(line, builder, getLineStyles(cm, line, allowFrontierUpdate));\n if (line.styleClasses) {\n if (line.styleClasses.bgClass)\n builder.bgClass = joinClasses(line.styleClasses.bgClass, builder.bgClass || \"\");\n if (line.styleClasses.textClass)\n builder.textClass = joinClasses(line.styleClasses.textClass, builder.textClass || \"\");\n }\n\n // Ensure at least a single node is present, for measuring.\n if (builder.map.length == 0)\n builder.map.push(0, 0, builder.content.appendChild(zeroWidthElement(cm.display.measure)));\n\n // Store the map and a cache object for the current logical line\n if (i == 0) {\n lineView.measure.map = builder.map;\n lineView.measure.cache = {};\n } else {\n (lineView.measure.maps || (lineView.measure.maps = [])).push(builder.map);\n (lineView.measure.caches || (lineView.measure.caches = [])).push({});\n }\n }\n\n // See issue #2901\n if (webkit && /\\bcm-tab\\b/.test(builder.content.lastChild.className))\n builder.content.className = \"cm-tab-wrap-hack\";\n\n signal(cm, \"renderLine\", cm, lineView.line, builder.pre);\n if (builder.pre.className)\n builder.textClass = joinClasses(builder.pre.className, builder.textClass || \"\");\n\n return builder;\n }\n\n function defaultSpecialCharPlaceholder(ch) {\n var token = elt(\"span\", \"\\u2022\", \"cm-invalidchar\");\n token.title = \"\\\\u\" + ch.charCodeAt(0).toString(16);\n token.setAttribute(\"aria-label\", token.title);\n return token;\n }\n\n // Build up the DOM representation for a single token, and add it to\n // the line map. Takes care to render special characters separately.\n function buildToken(builder, text, style, startStyle, endStyle, title, css) {\n if (!text) return;\n var displayText = builder.splitSpaces ? text.replace(/ {3,}/g, splitSpaces) : text;\n var special = builder.cm.state.specialChars, mustWrap = false;\n if (!special.test(text)) {\n builder.col += text.length;\n var content = document.createTextNode(displayText);\n builder.map.push(builder.pos, builder.pos + text.length, content);\n if (ie && ie_version < 9) mustWrap = true;\n builder.pos += text.length;\n } else {\n var content = document.createDocumentFragment(), pos = 0;\n while (true) {\n special.lastIndex = pos;\n var m = special.exec(text);\n var skipped = m ? m.index - pos : text.length - pos;\n if (skipped) {\n var txt = document.createTextNode(displayText.slice(pos, pos + skipped));\n if (ie && ie_version < 9) content.appendChild(elt(\"span\", [txt]));\n else content.appendChild(txt);\n builder.map.push(builder.pos, builder.pos + skipped, txt);\n builder.col += skipped;\n builder.pos += skipped;\n }\n if (!m) break;\n pos += skipped + 1;\n if (m[0] == \"\\t\") {\n var tabSize = builder.cm.options.tabSize, tabWidth = tabSize - builder.col % tabSize;\n var txt = content.appendChild(elt(\"span\", spaceStr(tabWidth), \"cm-tab\"));\n txt.setAttribute(\"role\", \"presentation\");\n txt.setAttribute(\"cm-text\", \"\\t\");\n builder.col += tabWidth;\n } else if (m[0] == \"\\r\" || m[0] == \"\\n\") {\n var txt = content.appendChild(elt(\"span\", m[0] == \"\\r\" ? \"\\u240d\" : \"\\u2424\", \"cm-invalidchar\"));\n txt.setAttribute(\"cm-text\", m[0]);\n builder.col += 1;\n } else {\n var txt = builder.cm.options.specialCharPlaceholder(m[0]);\n txt.setAttribute(\"cm-text\", m[0]);\n if (ie && ie_version < 9) content.appendChild(elt(\"span\", [txt]));\n else content.appendChild(txt);\n builder.col += 1;\n }\n builder.map.push(builder.pos, builder.pos + 1, txt);\n builder.pos++;\n }\n }\n if (style || startStyle || endStyle || mustWrap || css) {\n var fullStyle = style || \"\";\n if (startStyle) fullStyle += startStyle;\n if (endStyle) fullStyle += endStyle;\n var token = elt(\"span\", [content], fullStyle, css);\n if (title) token.title = title;\n return builder.content.appendChild(token);\n }\n builder.content.appendChild(content);\n }\n\n function splitSpaces(old) {\n var out = \" \";\n for (var i = 0; i < old.length - 2; ++i) out += i % 2 ? \" \" : \"\\u00a0\";\n out += \" \";\n return out;\n }\n\n // Work around nonsense dimensions being reported for stretches of\n // right-to-left text.\n function buildTokenBadBidi(inner, order) {\n return function(builder, text, style, startStyle, endStyle, title, css) {\n style = style ? style + \" cm-force-border\" : \"cm-force-border\";\n var start = builder.pos, end = start + text.length;\n for (;;) {\n // Find the part that overlaps with the start of this text\n for (var i = 0; i < order.length; i++) {\n var part = order[i];\n if (part.to > start && part.from <= start) break;\n }\n if (part.to >= end) return inner(builder, text, style, startStyle, endStyle, title, css);\n inner(builder, text.slice(0, part.to - start), style, startStyle, null, title, css);\n startStyle = null;\n text = text.slice(part.to - start);\n start = part.to;\n }\n };\n }\n\n function buildCollapsedSpan(builder, size, marker, ignoreWidget) {\n var widget = !ignoreWidget && marker.widgetNode;\n if (widget) builder.map.push(builder.pos, builder.pos + size, widget);\n if (!ignoreWidget && builder.cm.display.input.needsContentAttribute) {\n if (!widget)\n widget = builder.content.appendChild(document.createElement(\"span\"));\n widget.setAttribute(\"cm-marker\", marker.id);\n }\n if (widget) {\n builder.cm.display.input.setUneditable(widget);\n builder.content.appendChild(widget);\n }\n builder.pos += size;\n }\n\n // Outputs a number of spans to make up a line, taking highlighting\n // and marked text into account.\n function insertLineContent(line, builder, styles) {\n var spans = line.markedSpans, allText = line.text, at = 0;\n if (!spans) {\n for (var i = 1; i < styles.length; i+=2)\n builder.addToken(builder, allText.slice(at, at = styles[i]), interpretTokenStyle(styles[i+1], builder.cm.options));\n return;\n }\n\n var len = allText.length, pos = 0, i = 1, text = \"\", style, css;\n var nextChange = 0, spanStyle, spanEndStyle, spanStartStyle, title, collapsed;\n for (;;) {\n if (nextChange == pos) { // Update current marker set\n spanStyle = spanEndStyle = spanStartStyle = title = css = \"\";\n collapsed = null; nextChange = Infinity;\n var foundBookmarks = [], endStyles\n for (var j = 0; j < spans.length; ++j) {\n var sp = spans[j], m = sp.marker;\n if (m.type == \"bookmark\" && sp.from == pos && m.widgetNode) {\n foundBookmarks.push(m);\n } else if (sp.from <= pos && (sp.to == null || sp.to > pos || m.collapsed && sp.to == pos && sp.from == pos)) {\n if (sp.to != null && sp.to != pos && nextChange > sp.to) {\n nextChange = sp.to;\n spanEndStyle = \"\";\n }\n if (m.className) spanStyle += \" \" + m.className;\n if (m.css) css = (css ? css + \";\" : \"\") + m.css;\n if (m.startStyle && sp.from == pos) spanStartStyle += \" \" + m.startStyle;\n if (m.endStyle && sp.to == nextChange) (endStyles || (endStyles = [])).push(m.endStyle, sp.to)\n if (m.title && !title) title = m.title;\n if (m.collapsed && (!collapsed || compareCollapsedMarkers(collapsed.marker, m) < 0))\n collapsed = sp;\n } else if (sp.from > pos && nextChange > sp.from) {\n nextChange = sp.from;\n }\n }\n if (endStyles) for (var j = 0; j < endStyles.length; j += 2)\n if (endStyles[j + 1] == nextChange) spanEndStyle += \" \" + endStyles[j]\n\n if (!collapsed || collapsed.from == pos) for (var j = 0; j < foundBookmarks.length; ++j)\n buildCollapsedSpan(builder, 0, foundBookmarks[j]);\n if (collapsed && (collapsed.from || 0) == pos) {\n buildCollapsedSpan(builder, (collapsed.to == null ? len + 1 : collapsed.to) - pos,\n collapsed.marker, collapsed.from == null);\n if (collapsed.to == null) return;\n if (collapsed.to == pos) collapsed = false;\n }\n }\n if (pos >= len) break;\n\n var upto = Math.min(len, nextChange);\n while (true) {\n if (text) {\n var end = pos + text.length;\n if (!collapsed) {\n var tokenText = end > upto ? text.slice(0, upto - pos) : text;\n builder.addToken(builder, tokenText, style ? style + spanStyle : spanStyle,\n spanStartStyle, pos + tokenText.length == nextChange ? spanEndStyle : \"\", title, css);\n }\n if (end >= upto) {text = text.slice(upto - pos); pos = upto; break;}\n pos = end;\n spanStartStyle = \"\";\n }\n text = allText.slice(at, at = styles[i++]);\n style = interpretTokenStyle(styles[i++], builder.cm.options);\n }\n }\n }\n\n // DOCUMENT DATA STRUCTURE\n\n // By default, updates that start and end at the beginning of a line\n // are treated specially, in order to make the association of line\n // widgets and marker elements with the text behave more intuitive.\n function isWholeLineUpdate(doc, change) {\n return change.from.ch == 0 && change.to.ch == 0 && lst(change.text) == \"\" &&\n (!doc.cm || doc.cm.options.wholeLineUpdateBefore);\n }\n\n // Perform a change on the document data structure.\n function updateDoc(doc, change, markedSpans, estimateHeight) {\n function spansFor(n) {return markedSpans ? markedSpans[n] : null;}\n function update(line, text, spans) {\n updateLine(line, text, spans, estimateHeight);\n signalLater(line, \"change\", line, change);\n }\n function linesFor(start, end) {\n for (var i = start, result = []; i < end; ++i)\n result.push(new Line(text[i], spansFor(i), estimateHeight));\n return result;\n }\n\n var from = change.from, to = change.to, text = change.text;\n var firstLine = getLine(doc, from.line), lastLine = getLine(doc, to.line);\n var lastText = lst(text), lastSpans = spansFor(text.length - 1), nlines = to.line - from.line;\n\n // Adjust the line structure\n if (change.full) {\n doc.insert(0, linesFor(0, text.length));\n doc.remove(text.length, doc.size - text.length);\n } else if (isWholeLineUpdate(doc, change)) {\n // This is a whole-line replace. Treated specially to make\n // sure line objects move the way they are supposed to.\n var added = linesFor(0, text.length - 1);\n update(lastLine, lastLine.text, lastSpans);\n if (nlines) doc.remove(from.line, nlines);\n if (added.length) doc.insert(from.line, added);\n } else if (firstLine == lastLine) {\n if (text.length == 1) {\n update(firstLine, firstLine.text.slice(0, from.ch) + lastText + firstLine.text.slice(to.ch), lastSpans);\n } else {\n var added = linesFor(1, text.length - 1);\n added.push(new Line(lastText + firstLine.text.slice(to.ch), lastSpans, estimateHeight));\n update(firstLine, firstLine.text.slice(0, from.ch) + text[0], spansFor(0));\n doc.insert(from.line + 1, added);\n }\n } else if (text.length == 1) {\n update(firstLine, firstLine.text.slice(0, from.ch) + text[0] + lastLine.text.slice(to.ch), spansFor(0));\n doc.remove(from.line + 1, nlines);\n } else {\n update(firstLine, firstLine.text.slice(0, from.ch) + text[0], spansFor(0));\n update(lastLine, lastText + lastLine.text.slice(to.ch), lastSpans);\n var added = linesFor(1, text.length - 1);\n if (nlines > 1) doc.remove(from.line + 1, nlines - 1);\n doc.insert(from.line + 1, added);\n }\n\n signalLater(doc, \"change\", doc, change);\n }\n\n // The document is represented as a BTree consisting of leaves, with\n // chunk of lines in them, and branches, with up to ten leaves or\n // other branch nodes below them. The top node is always a branch\n // node, and is the document object itself (meaning it has\n // additional methods and properties).\n //\n // All nodes have parent links. The tree is used both to go from\n // line numbers to line objects, and to go from objects to numbers.\n // It also indexes by height, and is used to convert between height\n // and line object, and to find the total height of the document.\n //\n // See also http://marijnhaverbeke.nl/blog/codemirror-line-tree.html\n\n function LeafChunk(lines) {\n this.lines = lines;\n this.parent = null;\n for (var i = 0, height = 0; i < lines.length; ++i) {\n lines[i].parent = this;\n height += lines[i].height;\n }\n this.height = height;\n }\n\n LeafChunk.prototype = {\n chunkSize: function() { return this.lines.length; },\n // Remove the n lines at offset 'at'.\n removeInner: function(at, n) {\n for (var i = at, e = at + n; i < e; ++i) {\n var line = this.lines[i];\n this.height -= line.height;\n cleanUpLine(line);\n signalLater(line, \"delete\");\n }\n this.lines.splice(at, n);\n },\n // Helper used to collapse a small branch into a single leaf.\n collapse: function(lines) {\n lines.push.apply(lines, this.lines);\n },\n // Insert the given array of lines at offset 'at', count them as\n // having the given height.\n insertInner: function(at, lines, height) {\n this.height += height;\n this.lines = this.lines.slice(0, at).concat(lines).concat(this.lines.slice(at));\n for (var i = 0; i < lines.length; ++i) lines[i].parent = this;\n },\n // Used to iterate over a part of the tree.\n iterN: function(at, n, op) {\n for (var e = at + n; at < e; ++at)\n if (op(this.lines[at])) return true;\n }\n };\n\n function BranchChunk(children) {\n this.children = children;\n var size = 0, height = 0;\n for (var i = 0; i < children.length; ++i) {\n var ch = children[i];\n size += ch.chunkSize(); height += ch.height;\n ch.parent = this;\n }\n this.size = size;\n this.height = height;\n this.parent = null;\n }\n\n BranchChunk.prototype = {\n chunkSize: function() { return this.size; },\n removeInner: function(at, n) {\n this.size -= n;\n for (var i = 0; i < this.children.length; ++i) {\n var child = this.children[i], sz = child.chunkSize();\n if (at < sz) {\n var rm = Math.min(n, sz - at), oldHeight = child.height;\n child.removeInner(at, rm);\n this.height -= oldHeight - child.height;\n if (sz == rm) { this.children.splice(i--, 1); child.parent = null; }\n if ((n -= rm) == 0) break;\n at = 0;\n } else at -= sz;\n }\n // If the result is smaller than 25 lines, ensure that it is a\n // single leaf node.\n if (this.size - n < 25 &&\n (this.children.length > 1 || !(this.children[0] instanceof LeafChunk))) {\n var lines = [];\n this.collapse(lines);\n this.children = [new LeafChunk(lines)];\n this.children[0].parent = this;\n }\n },\n collapse: function(lines) {\n for (var i = 0; i < this.children.length; ++i) this.children[i].collapse(lines);\n },\n insertInner: function(at, lines, height) {\n this.size += lines.length;\n this.height += height;\n for (var i = 0; i < this.children.length; ++i) {\n var child = this.children[i], sz = child.chunkSize();\n if (at <= sz) {\n child.insertInner(at, lines, height);\n if (child.lines && child.lines.length > 50) {\n while (child.lines.length > 50) {\n var spilled = child.lines.splice(child.lines.length - 25, 25);\n var newleaf = new LeafChunk(spilled);\n child.height -= newleaf.height;\n this.children.splice(i + 1, 0, newleaf);\n newleaf.parent = this;\n }\n this.maybeSpill();\n }\n break;\n }\n at -= sz;\n }\n },\n // When a node has grown, check whether it should be split.\n maybeSpill: function() {\n if (this.children.length <= 10) return;\n var me = this;\n do {\n var spilled = me.children.splice(me.children.length - 5, 5);\n var sibling = new BranchChunk(spilled);\n if (!me.parent) { // Become the parent node\n var copy = new BranchChunk(me.children);\n copy.parent = me;\n me.children = [copy, sibling];\n me = copy;\n } else {\n me.size -= sibling.size;\n me.height -= sibling.height;\n var myIndex = indexOf(me.parent.children, me);\n me.parent.children.splice(myIndex + 1, 0, sibling);\n }\n sibling.parent = me.parent;\n } while (me.children.length > 10);\n me.parent.maybeSpill();\n },\n iterN: function(at, n, op) {\n for (var i = 0; i < this.children.length; ++i) {\n var child = this.children[i], sz = child.chunkSize();\n if (at < sz) {\n var used = Math.min(n, sz - at);\n if (child.iterN(at, used, op)) return true;\n if ((n -= used) == 0) break;\n at = 0;\n } else at -= sz;\n }\n }\n };\n\n var nextDocId = 0;\n var Doc = CodeMirror.Doc = function(text, mode, firstLine, lineSep) {\n if (!(this instanceof Doc)) return new Doc(text, mode, firstLine, lineSep);\n if (firstLine == null) firstLine = 0;\n\n BranchChunk.call(this, [new LeafChunk([new Line(\"\", null)])]);\n this.first = firstLine;\n this.scrollTop = this.scrollLeft = 0;\n this.cantEdit = false;\n this.cleanGeneration = 1;\n this.frontier = firstLine;\n var start = Pos(firstLine, 0);\n this.sel = simpleSelection(start);\n this.history = new History(null);\n this.id = ++nextDocId;\n this.modeOption = mode;\n this.lineSep = lineSep;\n this.extend = false;\n\n if (typeof text == \"string\") text = this.splitLines(text);\n updateDoc(this, {from: start, to: start, text: text});\n setSelection(this, simpleSelection(start), sel_dontScroll);\n };\n\n Doc.prototype = createObj(BranchChunk.prototype, {\n constructor: Doc,\n // Iterate over the document. Supports two forms -- with only one\n // argument, it calls that for each line in the document. With\n // three, it iterates over the range given by the first two (with\n // the second being non-inclusive).\n iter: function(from, to, op) {\n if (op) this.iterN(from - this.first, to - from, op);\n else this.iterN(this.first, this.first + this.size, from);\n },\n\n // Non-public interface for adding and removing lines.\n insert: function(at, lines) {\n var height = 0;\n for (var i = 0; i < lines.length; ++i) height += lines[i].height;\n this.insertInner(at - this.first, lines, height);\n },\n remove: function(at, n) { this.removeInner(at - this.first, n); },\n\n // From here, the methods are part of the public interface. Most\n // are also available from CodeMirror (editor) instances.\n\n getValue: function(lineSep) {\n var lines = getLines(this, this.first, this.first + this.size);\n if (lineSep === false) return lines;\n return lines.join(lineSep || this.lineSeparator());\n },\n setValue: docMethodOp(function(code) {\n var top = Pos(this.first, 0), last = this.first + this.size - 1;\n makeChange(this, {from: top, to: Pos(last, getLine(this, last).text.length),\n text: this.splitLines(code), origin: \"setValue\", full: true}, true);\n setSelection(this, simpleSelection(top));\n }),\n replaceRange: function(code, from, to, origin) {\n from = clipPos(this, from);\n to = to ? clipPos(this, to) : from;\n replaceRange(this, code, from, to, origin);\n },\n getRange: function(from, to, lineSep) {\n var lines = getBetween(this, clipPos(this, from), clipPos(this, to));\n if (lineSep === false) return lines;\n return lines.join(lineSep || this.lineSeparator());\n },\n\n getLine: function(line) {var l = this.getLineHandle(line); return l && l.text;},\n\n getLineHandle: function(line) {if (isLine(this, line)) return getLine(this, line);},\n getLineNumber: function(line) {return lineNo(line);},\n\n getLineHandleVisualStart: function(line) {\n if (typeof line == \"number\") line = getLine(this, line);\n return visualLine(line);\n },\n\n lineCount: function() {return this.size;},\n firstLine: function() {return this.first;},\n lastLine: function() {return this.first + this.size - 1;},\n\n clipPos: function(pos) {return clipPos(this, pos);},\n\n getCursor: function(start) {\n var range = this.sel.primary(), pos;\n if (start == null || start == \"head\") pos = range.head;\n else if (start == \"anchor\") pos = range.anchor;\n else if (start == \"end\" || start == \"to\" || start === false) pos = range.to();\n else pos = range.from();\n return pos;\n },\n listSelections: function() { return this.sel.ranges; },\n somethingSelected: function() {return this.sel.somethingSelected();},\n\n setCursor: docMethodOp(function(line, ch, options) {\n setSimpleSelection(this, clipPos(this, typeof line == \"number\" ? Pos(line, ch || 0) : line), null, options);\n }),\n setSelection: docMethodOp(function(anchor, head, options) {\n setSimpleSelection(this, clipPos(this, anchor), clipPos(this, head || anchor), options);\n }),\n extendSelection: docMethodOp(function(head, other, options) {\n extendSelection(this, clipPos(this, head), other && clipPos(this, other), options);\n }),\n extendSelections: docMethodOp(function(heads, options) {\n extendSelections(this, clipPosArray(this, heads), options);\n }),\n extendSelectionsBy: docMethodOp(function(f, options) {\n var heads = map(this.sel.ranges, f);\n extendSelections(this, clipPosArray(this, heads), options);\n }),\n setSelections: docMethodOp(function(ranges, primary, options) {\n if (!ranges.length) return;\n for (var i = 0, out = []; i < ranges.length; i++)\n out[i] = new Range(clipPos(this, ranges[i].anchor),\n clipPos(this, ranges[i].head));\n if (primary == null) primary = Math.min(ranges.length - 1, this.sel.primIndex);\n setSelection(this, normalizeSelection(out, primary), options);\n }),\n addSelection: docMethodOp(function(anchor, head, options) {\n var ranges = this.sel.ranges.slice(0);\n ranges.push(new Range(clipPos(this, anchor), clipPos(this, head || anchor)));\n setSelection(this, normalizeSelection(ranges, ranges.length - 1), options);\n }),\n\n getSelection: function(lineSep) {\n var ranges = this.sel.ranges, lines;\n for (var i = 0; i < ranges.length; i++) {\n var sel = getBetween(this, ranges[i].from(), ranges[i].to());\n lines = lines ? lines.concat(sel) : sel;\n }\n if (lineSep === false) return lines;\n else return lines.join(lineSep || this.lineSeparator());\n },\n getSelections: function(lineSep) {\n var parts = [], ranges = this.sel.ranges;\n for (var i = 0; i < ranges.length; i++) {\n var sel = getBetween(this, ranges[i].from(), ranges[i].to());\n if (lineSep !== false) sel = sel.join(lineSep || this.lineSeparator());\n parts[i] = sel;\n }\n return parts;\n },\n replaceSelection: function(code, collapse, origin) {\n var dup = [];\n for (var i = 0; i < this.sel.ranges.length; i++)\n dup[i] = code;\n this.replaceSelections(dup, collapse, origin || \"+input\");\n },\n replaceSelections: docMethodOp(function(code, collapse, origin) {\n var changes = [], sel = this.sel;\n for (var i = 0; i < sel.ranges.length; i++) {\n var range = sel.ranges[i];\n changes[i] = {from: range.from(), to: range.to(), text: this.splitLines(code[i]), origin: origin};\n }\n var newSel = collapse && collapse != \"end\" && computeReplacedSel(this, changes, collapse);\n for (var i = changes.length - 1; i >= 0; i--)\n makeChange(this, changes[i]);\n if (newSel) setSelectionReplaceHistory(this, newSel);\n else if (this.cm) ensureCursorVisible(this.cm);\n }),\n undo: docMethodOp(function() {makeChangeFromHistory(this, \"undo\");}),\n redo: docMethodOp(function() {makeChangeFromHistory(this, \"redo\");}),\n undoSelection: docMethodOp(function() {makeChangeFromHistory(this, \"undo\", true);}),\n redoSelection: docMethodOp(function() {makeChangeFromHistory(this, \"redo\", true);}),\n\n setExtending: function(val) {this.extend = val;},\n getExtending: function() {return this.extend;},\n\n historySize: function() {\n var hist = this.history, done = 0, undone = 0;\n for (var i = 0; i < hist.done.length; i++) if (!hist.done[i].ranges) ++done;\n for (var i = 0; i < hist.undone.length; i++) if (!hist.undone[i].ranges) ++undone;\n return {undo: done, redo: undone};\n },\n clearHistory: function() {this.history = new History(this.history.maxGeneration);},\n\n markClean: function() {\n this.cleanGeneration = this.changeGeneration(true);\n },\n changeGeneration: function(forceSplit) {\n if (forceSplit)\n this.history.lastOp = this.history.lastSelOp = this.history.lastOrigin = null;\n return this.history.generation;\n },\n isClean: function (gen) {\n return this.history.generation == (gen || this.cleanGeneration);\n },\n\n getHistory: function() {\n return {done: copyHistoryArray(this.history.done),\n undone: copyHistoryArray(this.history.undone)};\n },\n setHistory: function(histData) {\n var hist = this.history = new History(this.history.maxGeneration);\n hist.done = copyHistoryArray(histData.done.slice(0), null, true);\n hist.undone = copyHistoryArray(histData.undone.slice(0), null, true);\n },\n\n addLineClass: docMethodOp(function(handle, where, cls) {\n return changeLine(this, handle, where == \"gutter\" ? \"gutter\" : \"class\", function(line) {\n var prop = where == \"text\" ? \"textClass\"\n : where == \"background\" ? \"bgClass\"\n : where == \"gutter\" ? \"gutterClass\" : \"wrapClass\";\n if (!line[prop]) line[prop] = cls;\n else if (classTest(cls).test(line[prop])) return false;\n else line[prop] += \" \" + cls;\n return true;\n });\n }),\n removeLineClass: docMethodOp(function(handle, where, cls) {\n return changeLine(this, handle, where == \"gutter\" ? \"gutter\" : \"class\", function(line) {\n var prop = where == \"text\" ? \"textClass\"\n : where == \"background\" ? \"bgClass\"\n : where == \"gutter\" ? \"gutterClass\" : \"wrapClass\";\n var cur = line[prop];\n if (!cur) return false;\n else if (cls == null) line[prop] = null;\n else {\n var found = cur.match(classTest(cls));\n if (!found) return false;\n var end = found.index + found[0].length;\n line[prop] = cur.slice(0, found.index) + (!found.index || end == cur.length ? \"\" : \" \") + cur.slice(end) || null;\n }\n return true;\n });\n }),\n\n addLineWidget: docMethodOp(function(handle, node, options) {\n return addLineWidget(this, handle, node, options);\n }),\n removeLineWidget: function(widget) { widget.clear(); },\n\n markText: function(from, to, options) {\n return markText(this, clipPos(this, from), clipPos(this, to), options, options && options.type || \"range\");\n },\n setBookmark: function(pos, options) {\n var realOpts = {replacedWith: options && (options.nodeType == null ? options.widget : options),\n insertLeft: options && options.insertLeft,\n clearWhenEmpty: false, shared: options && options.shared,\n handleMouseEvents: options && options.handleMouseEvents};\n pos = clipPos(this, pos);\n return markText(this, pos, pos, realOpts, \"bookmark\");\n },\n findMarksAt: function(pos) {\n pos = clipPos(this, pos);\n var markers = [], spans = getLine(this, pos.line).markedSpans;\n if (spans) for (var i = 0; i < spans.length; ++i) {\n var span = spans[i];\n if ((span.from == null || span.from <= pos.ch) &&\n (span.to == null || span.to >= pos.ch))\n markers.push(span.marker.parent || span.marker);\n }\n return markers;\n },\n findMarks: function(from, to, filter) {\n from = clipPos(this, from); to = clipPos(this, to);\n var found = [], lineNo = from.line;\n this.iter(from.line, to.line + 1, function(line) {\n var spans = line.markedSpans;\n if (spans) for (var i = 0; i < spans.length; i++) {\n var span = spans[i];\n if (!(span.to != null && lineNo == from.line && from.ch > span.to ||\n span.from == null && lineNo != from.line ||\n span.from != null && lineNo == to.line && span.from > to.ch) &&\n (!filter || filter(span.marker)))\n found.push(span.marker.parent || span.marker);\n }\n ++lineNo;\n });\n return found;\n },\n getAllMarks: function() {\n var markers = [];\n this.iter(function(line) {\n var sps = line.markedSpans;\n if (sps) for (var i = 0; i < sps.length; ++i)\n if (sps[i].from != null) markers.push(sps[i].marker);\n });\n return markers;\n },\n\n posFromIndex: function(off) {\n var ch, lineNo = this.first;\n this.iter(function(line) {\n var sz = line.text.length + 1;\n if (sz > off) { ch = off; return true; }\n off -= sz;\n ++lineNo;\n });\n return clipPos(this, Pos(lineNo, ch));\n },\n indexFromPos: function (coords) {\n coords = clipPos(this, coords);\n var index = coords.ch;\n if (coords.line < this.first || coords.ch < 0) return 0;\n this.iter(this.first, coords.line, function (line) {\n index += line.text.length + 1;\n });\n return index;\n },\n\n copy: function(copyHistory) {\n var doc = new Doc(getLines(this, this.first, this.first + this.size),\n this.modeOption, this.first, this.lineSep);\n doc.scrollTop = this.scrollTop; doc.scrollLeft = this.scrollLeft;\n doc.sel = this.sel;\n doc.extend = false;\n if (copyHistory) {\n doc.history.undoDepth = this.history.undoDepth;\n doc.setHistory(this.getHistory());\n }\n return doc;\n },\n\n linkedDoc: function(options) {\n if (!options) options = {};\n var from = this.first, to = this.first + this.size;\n if (options.from != null && options.from > from) from = options.from;\n if (options.to != null && options.to < to) to = options.to;\n var copy = new Doc(getLines(this, from, to), options.mode || this.modeOption, from, this.lineSep);\n if (options.sharedHist) copy.history = this.history;\n (this.linked || (this.linked = [])).push({doc: copy, sharedHist: options.sharedHist});\n copy.linked = [{doc: this, isParent: true, sharedHist: options.sharedHist}];\n copySharedMarkers(copy, findSharedMarkers(this));\n return copy;\n },\n unlinkDoc: function(other) {\n if (other instanceof CodeMirror) other = other.doc;\n if (this.linked) for (var i = 0; i < this.linked.length; ++i) {\n var link = this.linked[i];\n if (link.doc != other) continue;\n this.linked.splice(i, 1);\n other.unlinkDoc(this);\n detachSharedMarkers(findSharedMarkers(this));\n break;\n }\n // If the histories were shared, split them again\n if (other.history == this.history) {\n var splitIds = [other.id];\n linkedDocs(other, function(doc) {splitIds.push(doc.id);}, true);\n other.history = new History(null);\n other.history.done = copyHistoryArray(this.history.done, splitIds);\n other.history.undone = copyHistoryArray(this.history.undone, splitIds);\n }\n },\n iterLinkedDocs: function(f) {linkedDocs(this, f);},\n\n getMode: function() {return this.mode;},\n getEditor: function() {return this.cm;},\n\n splitLines: function(str) {\n if (this.lineSep) return str.split(this.lineSep);\n return splitLinesAuto(str);\n },\n lineSeparator: function() { return this.lineSep || \"\\n\"; }\n });\n\n // Public alias.\n Doc.prototype.eachLine = Doc.prototype.iter;\n\n // Set up methods on CodeMirror's prototype to redirect to the editor's document.\n var dontDelegate = \"iter insert remove copy getEditor constructor\".split(\" \");\n for (var prop in Doc.prototype) if (Doc.prototype.hasOwnProperty(prop) && indexOf(dontDelegate, prop) < 0)\n CodeMirror.prototype[prop] = (function(method) {\n return function() {return method.apply(this.doc, arguments);};\n })(Doc.prototype[prop]);\n\n eventMixin(Doc);\n\n // Call f for all linked documents.\n function linkedDocs(doc, f, sharedHistOnly) {\n function propagate(doc, skip, sharedHist) {\n if (doc.linked) for (var i = 0; i < doc.linked.length; ++i) {\n var rel = doc.linked[i];\n if (rel.doc == skip) continue;\n var shared = sharedHist && rel.sharedHist;\n if (sharedHistOnly && !shared) continue;\n f(rel.doc, shared);\n propagate(rel.doc, doc, shared);\n }\n }\n propagate(doc, null, true);\n }\n\n // Attach a document to an editor.\n function attachDoc(cm, doc) {\n if (doc.cm) throw new Error(\"This document is already in use.\");\n cm.doc = doc;\n doc.cm = cm;\n estimateLineHeights(cm);\n loadMode(cm);\n if (!cm.options.lineWrapping) findMaxLine(cm);\n cm.options.mode = doc.modeOption;\n regChange(cm);\n }\n\n // LINE UTILITIES\n\n // Find the line object corresponding to the given line number.\n function getLine(doc, n) {\n n -= doc.first;\n if (n < 0 || n >= doc.size) throw new Error(\"There is no line \" + (n + doc.first) + \" in the document.\");\n for (var chunk = doc; !chunk.lines;) {\n for (var i = 0;; ++i) {\n var child = chunk.children[i], sz = child.chunkSize();\n if (n < sz) { chunk = child; break; }\n n -= sz;\n }\n }\n return chunk.lines[n];\n }\n\n // Get the part of a document between two positions, as an array of\n // strings.\n function getBetween(doc, start, end) {\n var out = [], n = start.line;\n doc.iter(start.line, end.line + 1, function(line) {\n var text = line.text;\n if (n == end.line) text = text.slice(0, end.ch);\n if (n == start.line) text = text.slice(start.ch);\n out.push(text);\n ++n;\n });\n return out;\n }\n // Get the lines between from and to, as array of strings.\n function getLines(doc, from, to) {\n var out = [];\n doc.iter(from, to, function(line) { out.push(line.text); });\n return out;\n }\n\n // Update the height of a line, propagating the height change\n // upwards to parent nodes.\n function updateLineHeight(line, height) {\n var diff = height - line.height;\n if (diff) for (var n = line; n; n = n.parent) n.height += diff;\n }\n\n // Given a line object, find its line number by walking up through\n // its parent links.\n function lineNo(line) {\n if (line.parent == null) return null;\n var cur = line.parent, no = indexOf(cur.lines, line);\n for (var chunk = cur.parent; chunk; cur = chunk, chunk = chunk.parent) {\n for (var i = 0;; ++i) {\n if (chunk.children[i] == cur) break;\n no += chunk.children[i].chunkSize();\n }\n }\n return no + cur.first;\n }\n\n // Find the line at the given vertical position, using the height\n // information in the document tree.\n function lineAtHeight(chunk, h) {\n var n = chunk.first;\n outer: do {\n for (var i = 0; i < chunk.children.length; ++i) {\n var child = chunk.children[i], ch = child.height;\n if (h < ch) { chunk = child; continue outer; }\n h -= ch;\n n += child.chunkSize();\n }\n return n;\n } while (!chunk.lines);\n for (var i = 0; i < chunk.lines.length; ++i) {\n var line = chunk.lines[i], lh = line.height;\n if (h < lh) break;\n h -= lh;\n }\n return n + i;\n }\n\n\n // Find the height above the given line.\n function heightAtLine(lineObj) {\n lineObj = visualLine(lineObj);\n\n var h = 0, chunk = lineObj.parent;\n for (var i = 0; i < chunk.lines.length; ++i) {\n var line = chunk.lines[i];\n if (line == lineObj) break;\n else h += line.height;\n }\n for (var p = chunk.parent; p; chunk = p, p = chunk.parent) {\n for (var i = 0; i < p.children.length; ++i) {\n var cur = p.children[i];\n if (cur == chunk) break;\n else h += cur.height;\n }\n }\n return h;\n }\n\n // Get the bidi ordering for the given line (and cache it). Returns\n // false for lines that are fully left-to-right, and an array of\n // BidiSpan objects otherwise.\n function getOrder(line) {\n var order = line.order;\n if (order == null) order = line.order = bidiOrdering(line.text);\n return order;\n }\n\n // HISTORY\n\n function History(startGen) {\n // Arrays of change events and selections. Doing something adds an\n // event to done and clears undo. Undoing moves events from done\n // to undone, redoing moves them in the other direction.\n this.done = []; this.undone = [];\n this.undoDepth = Infinity;\n // Used to track when changes can be merged into a single undo\n // event\n this.lastModTime = this.lastSelTime = 0;\n this.lastOp = this.lastSelOp = null;\n this.lastOrigin = this.lastSelOrigin = null;\n // Used by the isClean() method\n this.generation = this.maxGeneration = startGen || 1;\n }\n\n // Create a history change event from an updateDoc-style change\n // object.\n function historyChangeFromChange(doc, change) {\n var histChange = {from: copyPos(change.from), to: changeEnd(change), text: getBetween(doc, change.from, change.to)};\n attachLocalSpans(doc, histChange, change.from.line, change.to.line + 1);\n linkedDocs(doc, function(doc) {attachLocalSpans(doc, histChange, change.from.line, change.to.line + 1);}, true);\n return histChange;\n }\n\n // Pop all selection events off the end of a history array. Stop at\n // a change event.\n function clearSelectionEvents(array) {\n while (array.length) {\n var last = lst(array);\n if (last.ranges) array.pop();\n else break;\n }\n }\n\n // Find the top change event in the history. Pop off selection\n // events that are in the way.\n function lastChangeEvent(hist, force) {\n if (force) {\n clearSelectionEvents(hist.done);\n return lst(hist.done);\n } else if (hist.done.length && !lst(hist.done).ranges) {\n return lst(hist.done);\n } else if (hist.done.length > 1 && !hist.done[hist.done.length - 2].ranges) {\n hist.done.pop();\n return lst(hist.done);\n }\n }\n\n // Register a change in the history. Merges changes that are within\n // a single operation, ore are close together with an origin that\n // allows merging (starting with \"+\") into a single event.\n function addChangeToHistory(doc, change, selAfter, opId) {\n var hist = doc.history;\n hist.undone.length = 0;\n var time = +new Date, cur;\n\n if ((hist.lastOp == opId ||\n hist.lastOrigin == change.origin && change.origin &&\n ((change.origin.charAt(0) == \"+\" && doc.cm && hist.lastModTime > time - doc.cm.options.historyEventDelay) ||\n change.origin.charAt(0) == \"*\")) &&\n (cur = lastChangeEvent(hist, hist.lastOp == opId))) {\n // Merge this change into the last event\n var last = lst(cur.changes);\n if (cmp(change.from, change.to) == 0 && cmp(change.from, last.to) == 0) {\n // Optimized case for simple insertion -- don't want to add\n // new changesets for every character typed\n last.to = changeEnd(change);\n } else {\n // Add new sub-event\n cur.changes.push(historyChangeFromChange(doc, change));\n }\n } else {\n // Can not be merged, start a new event.\n var before = lst(hist.done);\n if (!before || !before.ranges)\n pushSelectionToHistory(doc.sel, hist.done);\n cur = {changes: [historyChangeFromChange(doc, change)],\n generation: hist.generation};\n hist.done.push(cur);\n while (hist.done.length > hist.undoDepth) {\n hist.done.shift();\n if (!hist.done[0].ranges) hist.done.shift();\n }\n }\n hist.done.push(selAfter);\n hist.generation = ++hist.maxGeneration;\n hist.lastModTime = hist.lastSelTime = time;\n hist.lastOp = hist.lastSelOp = opId;\n hist.lastOrigin = hist.lastSelOrigin = change.origin;\n\n if (!last) signal(doc, \"historyAdded\");\n }\n\n function selectionEventCanBeMerged(doc, origin, prev, sel) {\n var ch = origin.charAt(0);\n return ch == \"*\" ||\n ch == \"+\" &&\n prev.ranges.length == sel.ranges.length &&\n prev.somethingSelected() == sel.somethingSelected() &&\n new Date - doc.history.lastSelTime <= (doc.cm ? doc.cm.options.historyEventDelay : 500);\n }\n\n // Called whenever the selection changes, sets the new selection as\n // the pending selection in the history, and pushes the old pending\n // selection into the 'done' array when it was significantly\n // different (in number of selected ranges, emptiness, or time).\n function addSelectionToHistory(doc, sel, opId, options) {\n var hist = doc.history, origin = options && options.origin;\n\n // A new event is started when the previous origin does not match\n // the current, or the origins don't allow matching. Origins\n // starting with * are always merged, those starting with + are\n // merged when similar and close together in time.\n if (opId == hist.lastSelOp ||\n (origin && hist.lastSelOrigin == origin &&\n (hist.lastModTime == hist.lastSelTime && hist.lastOrigin == origin ||\n selectionEventCanBeMerged(doc, origin, lst(hist.done), sel))))\n hist.done[hist.done.length - 1] = sel;\n else\n pushSelectionToHistory(sel, hist.done);\n\n hist.lastSelTime = +new Date;\n hist.lastSelOrigin = origin;\n hist.lastSelOp = opId;\n if (options && options.clearRedo !== false)\n clearSelectionEvents(hist.undone);\n }\n\n function pushSelectionToHistory(sel, dest) {\n var top = lst(dest);\n if (!(top && top.ranges && top.equals(sel)))\n dest.push(sel);\n }\n\n // Used to store marked span information in the history.\n function attachLocalSpans(doc, change, from, to) {\n var existing = change[\"spans_\" + doc.id], n = 0;\n doc.iter(Math.max(doc.first, from), Math.min(doc.first + doc.size, to), function(line) {\n if (line.markedSpans)\n (existing || (existing = change[\"spans_\" + doc.id] = {}))[n] = line.markedSpans;\n ++n;\n });\n }\n\n // When un/re-doing restores text containing marked spans, those\n // that have been explicitly cleared should not be restored.\n function removeClearedSpans(spans) {\n if (!spans) return null;\n for (var i = 0, out; i < spans.length; ++i) {\n if (spans[i].marker.explicitlyCleared) { if (!out) out = spans.slice(0, i); }\n else if (out) out.push(spans[i]);\n }\n return !out ? spans : out.length ? out : null;\n }\n\n // Retrieve and filter the old marked spans stored in a change event.\n function getOldSpans(doc, change) {\n var found = change[\"spans_\" + doc.id];\n if (!found) return null;\n for (var i = 0, nw = []; i < change.text.length; ++i)\n nw.push(removeClearedSpans(found[i]));\n return nw;\n }\n\n // Used both to provide a JSON-safe object in .getHistory, and, when\n // detaching a document, to split the history in two\n function copyHistoryArray(events, newGroup, instantiateSel) {\n for (var i = 0, copy = []; i < events.length; ++i) {\n var event = events[i];\n if (event.ranges) {\n copy.push(instantiateSel ? Selection.prototype.deepCopy.call(event) : event);\n continue;\n }\n var changes = event.changes, newChanges = [];\n copy.push({changes: newChanges});\n for (var j = 0; j < changes.length; ++j) {\n var change = changes[j], m;\n newChanges.push({from: change.from, to: change.to, text: change.text});\n if (newGroup) for (var prop in change) if (m = prop.match(/^spans_(\\d+)$/)) {\n if (indexOf(newGroup, Number(m[1])) > -1) {\n lst(newChanges)[prop] = change[prop];\n delete change[prop];\n }\n }\n }\n }\n return copy;\n }\n\n // Rebasing/resetting history to deal with externally-sourced changes\n\n function rebaseHistSelSingle(pos, from, to, diff) {\n if (to < pos.line) {\n pos.line += diff;\n } else if (from < pos.line) {\n pos.line = from;\n pos.ch = 0;\n }\n }\n\n // Tries to rebase an array of history events given a change in the\n // document. If the change touches the same lines as the event, the\n // event, and everything 'behind' it, is discarded. If the change is\n // before the event, the event's positions are updated. Uses a\n // copy-on-write scheme for the positions, to avoid having to\n // reallocate them all on every rebase, but also avoid problems with\n // shared position objects being unsafely updated.\n function rebaseHistArray(array, from, to, diff) {\n for (var i = 0; i < array.length; ++i) {\n var sub = array[i], ok = true;\n if (sub.ranges) {\n if (!sub.copied) { sub = array[i] = sub.deepCopy(); sub.copied = true; }\n for (var j = 0; j < sub.ranges.length; j++) {\n rebaseHistSelSingle(sub.ranges[j].anchor, from, to, diff);\n rebaseHistSelSingle(sub.ranges[j].head, from, to, diff);\n }\n continue;\n }\n for (var j = 0; j < sub.changes.length; ++j) {\n var cur = sub.changes[j];\n if (to < cur.from.line) {\n cur.from = Pos(cur.from.line + diff, cur.from.ch);\n cur.to = Pos(cur.to.line + diff, cur.to.ch);\n } else if (from <= cur.to.line) {\n ok = false;\n break;\n }\n }\n if (!ok) {\n array.splice(0, i + 1);\n i = 0;\n }\n }\n }\n\n function rebaseHist(hist, change) {\n var from = change.from.line, to = change.to.line, diff = change.text.length - (to - from) - 1;\n rebaseHistArray(hist.done, from, to, diff);\n rebaseHistArray(hist.undone, from, to, diff);\n }\n\n // EVENT UTILITIES\n\n // Due to the fact that we still support jurassic IE versions, some\n // compatibility wrappers are needed.\n\n var e_preventDefault = CodeMirror.e_preventDefault = function(e) {\n if (e.preventDefault) e.preventDefault();\n else e.returnValue = false;\n };\n var e_stopPropagation = CodeMirror.e_stopPropagation = function(e) {\n if (e.stopPropagation) e.stopPropagation();\n else e.cancelBubble = true;\n };\n function e_defaultPrevented(e) {\n return e.defaultPrevented != null ? e.defaultPrevented : e.returnValue == false;\n }\n var e_stop = CodeMirror.e_stop = function(e) {e_preventDefault(e); e_stopPropagation(e);};\n\n function e_target(e) {return e.target || e.srcElement;}\n function e_button(e) {\n var b = e.which;\n if (b == null) {\n if (e.button & 1) b = 1;\n else if (e.button & 2) b = 3;\n else if (e.button & 4) b = 2;\n }\n if (mac && e.ctrlKey && b == 1) b = 3;\n return b;\n }\n\n // EVENT HANDLING\n\n // Lightweight event framework. on/off also work on DOM nodes,\n // registering native DOM handlers.\n\n var on = CodeMirror.on = function(emitter, type, f) {\n if (emitter.addEventListener)\n emitter.addEventListener(type, f, false);\n else if (emitter.attachEvent)\n emitter.attachEvent(\"on\" + type, f);\n else {\n var map = emitter._handlers || (emitter._handlers = {});\n var arr = map[type] || (map[type] = []);\n arr.push(f);\n }\n };\n\n var noHandlers = []\n function getHandlers(emitter, type, copy) {\n var arr = emitter._handlers && emitter._handlers[type]\n if (copy) return arr && arr.length > 0 ? arr.slice() : noHandlers\n else return arr || noHandlers\n }\n\n var off = CodeMirror.off = function(emitter, type, f) {\n if (emitter.removeEventListener)\n emitter.removeEventListener(type, f, false);\n else if (emitter.detachEvent)\n emitter.detachEvent(\"on\" + type, f);\n else {\n var handlers = getHandlers(emitter, type, false)\n for (var i = 0; i < handlers.length; ++i)\n if (handlers[i] == f) { handlers.splice(i, 1); break; }\n }\n };\n\n var signal = CodeMirror.signal = function(emitter, type /*, values...*/) {\n var handlers = getHandlers(emitter, type, true)\n if (!handlers.length) return;\n var args = Array.prototype.slice.call(arguments, 2);\n for (var i = 0; i < handlers.length; ++i) handlers[i].apply(null, args);\n };\n\n var orphanDelayedCallbacks = null;\n\n // Often, we want to signal events at a point where we are in the\n // middle of some work, but don't want the handler to start calling\n // other methods on the editor, which might be in an inconsistent\n // state or simply not expect any other events to happen.\n // signalLater looks whether there are any handlers, and schedules\n // them to be executed when the last operation ends, or, if no\n // operation is active, when a timeout fires.\n function signalLater(emitter, type /*, values...*/) {\n var arr = getHandlers(emitter, type, false)\n if (!arr.length) return;\n var args = Array.prototype.slice.call(arguments, 2), list;\n if (operationGroup) {\n list = operationGroup.delayedCallbacks;\n } else if (orphanDelayedCallbacks) {\n list = orphanDelayedCallbacks;\n } else {\n list = orphanDelayedCallbacks = [];\n setTimeout(fireOrphanDelayed, 0);\n }\n function bnd(f) {return function(){f.apply(null, args);};};\n for (var i = 0; i < arr.length; ++i)\n list.push(bnd(arr[i]));\n }\n\n function fireOrphanDelayed() {\n var delayed = orphanDelayedCallbacks;\n orphanDelayedCallbacks = null;\n for (var i = 0; i < delayed.length; ++i) delayed[i]();\n }\n\n // The DOM events that CodeMirror handles can be overridden by\n // registering a (non-DOM) handler on the editor for the event name,\n // and preventDefault-ing the event in that handler.\n function signalDOMEvent(cm, e, override) {\n if (typeof e == \"string\")\n e = {type: e, preventDefault: function() { this.defaultPrevented = true; }};\n signal(cm, override || e.type, cm, e);\n return e_defaultPrevented(e) || e.codemirrorIgnore;\n }\n\n function signalCursorActivity(cm) {\n var arr = cm._handlers && cm._handlers.cursorActivity;\n if (!arr) return;\n var set = cm.curOp.cursorActivityHandlers || (cm.curOp.cursorActivityHandlers = []);\n for (var i = 0; i < arr.length; ++i) if (indexOf(set, arr[i]) == -1)\n set.push(arr[i]);\n }\n\n function hasHandler(emitter, type) {\n return getHandlers(emitter, type).length > 0\n }\n\n // Add on and off methods to a constructor's prototype, to make\n // registering events on such objects more convenient.\n function eventMixin(ctor) {\n ctor.prototype.on = function(type, f) {on(this, type, f);};\n ctor.prototype.off = function(type, f) {off(this, type, f);};\n }\n\n // MISC UTILITIES\n\n // Number of pixels added to scroller and sizer to hide scrollbar\n var scrollerGap = 30;\n\n // Returned or thrown by various protocols to signal 'I'm not\n // handling this'.\n var Pass = CodeMirror.Pass = {toString: function(){return \"CodeMirror.Pass\";}};\n\n // Reused option objects for setSelection & friends\n var sel_dontScroll = {scroll: false}, sel_mouse = {origin: \"*mouse\"}, sel_move = {origin: \"+move\"};\n\n function Delayed() {this.id = null;}\n Delayed.prototype.set = function(ms, f) {\n clearTimeout(this.id);\n this.id = setTimeout(f, ms);\n };\n\n // Counts the column offset in a string, taking tabs into account.\n // Used mostly to find indentation.\n var countColumn = CodeMirror.countColumn = function(string, end, tabSize, startIndex, startValue) {\n if (end == null) {\n end = string.search(/[^\\s\\u00a0]/);\n if (end == -1) end = string.length;\n }\n for (var i = startIndex || 0, n = startValue || 0;;) {\n var nextTab = string.indexOf(\"\\t\", i);\n if (nextTab < 0 || nextTab >= end)\n return n + (end - i);\n n += nextTab - i;\n n += tabSize - (n % tabSize);\n i = nextTab + 1;\n }\n };\n\n // The inverse of countColumn -- find the offset that corresponds to\n // a particular column.\n var findColumn = CodeMirror.findColumn = function(string, goal, tabSize) {\n for (var pos = 0, col = 0;;) {\n var nextTab = string.indexOf(\"\\t\", pos);\n if (nextTab == -1) nextTab = string.length;\n var skipped = nextTab - pos;\n if (nextTab == string.length || col + skipped >= goal)\n return pos + Math.min(skipped, goal - col);\n col += nextTab - pos;\n col += tabSize - (col % tabSize);\n pos = nextTab + 1;\n if (col >= goal) return pos;\n }\n }\n\n var spaceStrs = [\"\"];\n function spaceStr(n) {\n while (spaceStrs.length <= n)\n spaceStrs.push(lst(spaceStrs) + \" \");\n return spaceStrs[n];\n }\n\n function lst(arr) { return arr[arr.length-1]; }\n\n var selectInput = function(node) { node.select(); };\n if (ios) // Mobile Safari apparently has a bug where select() is broken.\n selectInput = function(node) { node.selectionStart = 0; node.selectionEnd = node.value.length; };\n else if (ie) // Suppress mysterious IE10 errors\n selectInput = function(node) { try { node.select(); } catch(_e) {} };\n\n function indexOf(array, elt) {\n for (var i = 0; i < array.length; ++i)\n if (array[i] == elt) return i;\n return -1;\n }\n function map(array, f) {\n var out = [];\n for (var i = 0; i < array.length; i++) out[i] = f(array[i], i);\n return out;\n }\n\n function nothing() {}\n\n function createObj(base, props) {\n var inst;\n if (Object.create) {\n inst = Object.create(base);\n } else {\n nothing.prototype = base;\n inst = new nothing();\n }\n if (props) copyObj(props, inst);\n return inst;\n };\n\n function copyObj(obj, target, overwrite) {\n if (!target) target = {};\n for (var prop in obj)\n if (obj.hasOwnProperty(prop) && (overwrite !== false || !target.hasOwnProperty(prop)))\n target[prop] = obj[prop];\n return target;\n }\n\n function bind(f) {\n var args = Array.prototype.slice.call(arguments, 1);\n return function(){return f.apply(null, args);};\n }\n\n var nonASCIISingleCaseWordChar = /[\\u00df\\u0587\\u0590-\\u05f4\\u0600-\\u06ff\\u3040-\\u309f\\u30a0-\\u30ff\\u3400-\\u4db5\\u4e00-\\u9fcc\\uac00-\\ud7af]/;\n var isWordCharBasic = CodeMirror.isWordChar = function(ch) {\n return /\\w/.test(ch) || ch > \"\\x80\" &&\n (ch.toUpperCase() != ch.toLowerCase() || nonASCIISingleCaseWordChar.test(ch));\n };\n function isWordChar(ch, helper) {\n if (!helper) return isWordCharBasic(ch);\n if (helper.source.indexOf(\"\\\\w\") > -1 && isWordCharBasic(ch)) return true;\n return helper.test(ch);\n }\n\n function isEmpty(obj) {\n for (var n in obj) if (obj.hasOwnProperty(n) && obj[n]) return false;\n return true;\n }\n\n // Extending unicode characters. A series of a non-extending char +\n // any number of extending chars is treated as a single unit as far\n // as editing and measuring is concerned. This is not fully correct,\n // since some scripts/fonts/browsers also treat other configurations\n // of code points as a group.\n var extendingChars = /[\\u0300-\\u036f\\u0483-\\u0489\\u0591-\\u05bd\\u05bf\\u05c1\\u05c2\\u05c4\\u05c5\\u05c7\\u0610-\\u061a\\u064b-\\u065e\\u0670\\u06d6-\\u06dc\\u06de-\\u06e4\\u06e7\\u06e8\\u06ea-\\u06ed\\u0711\\u0730-\\u074a\\u07a6-\\u07b0\\u07eb-\\u07f3\\u0816-\\u0819\\u081b-\\u0823\\u0825-\\u0827\\u0829-\\u082d\\u0900-\\u0902\\u093c\\u0941-\\u0948\\u094d\\u0951-\\u0955\\u0962\\u0963\\u0981\\u09bc\\u09be\\u09c1-\\u09c4\\u09cd\\u09d7\\u09e2\\u09e3\\u0a01\\u0a02\\u0a3c\\u0a41\\u0a42\\u0a47\\u0a48\\u0a4b-\\u0a4d\\u0a51\\u0a70\\u0a71\\u0a75\\u0a81\\u0a82\\u0abc\\u0ac1-\\u0ac5\\u0ac7\\u0ac8\\u0acd\\u0ae2\\u0ae3\\u0b01\\u0b3c\\u0b3e\\u0b3f\\u0b41-\\u0b44\\u0b4d\\u0b56\\u0b57\\u0b62\\u0b63\\u0b82\\u0bbe\\u0bc0\\u0bcd\\u0bd7\\u0c3e-\\u0c40\\u0c46-\\u0c48\\u0c4a-\\u0c4d\\u0c55\\u0c56\\u0c62\\u0c63\\u0cbc\\u0cbf\\u0cc2\\u0cc6\\u0ccc\\u0ccd\\u0cd5\\u0cd6\\u0ce2\\u0ce3\\u0d3e\\u0d41-\\u0d44\\u0d4d\\u0d57\\u0d62\\u0d63\\u0dca\\u0dcf\\u0dd2-\\u0dd4\\u0dd6\\u0ddf\\u0e31\\u0e34-\\u0e3a\\u0e47-\\u0e4e\\u0eb1\\u0eb4-\\u0eb9\\u0ebb\\u0ebc\\u0ec8-\\u0ecd\\u0f18\\u0f19\\u0f35\\u0f37\\u0f39\\u0f71-\\u0f7e\\u0f80-\\u0f84\\u0f86\\u0f87\\u0f90-\\u0f97\\u0f99-\\u0fbc\\u0fc6\\u102d-\\u1030\\u1032-\\u1037\\u1039\\u103a\\u103d\\u103e\\u1058\\u1059\\u105e-\\u1060\\u1071-\\u1074\\u1082\\u1085\\u1086\\u108d\\u109d\\u135f\\u1712-\\u1714\\u1732-\\u1734\\u1752\\u1753\\u1772\\u1773\\u17b7-\\u17bd\\u17c6\\u17c9-\\u17d3\\u17dd\\u180b-\\u180d\\u18a9\\u1920-\\u1922\\u1927\\u1928\\u1932\\u1939-\\u193b\\u1a17\\u1a18\\u1a56\\u1a58-\\u1a5e\\u1a60\\u1a62\\u1a65-\\u1a6c\\u1a73-\\u1a7c\\u1a7f\\u1b00-\\u1b03\\u1b34\\u1b36-\\u1b3a\\u1b3c\\u1b42\\u1b6b-\\u1b73\\u1b80\\u1b81\\u1ba2-\\u1ba5\\u1ba8\\u1ba9\\u1c2c-\\u1c33\\u1c36\\u1c37\\u1cd0-\\u1cd2\\u1cd4-\\u1ce0\\u1ce2-\\u1ce8\\u1ced\\u1dc0-\\u1de6\\u1dfd-\\u1dff\\u200c\\u200d\\u20d0-\\u20f0\\u2cef-\\u2cf1\\u2de0-\\u2dff\\u302a-\\u302f\\u3099\\u309a\\ua66f-\\ua672\\ua67c\\ua67d\\ua6f0\\ua6f1\\ua802\\ua806\\ua80b\\ua825\\ua826\\ua8c4\\ua8e0-\\ua8f1\\ua926-\\ua92d\\ua947-\\ua951\\ua980-\\ua982\\ua9b3\\ua9b6-\\ua9b9\\ua9bc\\uaa29-\\uaa2e\\uaa31\\uaa32\\uaa35\\uaa36\\uaa43\\uaa4c\\uaab0\\uaab2-\\uaab4\\uaab7\\uaab8\\uaabe\\uaabf\\uaac1\\uabe5\\uabe8\\uabed\\udc00-\\udfff\\ufb1e\\ufe00-\\ufe0f\\ufe20-\\ufe26\\uff9e\\uff9f]/;\n function isExtendingChar(ch) { return ch.charCodeAt(0) >= 768 && extendingChars.test(ch); }\n\n // DOM UTILITIES\n\n function elt(tag, content, className, style) {\n var e = document.createElement(tag);\n if (className) e.className = className;\n if (style) e.style.cssText = style;\n if (typeof content == \"string\") e.appendChild(document.createTextNode(content));\n else if (content) for (var i = 0; i < content.length; ++i) e.appendChild(content[i]);\n return e;\n }\n\n var range;\n if (document.createRange) range = function(node, start, end, endNode) {\n var r = document.createRange();\n r.setEnd(endNode || node, end);\n r.setStart(node, start);\n return r;\n };\n else range = function(node, start, end) {\n var r = document.body.createTextRange();\n try { r.moveToElementText(node.parentNode); }\n catch(e) { return r; }\n r.collapse(true);\n r.moveEnd(\"character\", end);\n r.moveStart(\"character\", start);\n return r;\n };\n\n function removeChildren(e) {\n for (var count = e.childNodes.length; count > 0; --count)\n e.removeChild(e.firstChild);\n return e;\n }\n\n function removeChildrenAndAdd(parent, e) {\n return removeChildren(parent).appendChild(e);\n }\n\n var contains = CodeMirror.contains = function(parent, child) {\n if (child.nodeType == 3) // Android browser always returns false when child is a textnode\n child = child.parentNode;\n if (parent.contains)\n return parent.contains(child);\n do {\n if (child.nodeType == 11) child = child.host;\n if (child == parent) return true;\n } while (child = child.parentNode);\n };\n\n function activeElt() {\n var activeElement = document.activeElement;\n while (activeElement && activeElement.root && activeElement.root.activeElement)\n activeElement = activeElement.root.activeElement;\n return activeElement;\n }\n // Older versions of IE throws unspecified error when touching\n // document.activeElement in some cases (during loading, in iframe)\n if (ie && ie_version < 11) activeElt = function() {\n try { return document.activeElement; }\n catch(e) { return document.body; }\n };\n\n function classTest(cls) { return new RegExp(\"(^|\\\\s)\" + cls + \"(?:$|\\\\s)\\\\s*\"); }\n var rmClass = CodeMirror.rmClass = function(node, cls) {\n var current = node.className;\n var match = classTest(cls).exec(current);\n if (match) {\n var after = current.slice(match.index + match[0].length);\n node.className = current.slice(0, match.index) + (after ? match[1] + after : \"\");\n }\n };\n var addClass = CodeMirror.addClass = function(node, cls) {\n var current = node.className;\n if (!classTest(cls).test(current)) node.className += (current ? \" \" : \"\") + cls;\n };\n function joinClasses(a, b) {\n var as = a.split(\" \");\n for (var i = 0; i < as.length; i++)\n if (as[i] && !classTest(as[i]).test(b)) b += \" \" + as[i];\n return b;\n }\n\n // WINDOW-WIDE EVENTS\n\n // These must be handled carefully, because naively registering a\n // handler for each editor will cause the editors to never be\n // garbage collected.\n\n function forEachCodeMirror(f) {\n if (!document.body.getElementsByClassName) return;\n var byClass = document.body.getElementsByClassName(\"CodeMirror\");\n for (var i = 0; i < byClass.length; i++) {\n var cm = byClass[i].CodeMirror;\n if (cm) f(cm);\n }\n }\n\n var globalsRegistered = false;\n function ensureGlobalHandlers() {\n if (globalsRegistered) return;\n registerGlobalHandlers();\n globalsRegistered = true;\n }\n function registerGlobalHandlers() {\n // When the window resizes, we need to refresh active editors.\n var resizeTimer;\n on(window, \"resize\", function() {\n if (resizeTimer == null) resizeTimer = setTimeout(function() {\n resizeTimer = null;\n forEachCodeMirror(onResize);\n }, 100);\n });\n // When the window loses focus, we want to show the editor as blurred\n on(window, \"blur\", function() {\n forEachCodeMirror(onBlur);\n });\n }\n\n // FEATURE DETECTION\n\n // Detect drag-and-drop\n var dragAndDrop = function() {\n // There is *some* kind of drag-and-drop support in IE6-8, but I\n // couldn't get it to work yet.\n if (ie && ie_version < 9) return false;\n var div = elt('div');\n return \"draggable\" in div || \"dragDrop\" in div;\n }();\n\n var zwspSupported;\n function zeroWidthElement(measure) {\n if (zwspSupported == null) {\n var test = elt(\"span\", \"\\u200b\");\n removeChildrenAndAdd(measure, elt(\"span\", [test, document.createTextNode(\"x\")]));\n if (measure.firstChild.offsetHeight != 0)\n zwspSupported = test.offsetWidth <= 1 && test.offsetHeight > 2 && !(ie && ie_version < 8);\n }\n var node = zwspSupported ? elt(\"span\", \"\\u200b\") :\n elt(\"span\", \"\\u00a0\", null, \"display: inline-block; width: 1px; margin-right: -1px\");\n node.setAttribute(\"cm-text\", \"\");\n return node;\n }\n\n // Feature-detect IE's crummy client rect reporting for bidi text\n var badBidiRects;\n function hasBadBidiRects(measure) {\n if (badBidiRects != null) return badBidiRects;\n var txt = removeChildrenAndAdd(measure, document.createTextNode(\"A\\u062eA\"));\n var r0 = range(txt, 0, 1).getBoundingClientRect();\n if (!r0 || r0.left == r0.right) return false; // Safari returns null in some cases (#2780)\n var r1 = range(txt, 1, 2).getBoundingClientRect();\n return badBidiRects = (r1.right - r0.right < 3);\n }\n\n // See if \"\".split is the broken IE version, if so, provide an\n // alternative way to split lines.\n var splitLinesAuto = CodeMirror.splitLines = \"\\n\\nb\".split(/\\n/).length != 3 ? function(string) {\n var pos = 0, result = [], l = string.length;\n while (pos <= l) {\n var nl = string.indexOf(\"\\n\", pos);\n if (nl == -1) nl = string.length;\n var line = string.slice(pos, string.charAt(nl - 1) == \"\\r\" ? nl - 1 : nl);\n var rt = line.indexOf(\"\\r\");\n if (rt != -1) {\n result.push(line.slice(0, rt));\n pos += rt + 1;\n } else {\n result.push(line);\n pos = nl + 1;\n }\n }\n return result;\n } : function(string){return string.split(/\\r\\n?|\\n/);};\n\n var hasSelection = window.getSelection ? function(te) {\n try { return te.selectionStart != te.selectionEnd; }\n catch(e) { return false; }\n } : function(te) {\n try {var range = te.ownerDocument.selection.createRange();}\n catch(e) {}\n if (!range || range.parentElement() != te) return false;\n return range.compareEndPoints(\"StartToEnd\", range) != 0;\n };\n\n var hasCopyEvent = (function() {\n var e = elt(\"div\");\n if (\"oncopy\" in e) return true;\n e.setAttribute(\"oncopy\", \"return;\");\n return typeof e.oncopy == \"function\";\n })();\n\n var badZoomedRects = null;\n function hasBadZoomedRects(measure) {\n if (badZoomedRects != null) return badZoomedRects;\n var node = removeChildrenAndAdd(measure, elt(\"span\", \"x\"));\n var normal = node.getBoundingClientRect();\n var fromRange = range(node, 0, 1).getBoundingClientRect();\n return badZoomedRects = Math.abs(normal.left - fromRange.left) > 1;\n }\n\n // KEY NAMES\n\n var keyNames = CodeMirror.keyNames = {\n 3: \"Enter\", 8: \"Backspace\", 9: \"Tab\", 13: \"Enter\", 16: \"Shift\", 17: \"Ctrl\", 18: \"Alt\",\n 19: \"Pause\", 20: \"CapsLock\", 27: \"Esc\", 32: \"Space\", 33: \"PageUp\", 34: \"PageDown\", 35: \"End\",\n 36: \"Home\", 37: \"Left\", 38: \"Up\", 39: \"Right\", 40: \"Down\", 44: \"PrintScrn\", 45: \"Insert\",\n 46: \"Delete\", 59: \";\", 61: \"=\", 91: \"Mod\", 92: \"Mod\", 93: \"Mod\",\n 106: \"*\", 107: \"=\", 109: \"-\", 110: \".\", 111: \"/\", 127: \"Delete\",\n 173: \"-\", 186: \";\", 187: \"=\", 188: \",\", 189: \"-\", 190: \".\", 191: \"/\", 192: \"`\", 219: \"[\", 220: \"\\\\\",\n 221: \"]\", 222: \"'\", 63232: \"Up\", 63233: \"Down\", 63234: \"Left\", 63235: \"Right\", 63272: \"Delete\",\n 63273: \"Home\", 63275: \"End\", 63276: \"PageUp\", 63277: \"PageDown\", 63302: \"Insert\"\n };\n (function() {\n // Number keys\n for (var i = 0; i < 10; i++) keyNames[i + 48] = keyNames[i + 96] = String(i);\n // Alphabetic keys\n for (var i = 65; i <= 90; i++) keyNames[i] = String.fromCharCode(i);\n // Function keys\n for (var i = 1; i <= 12; i++) keyNames[i + 111] = keyNames[i + 63235] = \"F\" + i;\n })();\n\n // BIDI HELPERS\n\n function iterateBidiSections(order, from, to, f) {\n if (!order) return f(from, to, \"ltr\");\n var found = false;\n for (var i = 0; i < order.length; ++i) {\n var part = order[i];\n if (part.from < to && part.to > from || from == to && part.to == from) {\n f(Math.max(part.from, from), Math.min(part.to, to), part.level == 1 ? \"rtl\" : \"ltr\");\n found = true;\n }\n }\n if (!found) f(from, to, \"ltr\");\n }\n\n function bidiLeft(part) { return part.level % 2 ? part.to : part.from; }\n function bidiRight(part) { return part.level % 2 ? part.from : part.to; }\n\n function lineLeft(line) { var order = getOrder(line); return order ? bidiLeft(order[0]) : 0; }\n function lineRight(line) {\n var order = getOrder(line);\n if (!order) return line.text.length;\n return bidiRight(lst(order));\n }\n\n function lineStart(cm, lineN) {\n var line = getLine(cm.doc, lineN);\n var visual = visualLine(line);\n if (visual != line) lineN = lineNo(visual);\n var order = getOrder(visual);\n var ch = !order ? 0 : order[0].level % 2 ? lineRight(visual) : lineLeft(visual);\n return Pos(lineN, ch);\n }\n function lineEnd(cm, lineN) {\n var merged, line = getLine(cm.doc, lineN);\n while (merged = collapsedSpanAtEnd(line)) {\n line = merged.find(1, true).line;\n lineN = null;\n }\n var order = getOrder(line);\n var ch = !order ? line.text.length : order[0].level % 2 ? lineLeft(line) : lineRight(line);\n return Pos(lineN == null ? lineNo(line) : lineN, ch);\n }\n function lineStartSmart(cm, pos) {\n var start = lineStart(cm, pos.line);\n var line = getLine(cm.doc, start.line);\n var order = getOrder(line);\n if (!order || order[0].level == 0) {\n var firstNonWS = Math.max(0, line.text.search(/\\S/));\n var inWS = pos.line == start.line && pos.ch <= firstNonWS && pos.ch;\n return Pos(start.line, inWS ? 0 : firstNonWS);\n }\n return start;\n }\n\n function compareBidiLevel(order, a, b) {\n var linedir = order[0].level;\n if (a == linedir) return true;\n if (b == linedir) return false;\n return a < b;\n }\n var bidiOther;\n function getBidiPartAt(order, pos) {\n bidiOther = null;\n for (var i = 0, found; i < order.length; ++i) {\n var cur = order[i];\n if (cur.from < pos && cur.to > pos) return i;\n if ((cur.from == pos || cur.to == pos)) {\n if (found == null) {\n found = i;\n } else if (compareBidiLevel(order, cur.level, order[found].level)) {\n if (cur.from != cur.to) bidiOther = found;\n return i;\n } else {\n if (cur.from != cur.to) bidiOther = i;\n return found;\n }\n }\n }\n return found;\n }\n\n function moveInLine(line, pos, dir, byUnit) {\n if (!byUnit) return pos + dir;\n do pos += dir;\n while (pos > 0 && isExtendingChar(line.text.charAt(pos)));\n return pos;\n }\n\n // This is needed in order to move 'visually' through bi-directional\n // text -- i.e., pressing left should make the cursor go left, even\n // when in RTL text. The tricky part is the 'jumps', where RTL and\n // LTR text touch each other. This often requires the cursor offset\n // to move more than one unit, in order to visually move one unit.\n function moveVisually(line, start, dir, byUnit) {\n var bidi = getOrder(line);\n if (!bidi) return moveLogically(line, start, dir, byUnit);\n var pos = getBidiPartAt(bidi, start), part = bidi[pos];\n var target = moveInLine(line, start, part.level % 2 ? -dir : dir, byUnit);\n\n for (;;) {\n if (target > part.from && target < part.to) return target;\n if (target == part.from || target == part.to) {\n if (getBidiPartAt(bidi, target) == pos) return target;\n part = bidi[pos += dir];\n return (dir > 0) == part.level % 2 ? part.to : part.from;\n } else {\n part = bidi[pos += dir];\n if (!part) return null;\n if ((dir > 0) == part.level % 2)\n target = moveInLine(line, part.to, -1, byUnit);\n else\n target = moveInLine(line, part.from, 1, byUnit);\n }\n }\n }\n\n function moveLogically(line, start, dir, byUnit) {\n var target = start + dir;\n if (byUnit) while (target > 0 && isExtendingChar(line.text.charAt(target))) target += dir;\n return target < 0 || target > line.text.length ? null : target;\n }\n\n // Bidirectional ordering algorithm\n // See http://unicode.org/reports/tr9/tr9-13.html for the algorithm\n // that this (partially) implements.\n\n // One-char codes used for character types:\n // L (L): Left-to-Right\n // R (R): Right-to-Left\n // r (AL): Right-to-Left Arabic\n // 1 (EN): European Number\n // + (ES): European Number Separator\n // % (ET): European Number Terminator\n // n (AN): Arabic Number\n // , (CS): Common Number Separator\n // m (NSM): Non-Spacing Mark\n // b (BN): Boundary Neutral\n // s (B): Paragraph Separator\n // t (S): Segment Separator\n // w (WS): Whitespace\n // N (ON): Other Neutrals\n\n // Returns null if characters are ordered as they appear\n // (left-to-right), or an array of sections ({from, to, level}\n // objects) in the order in which they occur visually.\n var bidiOrdering = (function() {\n // Character types for codepoints 0 to 0xff\n var lowTypes = \"bbbbbbbbbtstwsbbbbbbbbbbbbbbssstwNN%%%NNNNNN,N,N1111111111NNNNNNNLLLLLLLLLLLLLLLLLLLLLLLLLLNNNNNNLLLLLLLLLLLLLLLLLLLLLLLLLLNNNNbbbbbbsbbbbbbbbbbbbbbbbbbbbbbbbbb,N%%%%NNNNLNNNNN%%11NLNNN1LNNNNNLLLLLLLLLLLLLLLLLLLLLLLNLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLLN\";\n // Character types for codepoints 0x600 to 0x6ff\n var arabicTypes = \"rrrrrrrrrrrr,rNNmmmmmmrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrmmmmmmmmmmmmmmrrrrrrrnnnnnnnnnn%nnrrrmrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrmmmmmmmmmmmmmmmmmmmNmmmm\";\n function charType(code) {\n if (code <= 0xf7) return lowTypes.charAt(code);\n else if (0x590 <= code && code <= 0x5f4) return \"R\";\n else if (0x600 <= code && code <= 0x6ed) return arabicTypes.charAt(code - 0x600);\n else if (0x6ee <= code && code <= 0x8ac) return \"r\";\n else if (0x2000 <= code && code <= 0x200b) return \"w\";\n else if (code == 0x200c) return \"b\";\n else return \"L\";\n }\n\n var bidiRE = /[\\u0590-\\u05f4\\u0600-\\u06ff\\u0700-\\u08ac]/;\n var isNeutral = /[stwN]/, isStrong = /[LRr]/, countsAsLeft = /[Lb1n]/, countsAsNum = /[1n]/;\n // Browsers seem to always treat the boundaries of block elements as being L.\n var outerType = \"L\";\n\n function BidiSpan(level, from, to) {\n this.level = level;\n this.from = from; this.to = to;\n }\n\n return function(str) {\n if (!bidiRE.test(str)) return false;\n var len = str.length, types = [];\n for (var i = 0, type; i < len; ++i)\n types.push(type = charType(str.charCodeAt(i)));\n\n // W1. Examine each non-spacing mark (NSM) in the level run, and\n // change the type of the NSM to the type of the previous\n // character. If the NSM is at the start of the level run, it will\n // get the type of sor.\n for (var i = 0, prev = outerType; i < len; ++i) {\n var type = types[i];\n if (type == \"m\") types[i] = prev;\n else prev = type;\n }\n\n // W2. Search backwards from each instance of a European number\n // until the first strong type (R, L, AL, or sor) is found. If an\n // AL is found, change the type of the European number to Arabic\n // number.\n // W3. Change all ALs to R.\n for (var i = 0, cur = outerType; i < len; ++i) {\n var type = types[i];\n if (type == \"1\" && cur == \"r\") types[i] = \"n\";\n else if (isStrong.test(type)) { cur = type; if (type == \"r\") types[i] = \"R\"; }\n }\n\n // W4. A single European separator between two European numbers\n // changes to a European number. A single common separator between\n // two numbers of the same type changes to that type.\n for (var i = 1, prev = types[0]; i < len - 1; ++i) {\n var type = types[i];\n if (type == \"+\" && prev == \"1\" && types[i+1] == \"1\") types[i] = \"1\";\n else if (type == \",\" && prev == types[i+1] &&\n (prev == \"1\" || prev == \"n\")) types[i] = prev;\n prev = type;\n }\n\n // W5. A sequence of European terminators adjacent to European\n // numbers changes to all European numbers.\n // W6. Otherwise, separators and terminators change to Other\n // Neutral.\n for (var i = 0; i < len; ++i) {\n var type = types[i];\n if (type == \",\") types[i] = \"N\";\n else if (type == \"%\") {\n for (var end = i + 1; end < len && types[end] == \"%\"; ++end) {}\n var replace = (i && types[i-1] == \"!\") || (end < len && types[end] == \"1\") ? \"1\" : \"N\";\n for (var j = i; j < end; ++j) types[j] = replace;\n i = end - 1;\n }\n }\n\n // W7. Search backwards from each instance of a European number\n // until the first strong type (R, L, or sor) is found. If an L is\n // found, then change the type of the European number to L.\n for (var i = 0, cur = outerType; i < len; ++i) {\n var type = types[i];\n if (cur == \"L\" && type == \"1\") types[i] = \"L\";\n else if (isStrong.test(type)) cur = type;\n }\n\n // N1. A sequence of neutrals takes the direction of the\n // surrounding strong text if the text on both sides has the same\n // direction. European and Arabic numbers act as if they were R in\n // terms of their influence on neutrals. Start-of-level-run (sor)\n // and end-of-level-run (eor) are used at level run boundaries.\n // N2. Any remaining neutrals take the embedding direction.\n for (var i = 0; i < len; ++i) {\n if (isNeutral.test(types[i])) {\n for (var end = i + 1; end < len && isNeutral.test(types[end]); ++end) {}\n var before = (i ? types[i-1] : outerType) == \"L\";\n var after = (end < len ? types[end] : outerType) == \"L\";\n var replace = before || after ? \"L\" : \"R\";\n for (var j = i; j < end; ++j) types[j] = replace;\n i = end - 1;\n }\n }\n\n // Here we depart from the documented algorithm, in order to avoid\n // building up an actual levels array. Since there are only three\n // levels (0, 1, 2) in an implementation that doesn't take\n // explicit embedding into account, we can build up the order on\n // the fly, without following the level-based algorithm.\n var order = [], m;\n for (var i = 0; i < len;) {\n if (countsAsLeft.test(types[i])) {\n var start = i;\n for (++i; i < len && countsAsLeft.test(types[i]); ++i) {}\n order.push(new BidiSpan(0, start, i));\n } else {\n var pos = i, at = order.length;\n for (++i; i < len && types[i] != \"L\"; ++i) {}\n for (var j = pos; j < i;) {\n if (countsAsNum.test(types[j])) {\n if (pos < j) order.splice(at, 0, new BidiSpan(1, pos, j));\n var nstart = j;\n for (++j; j < i && countsAsNum.test(types[j]); ++j) {}\n order.splice(at, 0, new BidiSpan(2, nstart, j));\n pos = j;\n } else ++j;\n }\n if (pos < i) order.splice(at, 0, new BidiSpan(1, pos, i));\n }\n }\n if (order[0].level == 1 && (m = str.match(/^\\s+/))) {\n order[0].from = m[0].length;\n order.unshift(new BidiSpan(0, 0, m[0].length));\n }\n if (lst(order).level == 1 && (m = str.match(/\\s+$/))) {\n lst(order).to -= m[0].length;\n order.push(new BidiSpan(0, len - m[0].length, len));\n }\n if (order[0].level == 2)\n order.unshift(new BidiSpan(1, order[0].to, order[0].to));\n if (order[0].level != lst(order).level)\n order.push(new BidiSpan(order[0].level, len, len));\n\n return order;\n };\n })();\n\n // THE END\n\n CodeMirror.version = \"5.13.2\";\n\n return CodeMirror;\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/lib/codemirror.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/lib/codemirror.css": {
"text": "/* BASICS */\n\n.CodeMirror {\n /* Set height, width, borders, and global font properties here */\n font-family: monospace;\n height: 300px;\n color: black;\n}\n\n/* PADDING */\n\n.CodeMirror-lines {\n padding: 4px 0; /* Vertical padding around content */\n}\n.CodeMirror pre {\n padding: 0 4px; /* Horizontal padding of content */\n}\n\n.CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {\n background-color: white; /* The little square between H and V scrollbars */\n}\n\n/* GUTTER */\n\n.CodeMirror-gutters {\n border-right: 1px solid #ddd;\n background-color: #f7f7f7;\n white-space: nowrap;\n}\n.CodeMirror-linenumbers {}\n.CodeMirror-linenumber {\n padding: 0 3px 0 5px;\n min-width: 20px;\n text-align: right;\n color: #999;\n white-space: nowrap;\n}\n\n.CodeMirror-guttermarker { color: black; }\n.CodeMirror-guttermarker-subtle { color: #999; }\n\n/* CURSOR */\n\n.CodeMirror-cursor {\n border-left: 1px solid black;\n border-right: none;\n width: 0;\n}\n/* Shown when moving in bi-directional text */\n.CodeMirror div.CodeMirror-secondarycursor {\n border-left: 1px solid silver;\n}\n.cm-fat-cursor .CodeMirror-cursor {\n width: auto;\n border: 0;\n background: #7e7;\n}\n.cm-fat-cursor div.CodeMirror-cursors {\n z-index: 1;\n}\n\n.cm-animate-fat-cursor {\n width: auto;\n border: 0;\n -webkit-animation: blink 1.06s steps(1) infinite;\n -moz-animation: blink 1.06s steps(1) infinite;\n animation: blink 1.06s steps(1) infinite;\n background-color: #7e7;\n}\n@-moz-keyframes blink {\n 0% {}\n 50% { background-color: transparent; }\n 100% {}\n}\n@-webkit-keyframes blink {\n 0% {}\n 50% { background-color: transparent; }\n 100% {}\n}\n@keyframes blink {\n 0% {}\n 50% { background-color: transparent; }\n 100% {}\n}\n\n/* Can style cursor different in overwrite (non-insert) mode */\n.CodeMirror-overwrite .CodeMirror-cursor {}\n\n.cm-tab { display: inline-block; text-decoration: inherit; }\n\n.CodeMirror-ruler {\n border-left: 1px solid #ccc;\n position: absolute;\n}\n\n/* DEFAULT THEME */\n\n.cm-s-default .cm-header {color: blue;}\n.cm-s-default .cm-quote {color: #090;}\n.cm-negative {color: #d44;}\n.cm-positive {color: #292;}\n.cm-header, .cm-strong {font-weight: bold;}\n.cm-em {font-style: italic;}\n.cm-link {text-decoration: underline;}\n.cm-strikethrough {text-decoration: line-through;}\n\n.cm-s-default .cm-keyword {color: #708;}\n.cm-s-default .cm-atom {color: #219;}\n.cm-s-default .cm-number {color: #164;}\n.cm-s-default .cm-def {color: #00f;}\n.cm-s-default .cm-variable,\n.cm-s-default .cm-punctuation,\n.cm-s-default .cm-property,\n.cm-s-default .cm-operator {}\n.cm-s-default .cm-variable-2 {color: #05a;}\n.cm-s-default .cm-variable-3 {color: #085;}\n.cm-s-default .cm-comment {color: #a50;}\n.cm-s-default .cm-string {color: #a11;}\n.cm-s-default .cm-string-2 {color: #f50;}\n.cm-s-default .cm-meta {color: #555;}\n.cm-s-default .cm-qualifier {color: #555;}\n.cm-s-default .cm-builtin {color: #30a;}\n.cm-s-default .cm-bracket {color: #997;}\n.cm-s-default .cm-tag {color: #170;}\n.cm-s-default .cm-attribute {color: #00c;}\n.cm-s-default .cm-hr {color: #999;}\n.cm-s-default .cm-link {color: #00c;}\n\n.cm-s-default .cm-error {color: #f00;}\n.cm-invalidchar {color: #f00;}\n\n.CodeMirror-composing { border-bottom: 2px solid; }\n\n/* Default styles for common addons */\n\ndiv.CodeMirror span.CodeMirror-matchingbracket {color: #0f0;}\ndiv.CodeMirror span.CodeMirror-nonmatchingbracket {color: #f22;}\n.CodeMirror-matchingtag { background: rgba(255, 150, 0, .3); }\n.CodeMirror-activeline-background {background: #e8f2ff;}\n\n/* STOP */\n\n/* The rest of this file contains styles related to the mechanics of\n the editor. You probably shouldn't touch them. */\n\n.CodeMirror {\n position: relative;\n overflow: hidden;\n background: white;\n}\n\n.CodeMirror-scroll {\n overflow: scroll !important; /* Things will break if this is overridden */\n /* 30px is the magic margin used to hide the element's real scrollbars */\n /* See overflow: hidden in .CodeMirror */\n margin-bottom: -30px; margin-right: -30px;\n padding-bottom: 30px;\n height: 100%;\n outline: none; /* Prevent dragging from highlighting the element */\n position: relative;\n}\n.CodeMirror-sizer {\n position: relative;\n border-right: 30px solid transparent;\n}\n\n/* The fake, visible scrollbars. Used to force redraw during scrolling\n before actual scrolling happens, thus preventing shaking and\n flickering artifacts. */\n.CodeMirror-vscrollbar, .CodeMirror-hscrollbar, .CodeMirror-scrollbar-filler, .CodeMirror-gutter-filler {\n position: absolute;\n z-index: 6;\n display: none;\n}\n.CodeMirror-vscrollbar {\n right: 0; top: 0;\n overflow-x: hidden;\n overflow-y: scroll;\n}\n.CodeMirror-hscrollbar {\n bottom: 0; left: 0;\n overflow-y: hidden;\n overflow-x: scroll;\n}\n.CodeMirror-scrollbar-filler {\n right: 0; bottom: 0;\n}\n.CodeMirror-gutter-filler {\n left: 0; bottom: 0;\n}\n\n.CodeMirror-gutters {\n position: absolute; left: 0; top: 0;\n min-height: 100%;\n z-index: 3;\n}\n.CodeMirror-gutter {\n white-space: normal;\n height: 100%;\n display: inline-block;\n vertical-align: top;\n margin-bottom: -30px;\n /* Hack to make IE7 behave */\n *zoom:1;\n *display:inline;\n}\n.CodeMirror-gutter-wrapper {\n position: absolute;\n z-index: 4;\n background: none !important;\n border: none !important;\n}\n.CodeMirror-gutter-background {\n position: absolute;\n top: 0; bottom: 0;\n z-index: 4;\n}\n.CodeMirror-gutter-elt {\n position: absolute;\n cursor: default;\n z-index: 4;\n}\n.CodeMirror-gutter-wrapper {\n -webkit-user-select: none;\n -moz-user-select: none;\n user-select: none;\n}\n\n.CodeMirror-lines {\n cursor: text;\n min-height: 1px; /* prevents collapsing before first draw */\n}\n.CodeMirror pre {\n /* Reset some styles that the rest of the page might have set */\n -moz-border-radius: 0; -webkit-border-radius: 0; border-radius: 0;\n border-width: 0;\n background: transparent;\n font-family: inherit;\n font-size: inherit;\n margin: 0;\n white-space: pre;\n word-wrap: normal;\n line-height: inherit;\n color: inherit;\n z-index: 2;\n position: relative;\n overflow: visible;\n -webkit-tap-highlight-color: transparent;\n -webkit-font-variant-ligatures: none;\n font-variant-ligatures: none;\n}\n.CodeMirror-wrap pre {\n word-wrap: break-word;\n white-space: pre-wrap;\n word-break: normal;\n}\n\n.CodeMirror-linebackground {\n position: absolute;\n left: 0; right: 0; top: 0; bottom: 0;\n z-index: 0;\n}\n\n.CodeMirror-linewidget {\n position: relative;\n z-index: 2;\n overflow: auto;\n}\n\n.CodeMirror-widget {}\n\n.CodeMirror-code {\n outline: none;\n}\n\n/* Force content-box sizing for the elements where we expect it */\n.CodeMirror-scroll,\n.CodeMirror-sizer,\n.CodeMirror-gutter,\n.CodeMirror-gutters,\n.CodeMirror-linenumber {\n -moz-box-sizing: content-box;\n box-sizing: content-box;\n}\n\n.CodeMirror-measure {\n position: absolute;\n width: 100%;\n height: 0;\n overflow: hidden;\n visibility: hidden;\n}\n\n.CodeMirror-cursor { position: absolute; }\n.CodeMirror-measure pre { position: static; }\n\ndiv.CodeMirror-cursors {\n visibility: hidden;\n position: relative;\n z-index: 3;\n}\ndiv.CodeMirror-dragcursors {\n visibility: visible;\n}\n\n.CodeMirror-focused div.CodeMirror-cursors {\n visibility: visible;\n}\n\n.CodeMirror-selected { background: #d9d9d9; }\n.CodeMirror-focused .CodeMirror-selected { background: #d7d4f0; }\n.CodeMirror-crosshair { cursor: crosshair; }\n.CodeMirror-line::selection, .CodeMirror-line > span::selection, .CodeMirror-line > span > span::selection { background: #d7d4f0; }\n.CodeMirror-line::-moz-selection, .CodeMirror-line > span::-moz-selection, .CodeMirror-line > span > span::-moz-selection { background: #d7d4f0; }\n\n.cm-searching {\n background: #ffa;\n background: rgba(255, 255, 0, .4);\n}\n\n/* IE7 hack to prevent it from returning funny offsetTops on the spans */\n.CodeMirror span { *vertical-align: text-bottom; }\n\n/* Used to force a border model for a node */\n.cm-force-border { padding-right: .1px; }\n\n@media print {\n /* Hide the cursor when printing */\n .CodeMirror div.CodeMirror-cursors {\n visibility: hidden;\n }\n}\n\n/* See issue #2901 */\n.cm-tab-wrap-hack:after { content: ''; }\n\n/* Help users use markselection to safely style text background */\nspan.CodeMirror-selectedtext { background: none; }\n",
"type": "text/css",
"title": "$:/plugins/tiddlywiki/codemirror/lib/codemirror.css",
"tags": "[[$:/tags/Stylesheet]]"
},
"$:/plugins/tiddlywiki/codemirror/addon/dialog/dialog.css": {
"text": ".CodeMirror-dialog {\n position: absolute;\n left: 0; right: 0;\n background: inherit;\n z-index: 15;\n padding: .1em .8em;\n overflow: hidden;\n color: inherit;\n}\n\n.CodeMirror-dialog-top {\n border-bottom: 1px solid #eee;\n top: 0;\n}\n\n.CodeMirror-dialog-bottom {\n border-top: 1px solid #eee;\n bottom: 0;\n}\n\n.CodeMirror-dialog input {\n border: none;\n outline: none;\n background: transparent;\n width: 20em;\n color: inherit;\n font-family: monospace;\n}\n\n.CodeMirror-dialog button {\n font-size: 70%;\n}\n",
"type": "text/css",
"title": "$:/plugins/tiddlywiki/codemirror/addon/dialog/dialog.css",
"tags": "[[$:/tags/Stylesheet]]"
},
"$:/plugins/tiddlywiki/codemirror/addon/dialog/dialog.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n// Open simple dialogs on top of an editor. Relies on dialog.css.\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n function dialogDiv(cm, template, bottom) {\n var wrap = cm.getWrapperElement();\n var dialog;\n dialog = wrap.appendChild(document.createElement(\"div\"));\n if (bottom)\n dialog.className = \"CodeMirror-dialog CodeMirror-dialog-bottom\";\n else\n dialog.className = \"CodeMirror-dialog CodeMirror-dialog-top\";\n\n if (typeof template == \"string\") {\n dialog.innerHTML = template;\n } else { // Assuming it's a detached DOM element.\n dialog.appendChild(template);\n }\n return dialog;\n }\n\n function closeNotification(cm, newVal) {\n if (cm.state.currentNotificationClose)\n cm.state.currentNotificationClose();\n cm.state.currentNotificationClose = newVal;\n }\n\n CodeMirror.defineExtension(\"openDialog\", function(template, callback, options) {\n if (!options) options = {};\n\n closeNotification(this, null);\n\n var dialog = dialogDiv(this, template, options.bottom);\n var closed = false, me = this;\n function close(newVal) {\n if (typeof newVal == 'string') {\n inp.value = newVal;\n } else {\n if (closed) return;\n closed = true;\n dialog.parentNode.removeChild(dialog);\n me.focus();\n\n if (options.onClose) options.onClose(dialog);\n }\n }\n\n var inp = dialog.getElementsByTagName(\"input\")[0], button;\n if (inp) {\n inp.focus();\n\n if (options.value) {\n inp.value = options.value;\n if (options.selectValueOnOpen !== false) {\n inp.select();\n }\n }\n\n if (options.onInput)\n CodeMirror.on(inp, \"input\", function(e) { options.onInput(e, inp.value, close);});\n if (options.onKeyUp)\n CodeMirror.on(inp, \"keyup\", function(e) {options.onKeyUp(e, inp.value, close);});\n\n CodeMirror.on(inp, \"keydown\", function(e) {\n if (options && options.onKeyDown && options.onKeyDown(e, inp.value, close)) { return; }\n if (e.keyCode == 27 || (options.closeOnEnter !== false && e.keyCode == 13)) {\n inp.blur();\n CodeMirror.e_stop(e);\n close();\n }\n if (e.keyCode == 13) callback(inp.value, e);\n });\n\n if (options.closeOnBlur !== false) CodeMirror.on(inp, \"blur\", close);\n } else if (button = dialog.getElementsByTagName(\"button\")[0]) {\n CodeMirror.on(button, \"click\", function() {\n close();\n me.focus();\n });\n\n if (options.closeOnBlur !== false) CodeMirror.on(button, \"blur\", close);\n\n button.focus();\n }\n return close;\n });\n\n CodeMirror.defineExtension(\"openConfirm\", function(template, callbacks, options) {\n closeNotification(this, null);\n var dialog = dialogDiv(this, template, options && options.bottom);\n var buttons = dialog.getElementsByTagName(\"button\");\n var closed = false, me = this, blurring = 1;\n function close() {\n if (closed) return;\n closed = true;\n dialog.parentNode.removeChild(dialog);\n me.focus();\n }\n buttons[0].focus();\n for (var i = 0; i < buttons.length; ++i) {\n var b = buttons[i];\n (function(callback) {\n CodeMirror.on(b, \"click\", function(e) {\n CodeMirror.e_preventDefault(e);\n close();\n if (callback) callback(me);\n });\n })(callbacks[i]);\n CodeMirror.on(b, \"blur\", function() {\n --blurring;\n setTimeout(function() { if (blurring <= 0) close(); }, 200);\n });\n CodeMirror.on(b, \"focus\", function() { ++blurring; });\n }\n });\n\n /*\n * openNotification\n * Opens a notification, that can be closed with an optional timer\n * (default 5000ms timer) and always closes on click.\n *\n * If a notification is opened while another is opened, it will close the\n * currently opened one and open the new one immediately.\n */\n CodeMirror.defineExtension(\"openNotification\", function(template, options) {\n closeNotification(this, close);\n var dialog = dialogDiv(this, template, options && options.bottom);\n var closed = false, doneTimer;\n var duration = options && typeof options.duration !== \"undefined\" ? options.duration : 5000;\n\n function close() {\n if (closed) return;\n closed = true;\n clearTimeout(doneTimer);\n dialog.parentNode.removeChild(dialog);\n }\n\n CodeMirror.on(dialog, 'click', function(e) {\n CodeMirror.e_preventDefault(e);\n close();\n });\n\n if (duration)\n doneTimer = setTimeout(close, duration);\n\n return close;\n });\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/dialog/dialog.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n var ie_lt8 = /MSIE \\d/.test(navigator.userAgent) &&\n (document.documentMode == null || document.documentMode < 8);\n\n var Pos = CodeMirror.Pos;\n\n var matching = {\"(\": \")>\", \")\": \"(<\", \"[\": \"]>\", \"]\": \"[<\", \"{\": \"}>\", \"}\": \"{<\"};\n\n function findMatchingBracket(cm, where, strict, config) {\n var line = cm.getLineHandle(where.line), pos = where.ch - 1;\n var match = (pos >= 0 && matching[line.text.charAt(pos)]) || matching[line.text.charAt(++pos)];\n if (!match) return null;\n var dir = match.charAt(1) == \">\" ? 1 : -1;\n if (strict && (dir > 0) != (pos == where.ch)) return null;\n var style = cm.getTokenTypeAt(Pos(where.line, pos + 1));\n\n var found = scanForBracket(cm, Pos(where.line, pos + (dir > 0 ? 1 : 0)), dir, style || null, config);\n if (found == null) return null;\n return {from: Pos(where.line, pos), to: found && found.pos,\n match: found && found.ch == match.charAt(0), forward: dir > 0};\n }\n\n // bracketRegex is used to specify which type of bracket to scan\n // should be a regexp, e.g. /[[\\]]/\n //\n // Note: If \"where\" is on an open bracket, then this bracket is ignored.\n //\n // Returns false when no bracket was found, null when it reached\n // maxScanLines and gave up\n function scanForBracket(cm, where, dir, style, config) {\n var maxScanLen = (config && config.maxScanLineLength) || 10000;\n var maxScanLines = (config && config.maxScanLines) || 1000;\n\n var stack = [];\n var re = config && config.bracketRegex ? config.bracketRegex : /[(){}[\\]]/;\n var lineEnd = dir > 0 ? Math.min(where.line + maxScanLines, cm.lastLine() + 1)\n : Math.max(cm.firstLine() - 1, where.line - maxScanLines);\n for (var lineNo = where.line; lineNo != lineEnd; lineNo += dir) {\n var line = cm.getLine(lineNo);\n if (!line) continue;\n var pos = dir > 0 ? 0 : line.length - 1, end = dir > 0 ? line.length : -1;\n if (line.length > maxScanLen) continue;\n if (lineNo == where.line) pos = where.ch - (dir < 0 ? 1 : 0);\n for (; pos != end; pos += dir) {\n var ch = line.charAt(pos);\n if (re.test(ch) && (style === undefined || cm.getTokenTypeAt(Pos(lineNo, pos + 1)) == style)) {\n var match = matching[ch];\n if ((match.charAt(1) == \">\") == (dir > 0)) stack.push(ch);\n else if (!stack.length) return {pos: Pos(lineNo, pos), ch: ch};\n else stack.pop();\n }\n }\n }\n return lineNo - dir == (dir > 0 ? cm.lastLine() : cm.firstLine()) ? false : null;\n }\n\n function matchBrackets(cm, autoclear, config) {\n // Disable brace matching in long lines, since it'll cause hugely slow updates\n var maxHighlightLen = cm.state.matchBrackets.maxHighlightLineLength || 1000;\n var marks = [], ranges = cm.listSelections();\n for (var i = 0; i < ranges.length; i++) {\n var match = ranges[i].empty() && findMatchingBracket(cm, ranges[i].head, false, config);\n if (match && cm.getLine(match.from.line).length <= maxHighlightLen) {\n var style = match.match ? \"CodeMirror-matchingbracket\" : \"CodeMirror-nonmatchingbracket\";\n marks.push(cm.markText(match.from, Pos(match.from.line, match.from.ch + 1), {className: style}));\n if (match.to && cm.getLine(match.to.line).length <= maxHighlightLen)\n marks.push(cm.markText(match.to, Pos(match.to.line, match.to.ch + 1), {className: style}));\n }\n }\n\n if (marks.length) {\n // Kludge to work around the IE bug from issue #1193, where text\n // input stops going to the textare whever this fires.\n if (ie_lt8 && cm.state.focused) cm.focus();\n\n var clear = function() {\n cm.operation(function() {\n for (var i = 0; i < marks.length; i++) marks[i].clear();\n });\n };\n if (autoclear) setTimeout(clear, 800);\n else return clear;\n }\n }\n\n var currentlyHighlighted = null;\n function doMatchBrackets(cm) {\n cm.operation(function() {\n if (currentlyHighlighted) {currentlyHighlighted(); currentlyHighlighted = null;}\n currentlyHighlighted = matchBrackets(cm, false, cm.state.matchBrackets);\n });\n }\n\n CodeMirror.defineOption(\"matchBrackets\", false, function(cm, val, old) {\n if (old && old != CodeMirror.Init)\n cm.off(\"cursorActivity\", doMatchBrackets);\n if (val) {\n cm.state.matchBrackets = typeof val == \"object\" ? val : {};\n cm.on(\"cursorActivity\", doMatchBrackets);\n }\n });\n\n CodeMirror.defineExtension(\"matchBrackets\", function() {matchBrackets(this, true);});\n CodeMirror.defineExtension(\"findMatchingBracket\", function(pos, strict, config){\n return findMatchingBracket(this, pos, strict, config);\n });\n CodeMirror.defineExtension(\"scanForBracket\", function(pos, dir, style, config){\n return scanForBracket(this, pos, dir, style, config);\n });\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/addon/mode/multiplex.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n\"use strict\";\n\nCodeMirror.multiplexingMode = function(outer /*, others */) {\n // Others should be {open, close, mode [, delimStyle] [, innerStyle]} objects\n var others = Array.prototype.slice.call(arguments, 1);\n\n function indexOf(string, pattern, from, returnEnd) {\n if (typeof pattern == \"string\") {\n var found = string.indexOf(pattern, from);\n return returnEnd && found > -1 ? found + pattern.length : found;\n }\n var m = pattern.exec(from ? string.slice(from) : string);\n return m ? m.index + from + (returnEnd ? m[0].length : 0) : -1;\n }\n\n return {\n startState: function() {\n return {\n outer: CodeMirror.startState(outer),\n innerActive: null,\n inner: null\n };\n },\n\n copyState: function(state) {\n return {\n outer: CodeMirror.copyState(outer, state.outer),\n innerActive: state.innerActive,\n inner: state.innerActive && CodeMirror.copyState(state.innerActive.mode, state.inner)\n };\n },\n\n token: function(stream, state) {\n if (!state.innerActive) {\n var cutOff = Infinity, oldContent = stream.string;\n for (var i = 0; i < others.length; ++i) {\n var other = others[i];\n var found = indexOf(oldContent, other.open, stream.pos);\n if (found == stream.pos) {\n if (!other.parseDelimiters) stream.match(other.open);\n state.innerActive = other;\n state.inner = CodeMirror.startState(other.mode, outer.indent ? outer.indent(state.outer, \"\") : 0);\n return other.delimStyle && (other.delimStyle + \" \" + other.delimStyle + \"-open\");\n } else if (found != -1 && found < cutOff) {\n cutOff = found;\n }\n }\n if (cutOff != Infinity) stream.string = oldContent.slice(0, cutOff);\n var outerToken = outer.token(stream, state.outer);\n if (cutOff != Infinity) stream.string = oldContent;\n return outerToken;\n } else {\n var curInner = state.innerActive, oldContent = stream.string;\n if (!curInner.close && stream.sol()) {\n state.innerActive = state.inner = null;\n return this.token(stream, state);\n }\n var found = curInner.close ? indexOf(oldContent, curInner.close, stream.pos, curInner.parseDelimiters) : -1;\n if (found == stream.pos && !curInner.parseDelimiters) {\n stream.match(curInner.close);\n state.innerActive = state.inner = null;\n return curInner.delimStyle && (curInner.delimStyle + \" \" + curInner.delimStyle + \"-close\");\n }\n if (found > -1) stream.string = oldContent.slice(0, found);\n var innerToken = curInner.mode.token(stream, state.inner);\n if (found > -1) stream.string = oldContent;\n\n if (found == stream.pos && curInner.parseDelimiters)\n state.innerActive = state.inner = null;\n\n if (curInner.innerStyle) {\n if (innerToken) innerToken = innerToken + \" \" + curInner.innerStyle;\n else innerToken = curInner.innerStyle;\n }\n\n return innerToken;\n }\n },\n\n indent: function(state, textAfter) {\n var mode = state.innerActive ? state.innerActive.mode : outer;\n if (!mode.indent) return CodeMirror.Pass;\n return mode.indent(state.innerActive ? state.inner : state.outer, textAfter);\n },\n\n blankLine: function(state) {\n var mode = state.innerActive ? state.innerActive.mode : outer;\n if (mode.blankLine) {\n mode.blankLine(state.innerActive ? state.inner : state.outer);\n }\n if (!state.innerActive) {\n for (var i = 0; i < others.length; ++i) {\n var other = others[i];\n if (other.open === \"\\n\") {\n state.innerActive = other;\n state.inner = CodeMirror.startState(other.mode, mode.indent ? mode.indent(state.outer, \"\") : 0);\n }\n }\n } else if (state.innerActive.close === \"\\n\") {\n state.innerActive = state.inner = null;\n }\n },\n\n electricChars: outer.electricChars,\n\n innerMode: function(state) {\n return state.inner ? {state: state.inner, mode: state.innerActive.mode} : {state: state.outer, mode: outer};\n }\n };\n};\n\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/mode/multiplex.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/addon/search/searchcursor.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n \"use strict\";\n var Pos = CodeMirror.Pos;\n\n function SearchCursor(doc, query, pos, caseFold) {\n this.atOccurrence = false; this.doc = doc;\n if (caseFold == null && typeof query == \"string\") caseFold = false;\n\n pos = pos ? doc.clipPos(pos) : Pos(0, 0);\n this.pos = {from: pos, to: pos};\n\n // The matches method is filled in based on the type of query.\n // It takes a position and a direction, and returns an object\n // describing the next occurrence of the query, or null if no\n // more matches were found.\n if (typeof query != \"string\") { // Regexp match\n if (!query.global) query = new RegExp(query.source, query.ignoreCase ? \"ig\" : \"g\");\n this.matches = function(reverse, pos) {\n if (reverse) {\n query.lastIndex = 0;\n var line = doc.getLine(pos.line).slice(0, pos.ch), cutOff = 0, match, start;\n for (;;) {\n query.lastIndex = cutOff;\n var newMatch = query.exec(line);\n if (!newMatch) break;\n match = newMatch;\n start = match.index;\n cutOff = match.index + (match[0].length || 1);\n if (cutOff == line.length) break;\n }\n var matchLen = (match && match[0].length) || 0;\n if (!matchLen) {\n if (start == 0 && line.length == 0) {match = undefined;}\n else if (start != doc.getLine(pos.line).length) {\n matchLen++;\n }\n }\n } else {\n query.lastIndex = pos.ch;\n var line = doc.getLine(pos.line), match = query.exec(line);\n var matchLen = (match && match[0].length) || 0;\n var start = match && match.index;\n if (start + matchLen != line.length && !matchLen) matchLen = 1;\n }\n if (match && matchLen)\n return {from: Pos(pos.line, start),\n to: Pos(pos.line, start + matchLen),\n match: match};\n };\n } else { // String query\n var origQuery = query;\n if (caseFold) query = query.toLowerCase();\n var fold = caseFold ? function(str){return str.toLowerCase();} : function(str){return str;};\n var target = query.split(\"\\n\");\n // Different methods for single-line and multi-line queries\n if (target.length == 1) {\n if (!query.length) {\n // Empty string would match anything and never progress, so\n // we define it to match nothing instead.\n this.matches = function() {};\n } else {\n this.matches = function(reverse, pos) {\n if (reverse) {\n var orig = doc.getLine(pos.line).slice(0, pos.ch), line = fold(orig);\n var match = line.lastIndexOf(query);\n if (match > -1) {\n match = adjustPos(orig, line, match);\n return {from: Pos(pos.line, match), to: Pos(pos.line, match + origQuery.length)};\n }\n } else {\n var orig = doc.getLine(pos.line).slice(pos.ch), line = fold(orig);\n var match = line.indexOf(query);\n if (match > -1) {\n match = adjustPos(orig, line, match) + pos.ch;\n return {from: Pos(pos.line, match), to: Pos(pos.line, match + origQuery.length)};\n }\n }\n };\n }\n } else {\n var origTarget = origQuery.split(\"\\n\");\n this.matches = function(reverse, pos) {\n var last = target.length - 1;\n if (reverse) {\n if (pos.line - (target.length - 1) < doc.firstLine()) return;\n if (fold(doc.getLine(pos.line).slice(0, origTarget[last].length)) != target[target.length - 1]) return;\n var to = Pos(pos.line, origTarget[last].length);\n for (var ln = pos.line - 1, i = last - 1; i >= 1; --i, --ln)\n if (target[i] != fold(doc.getLine(ln))) return;\n var line = doc.getLine(ln), cut = line.length - origTarget[0].length;\n if (fold(line.slice(cut)) != target[0]) return;\n return {from: Pos(ln, cut), to: to};\n } else {\n if (pos.line + (target.length - 1) > doc.lastLine()) return;\n var line = doc.getLine(pos.line), cut = line.length - origTarget[0].length;\n if (fold(line.slice(cut)) != target[0]) return;\n var from = Pos(pos.line, cut);\n for (var ln = pos.line + 1, i = 1; i < last; ++i, ++ln)\n if (target[i] != fold(doc.getLine(ln))) return;\n if (fold(doc.getLine(ln).slice(0, origTarget[last].length)) != target[last]) return;\n return {from: from, to: Pos(ln, origTarget[last].length)};\n }\n };\n }\n }\n }\n\n SearchCursor.prototype = {\n findNext: function() {return this.find(false);},\n findPrevious: function() {return this.find(true);},\n\n find: function(reverse) {\n var self = this, pos = this.doc.clipPos(reverse ? this.pos.from : this.pos.to);\n function savePosAndFail(line) {\n var pos = Pos(line, 0);\n self.pos = {from: pos, to: pos};\n self.atOccurrence = false;\n return false;\n }\n\n for (;;) {\n if (this.pos = this.matches(reverse, pos)) {\n this.atOccurrence = true;\n return this.pos.match || true;\n }\n if (reverse) {\n if (!pos.line) return savePosAndFail(0);\n pos = Pos(pos.line-1, this.doc.getLine(pos.line-1).length);\n }\n else {\n var maxLine = this.doc.lineCount();\n if (pos.line == maxLine - 1) return savePosAndFail(maxLine);\n pos = Pos(pos.line + 1, 0);\n }\n }\n },\n\n from: function() {if (this.atOccurrence) return this.pos.from;},\n to: function() {if (this.atOccurrence) return this.pos.to;},\n\n replace: function(newText, origin) {\n if (!this.atOccurrence) return;\n var lines = CodeMirror.splitLines(newText);\n this.doc.replaceRange(lines, this.pos.from, this.pos.to, origin);\n this.pos.to = Pos(this.pos.from.line + lines.length - 1,\n lines[lines.length - 1].length + (lines.length == 1 ? this.pos.from.ch : 0));\n }\n };\n\n // Maps a position in a case-folded line back to a position in the original line\n // (compensating for codepoints increasing in number during folding)\n function adjustPos(orig, folded, pos) {\n if (orig.length == folded.length) return pos;\n for (var pos1 = Math.min(pos, orig.length);;) {\n var len1 = orig.slice(0, pos1).toLowerCase().length;\n if (len1 < pos) ++pos1;\n else if (len1 > pos) --pos1;\n else return pos1;\n }\n }\n\n CodeMirror.defineExtension(\"getSearchCursor\", function(query, pos, caseFold) {\n return new SearchCursor(this.doc, query, pos, caseFold);\n });\n CodeMirror.defineDocExtension(\"getSearchCursor\", function(query, pos, caseFold) {\n return new SearchCursor(this, query, pos, caseFold);\n });\n\n CodeMirror.defineExtension(\"selectMatches\", function(query, caseFold) {\n var ranges = [];\n var cur = this.getSearchCursor(query, this.getCursor(\"from\"), caseFold);\n while (cur.findNext()) {\n if (CodeMirror.cmpPos(cur.to(), this.getCursor(\"to\")) > 0) break;\n ranges.push({anchor: cur.from(), head: cur.to()});\n }\n if (ranges.length)\n this.setSelections(ranges, 0);\n });\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/addon/search/searchcursor.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/mode/css/css.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n\"use strict\";\n\nCodeMirror.defineMode(\"css\", function(config, parserConfig) {\n var inline = parserConfig.inline\n if (!parserConfig.propertyKeywords) parserConfig = CodeMirror.resolveMode(\"text/css\");\n\n var indentUnit = config.indentUnit,\n tokenHooks = parserConfig.tokenHooks,\n documentTypes = parserConfig.documentTypes || {},\n mediaTypes = parserConfig.mediaTypes || {},\n mediaFeatures = parserConfig.mediaFeatures || {},\n mediaValueKeywords = parserConfig.mediaValueKeywords || {},\n propertyKeywords = parserConfig.propertyKeywords || {},\n nonStandardPropertyKeywords = parserConfig.nonStandardPropertyKeywords || {},\n fontProperties = parserConfig.fontProperties || {},\n counterDescriptors = parserConfig.counterDescriptors || {},\n colorKeywords = parserConfig.colorKeywords || {},\n valueKeywords = parserConfig.valueKeywords || {},\n allowNested = parserConfig.allowNested,\n supportsAtComponent = parserConfig.supportsAtComponent === true;\n\n var type, override;\n function ret(style, tp) { type = tp; return style; }\n\n // Tokenizers\n\n function tokenBase(stream, state) {\n var ch = stream.next();\n if (tokenHooks[ch]) {\n var result = tokenHooks[ch](stream, state);\n if (result !== false) return result;\n }\n if (ch == \"@\") {\n stream.eatWhile(/[\\w\\\\\\-]/);\n return ret(\"def\", stream.current());\n } else if (ch == \"=\" || (ch == \"~\" || ch == \"|\") && stream.eat(\"=\")) {\n return ret(null, \"compare\");\n } else if (ch == \"\\\"\" || ch == \"'\") {\n state.tokenize = tokenString(ch);\n return state.tokenize(stream, state);\n } else if (ch == \"#\") {\n stream.eatWhile(/[\\w\\\\\\-]/);\n return ret(\"atom\", \"hash\");\n } else if (ch == \"!\") {\n stream.match(/^\\s*\\w*/);\n return ret(\"keyword\", \"important\");\n } else if (/\\d/.test(ch) || ch == \".\" && stream.eat(/\\d/)) {\n stream.eatWhile(/[\\w.%]/);\n return ret(\"number\", \"unit\");\n } else if (ch === \"-\") {\n if (/[\\d.]/.test(stream.peek())) {\n stream.eatWhile(/[\\w.%]/);\n return ret(\"number\", \"unit\");\n } else if (stream.match(/^-[\\w\\\\\\-]+/)) {\n stream.eatWhile(/[\\w\\\\\\-]/);\n if (stream.match(/^\\s*:/, false))\n return ret(\"variable-2\", \"variable-definition\");\n return ret(\"variable-2\", \"variable\");\n } else if (stream.match(/^\\w+-/)) {\n return ret(\"meta\", \"meta\");\n }\n } else if (/[,+>*\\/]/.test(ch)) {\n return ret(null, \"select-op\");\n } else if (ch == \".\" && stream.match(/^-?[_a-z][_a-z0-9-]*/i)) {\n return ret(\"qualifier\", \"qualifier\");\n } else if (/[:;{}\\[\\]\\(\\)]/.test(ch)) {\n return ret(null, ch);\n } else if ((ch == \"u\" && stream.match(/rl(-prefix)?\\(/)) ||\n (ch == \"d\" && stream.match(\"omain(\")) ||\n (ch == \"r\" && stream.match(\"egexp(\"))) {\n stream.backUp(1);\n state.tokenize = tokenParenthesized;\n return ret(\"property\", \"word\");\n } else if (/[\\w\\\\\\-]/.test(ch)) {\n stream.eatWhile(/[\\w\\\\\\-]/);\n return ret(\"property\", \"word\");\n } else {\n return ret(null, null);\n }\n }\n\n function tokenString(quote) {\n return function(stream, state) {\n var escaped = false, ch;\n while ((ch = stream.next()) != null) {\n if (ch == quote && !escaped) {\n if (quote == \")\") stream.backUp(1);\n break;\n }\n escaped = !escaped && ch == \"\\\\\";\n }\n if (ch == quote || !escaped && quote != \")\") state.tokenize = null;\n return ret(\"string\", \"string\");\n };\n }\n\n function tokenParenthesized(stream, state) {\n stream.next(); // Must be '('\n if (!stream.match(/\\s*[\\\"\\')]/, false))\n state.tokenize = tokenString(\")\");\n else\n state.tokenize = null;\n return ret(null, \"(\");\n }\n\n // Context management\n\n function Context(type, indent, prev) {\n this.type = type;\n this.indent = indent;\n this.prev = prev;\n }\n\n function pushContext(state, stream, type, indent) {\n state.context = new Context(type, stream.indentation() + (indent === false ? 0 : indentUnit), state.context);\n return type;\n }\n\n function popContext(state) {\n if (state.context.prev)\n state.context = state.context.prev;\n return state.context.type;\n }\n\n function pass(type, stream, state) {\n return states[state.context.type](type, stream, state);\n }\n function popAndPass(type, stream, state, n) {\n for (var i = n || 1; i > 0; i--)\n state.context = state.context.prev;\n return pass(type, stream, state);\n }\n\n // Parser\n\n function wordAsValue(stream) {\n var word = stream.current().toLowerCase();\n if (valueKeywords.hasOwnProperty(word))\n override = \"atom\";\n else if (colorKeywords.hasOwnProperty(word))\n override = \"keyword\";\n else\n override = \"variable\";\n }\n\n var states = {};\n\n states.top = function(type, stream, state) {\n if (type == \"{\") {\n return pushContext(state, stream, \"block\");\n } else if (type == \"}\" && state.context.prev) {\n return popContext(state);\n } else if (supportsAtComponent && /@component/.test(type)) {\n return pushContext(state, stream, \"atComponentBlock\");\n } else if (/^@(-moz-)?document$/.test(type)) {\n return pushContext(state, stream, \"documentTypes\");\n } else if (/^@(media|supports|(-moz-)?document|import)$/.test(type)) {\n return pushContext(state, stream, \"atBlock\");\n } else if (/^@(font-face|counter-style)/.test(type)) {\n state.stateArg = type;\n return \"restricted_atBlock_before\";\n } else if (/^@(-(moz|ms|o|webkit)-)?keyframes$/.test(type)) {\n return \"keyframes\";\n } else if (type && type.charAt(0) == \"@\") {\n return pushContext(state, stream, \"at\");\n } else if (type == \"hash\") {\n override = \"builtin\";\n } else if (type == \"word\") {\n override = \"tag\";\n } else if (type == \"variable-definition\") {\n return \"maybeprop\";\n } else if (type == \"interpolation\") {\n return pushContext(state, stream, \"interpolation\");\n } else if (type == \":\") {\n return \"pseudo\";\n } else if (allowNested && type == \"(\") {\n return pushContext(state, stream, \"parens\");\n }\n return state.context.type;\n };\n\n states.block = function(type, stream, state) {\n if (type == \"word\") {\n var word = stream.current().toLowerCase();\n if (propertyKeywords.hasOwnProperty(word)) {\n override = \"property\";\n return \"maybeprop\";\n } else if (nonStandardPropertyKeywords.hasOwnProperty(word)) {\n override = \"string-2\";\n return \"maybeprop\";\n } else if (allowNested) {\n override = stream.match(/^\\s*:(?:\\s|$)/, false) ? \"property\" : \"tag\";\n return \"block\";\n } else {\n override += \" error\";\n return \"maybeprop\";\n }\n } else if (type == \"meta\") {\n return \"block\";\n } else if (!allowNested && (type == \"hash\" || type == \"qualifier\")) {\n override = \"error\";\n return \"block\";\n } else {\n return states.top(type, stream, state);\n }\n };\n\n states.maybeprop = function(type, stream, state) {\n if (type == \":\") return pushContext(state, stream, \"prop\");\n return pass(type, stream, state);\n };\n\n states.prop = function(type, stream, state) {\n if (type == \";\") return popContext(state);\n if (type == \"{\" && allowNested) return pushContext(state, stream, \"propBlock\");\n if (type == \"}\" || type == \"{\") return popAndPass(type, stream, state);\n if (type == \"(\") return pushContext(state, stream, \"parens\");\n\n if (type == \"hash\" && !/^#([0-9a-fA-f]{3,4}|[0-9a-fA-f]{6}|[0-9a-fA-f]{8})$/.test(stream.current())) {\n override += \" error\";\n } else if (type == \"word\") {\n wordAsValue(stream);\n } else if (type == \"interpolation\") {\n return pushContext(state, stream, \"interpolation\");\n }\n return \"prop\";\n };\n\n states.propBlock = function(type, _stream, state) {\n if (type == \"}\") return popContext(state);\n if (type == \"word\") { override = \"property\"; return \"maybeprop\"; }\n return state.context.type;\n };\n\n states.parens = function(type, stream, state) {\n if (type == \"{\" || type == \"}\") return popAndPass(type, stream, state);\n if (type == \")\") return popContext(state);\n if (type == \"(\") return pushContext(state, stream, \"parens\");\n if (type == \"interpolation\") return pushContext(state, stream, \"interpolation\");\n if (type == \"word\") wordAsValue(stream);\n return \"parens\";\n };\n\n states.pseudo = function(type, stream, state) {\n if (type == \"word\") {\n override = \"variable-3\";\n return state.context.type;\n }\n return pass(type, stream, state);\n };\n\n states.documentTypes = function(type, stream, state) {\n if (type == \"word\" && documentTypes.hasOwnProperty(stream.current())) {\n override = \"tag\";\n return state.context.type;\n } else {\n return states.atBlock(type, stream, state);\n }\n };\n\n states.atBlock = function(type, stream, state) {\n if (type == \"(\") return pushContext(state, stream, \"atBlock_parens\");\n if (type == \"}\" || type == \";\") return popAndPass(type, stream, state);\n if (type == \"{\") return popContext(state) && pushContext(state, stream, allowNested ? \"block\" : \"top\");\n\n if (type == \"interpolation\") return pushContext(state, stream, \"interpolation\");\n\n if (type == \"word\") {\n var word = stream.current().toLowerCase();\n if (word == \"only\" || word == \"not\" || word == \"and\" || word == \"or\")\n override = \"keyword\";\n else if (mediaTypes.hasOwnProperty(word))\n override = \"attribute\";\n else if (mediaFeatures.hasOwnProperty(word))\n override = \"property\";\n else if (mediaValueKeywords.hasOwnProperty(word))\n override = \"keyword\";\n else if (propertyKeywords.hasOwnProperty(word))\n override = \"property\";\n else if (nonStandardPropertyKeywords.hasOwnProperty(word))\n override = \"string-2\";\n else if (valueKeywords.hasOwnProperty(word))\n override = \"atom\";\n else if (colorKeywords.hasOwnProperty(word))\n override = \"keyword\";\n else\n override = \"error\";\n }\n return state.context.type;\n };\n\n states.atComponentBlock = function(type, stream, state) {\n if (type == \"}\")\n return popAndPass(type, stream, state);\n if (type == \"{\")\n return popContext(state) && pushContext(state, stream, allowNested ? \"block\" : \"top\", false);\n if (type == \"word\")\n override = \"error\";\n return state.context.type;\n };\n\n states.atBlock_parens = function(type, stream, state) {\n if (type == \")\") return popContext(state);\n if (type == \"{\" || type == \"}\") return popAndPass(type, stream, state, 2);\n return states.atBlock(type, stream, state);\n };\n\n states.restricted_atBlock_before = function(type, stream, state) {\n if (type == \"{\")\n return pushContext(state, stream, \"restricted_atBlock\");\n if (type == \"word\" && state.stateArg == \"@counter-style\") {\n override = \"variable\";\n return \"restricted_atBlock_before\";\n }\n return pass(type, stream, state);\n };\n\n states.restricted_atBlock = function(type, stream, state) {\n if (type == \"}\") {\n state.stateArg = null;\n return popContext(state);\n }\n if (type == \"word\") {\n if ((state.stateArg == \"@font-face\" && !fontProperties.hasOwnProperty(stream.current().toLowerCase())) ||\n (state.stateArg == \"@counter-style\" && !counterDescriptors.hasOwnProperty(stream.current().toLowerCase())))\n override = \"error\";\n else\n override = \"property\";\n return \"maybeprop\";\n }\n return \"restricted_atBlock\";\n };\n\n states.keyframes = function(type, stream, state) {\n if (type == \"word\") { override = \"variable\"; return \"keyframes\"; }\n if (type == \"{\") return pushContext(state, stream, \"top\");\n return pass(type, stream, state);\n };\n\n states.at = function(type, stream, state) {\n if (type == \";\") return popContext(state);\n if (type == \"{\" || type == \"}\") return popAndPass(type, stream, state);\n if (type == \"word\") override = \"tag\";\n else if (type == \"hash\") override = \"builtin\";\n return \"at\";\n };\n\n states.interpolation = function(type, stream, state) {\n if (type == \"}\") return popContext(state);\n if (type == \"{\" || type == \";\") return popAndPass(type, stream, state);\n if (type == \"word\") override = \"variable\";\n else if (type != \"variable\" && type != \"(\" && type != \")\") override = \"error\";\n return \"interpolation\";\n };\n\n return {\n startState: function(base) {\n return {tokenize: null,\n state: inline ? \"block\" : \"top\",\n stateArg: null,\n context: new Context(inline ? \"block\" : \"top\", base || 0, null)};\n },\n\n token: function(stream, state) {\n if (!state.tokenize && stream.eatSpace()) return null;\n var style = (state.tokenize || tokenBase)(stream, state);\n if (style && typeof style == \"object\") {\n type = style[1];\n style = style[0];\n }\n override = style;\n state.state = states[state.state](type, stream, state);\n return override;\n },\n\n indent: function(state, textAfter) {\n var cx = state.context, ch = textAfter && textAfter.charAt(0);\n var indent = cx.indent;\n if (cx.type == \"prop\" && (ch == \"}\" || ch == \")\")) cx = cx.prev;\n if (cx.prev) {\n if (ch == \"}\" && (cx.type == \"block\" || cx.type == \"top\" ||\n cx.type == \"interpolation\" || cx.type == \"restricted_atBlock\")) {\n // Resume indentation from parent context.\n cx = cx.prev;\n indent = cx.indent;\n } else if (ch == \")\" && (cx.type == \"parens\" || cx.type == \"atBlock_parens\") ||\n ch == \"{\" && (cx.type == \"at\" || cx.type == \"atBlock\")) {\n // Dedent relative to current context.\n indent = Math.max(0, cx.indent - indentUnit);\n cx = cx.prev;\n }\n }\n return indent;\n },\n\n electricChars: \"}\",\n blockCommentStart: \"/*\",\n blockCommentEnd: \"*/\",\n fold: \"brace\"\n };\n});\n\n function keySet(array) {\n var keys = {};\n for (var i = 0; i < array.length; ++i) {\n keys[array[i]] = true;\n }\n return keys;\n }\n\n var documentTypes_ = [\n \"domain\", \"regexp\", \"url\", \"url-prefix\"\n ], documentTypes = keySet(documentTypes_);\n\n var mediaTypes_ = [\n \"all\", \"aural\", \"braille\", \"handheld\", \"print\", \"projection\", \"screen\",\n \"tty\", \"tv\", \"embossed\"\n ], mediaTypes = keySet(mediaTypes_);\n\n var mediaFeatures_ = [\n \"width\", \"min-width\", \"max-width\", \"height\", \"min-height\", \"max-height\",\n \"device-width\", \"min-device-width\", \"max-device-width\", \"device-height\",\n \"min-device-height\", \"max-device-height\", \"aspect-ratio\",\n \"min-aspect-ratio\", \"max-aspect-ratio\", \"device-aspect-ratio\",\n \"min-device-aspect-ratio\", \"max-device-aspect-ratio\", \"color\", \"min-color\",\n \"max-color\", \"color-index\", \"min-color-index\", \"max-color-index\",\n \"monochrome\", \"min-monochrome\", \"max-monochrome\", \"resolution\",\n \"min-resolution\", \"max-resolution\", \"scan\", \"grid\", \"orientation\",\n \"device-pixel-ratio\", \"min-device-pixel-ratio\", \"max-device-pixel-ratio\",\n \"pointer\", \"any-pointer\", \"hover\", \"any-hover\"\n ], mediaFeatures = keySet(mediaFeatures_);\n\n var mediaValueKeywords_ = [\n \"landscape\", \"portrait\", \"none\", \"coarse\", \"fine\", \"on-demand\", \"hover\",\n \"interlace\", \"progressive\"\n ], mediaValueKeywords = keySet(mediaValueKeywords_);\n\n var propertyKeywords_ = [\n \"align-content\", \"align-items\", \"align-self\", \"alignment-adjust\",\n \"alignment-baseline\", \"anchor-point\", \"animation\", \"animation-delay\",\n \"animation-direction\", \"animation-duration\", \"animation-fill-mode\",\n \"animation-iteration-count\", \"animation-name\", \"animation-play-state\",\n \"animation-timing-function\", \"appearance\", \"azimuth\", \"backface-visibility\",\n \"background\", \"background-attachment\", \"background-blend-mode\", \"background-clip\",\n \"background-color\", \"background-image\", \"background-origin\", \"background-position\",\n \"background-repeat\", \"background-size\", \"baseline-shift\", \"binding\",\n \"bleed\", \"bookmark-label\", \"bookmark-level\", \"bookmark-state\",\n \"bookmark-target\", \"border\", \"border-bottom\", \"border-bottom-color\",\n \"border-bottom-left-radius\", \"border-bottom-right-radius\",\n \"border-bottom-style\", \"border-bottom-width\", \"border-collapse\",\n \"border-color\", \"border-image\", \"border-image-outset\",\n \"border-image-repeat\", \"border-image-slice\", \"border-image-source\",\n \"border-image-width\", \"border-left\", \"border-left-color\",\n \"border-left-style\", \"border-left-width\", \"border-radius\", \"border-right\",\n \"border-right-color\", \"border-right-style\", \"border-right-width\",\n \"border-spacing\", \"border-style\", \"border-top\", \"border-top-color\",\n \"border-top-left-radius\", \"border-top-right-radius\", \"border-top-style\",\n \"border-top-width\", \"border-width\", \"bottom\", \"box-decoration-break\",\n \"box-shadow\", \"box-sizing\", \"break-after\", \"break-before\", \"break-inside\",\n \"caption-side\", \"clear\", \"clip\", \"color\", \"color-profile\", \"column-count\",\n \"column-fill\", \"column-gap\", \"column-rule\", \"column-rule-color\",\n \"column-rule-style\", \"column-rule-width\", \"column-span\", \"column-width\",\n \"columns\", \"content\", \"counter-increment\", \"counter-reset\", \"crop\", \"cue\",\n \"cue-after\", \"cue-before\", \"cursor\", \"direction\", \"display\",\n \"dominant-baseline\", \"drop-initial-after-adjust\",\n \"drop-initial-after-align\", \"drop-initial-before-adjust\",\n \"drop-initial-before-align\", \"drop-initial-size\", \"drop-initial-value\",\n \"elevation\", \"empty-cells\", \"fit\", \"fit-position\", \"flex\", \"flex-basis\",\n \"flex-direction\", \"flex-flow\", \"flex-grow\", \"flex-shrink\", \"flex-wrap\",\n \"float\", \"float-offset\", \"flow-from\", \"flow-into\", \"font\", \"font-feature-settings\",\n \"font-family\", \"font-kerning\", \"font-language-override\", \"font-size\", \"font-size-adjust\",\n \"font-stretch\", \"font-style\", \"font-synthesis\", \"font-variant\",\n \"font-variant-alternates\", \"font-variant-caps\", \"font-variant-east-asian\",\n \"font-variant-ligatures\", \"font-variant-numeric\", \"font-variant-position\",\n \"font-weight\", \"grid\", \"grid-area\", \"grid-auto-columns\", \"grid-auto-flow\",\n \"grid-auto-position\", \"grid-auto-rows\", \"grid-column\", \"grid-column-end\",\n \"grid-column-start\", \"grid-row\", \"grid-row-end\", \"grid-row-start\",\n \"grid-template\", \"grid-template-areas\", \"grid-template-columns\",\n \"grid-template-rows\", \"hanging-punctuation\", \"height\", \"hyphens\",\n \"icon\", \"image-orientation\", \"image-rendering\", \"image-resolution\",\n \"inline-box-align\", \"justify-content\", \"left\", \"letter-spacing\",\n \"line-break\", \"line-height\", \"line-stacking\", \"line-stacking-ruby\",\n \"line-stacking-shift\", \"line-stacking-strategy\", \"list-style\",\n \"list-style-image\", \"list-style-position\", \"list-style-type\", \"margin\",\n \"margin-bottom\", \"margin-left\", \"margin-right\", \"margin-top\",\n \"marker-offset\", \"marks\", \"marquee-direction\", \"marquee-loop\",\n \"marquee-play-count\", \"marquee-speed\", \"marquee-style\", \"max-height\",\n \"max-width\", \"min-height\", \"min-width\", \"move-to\", \"nav-down\", \"nav-index\",\n \"nav-left\", \"nav-right\", \"nav-up\", \"object-fit\", \"object-position\",\n \"opacity\", \"order\", \"orphans\", \"outline\",\n \"outline-color\", \"outline-offset\", \"outline-style\", \"outline-width\",\n \"overflow\", \"overflow-style\", \"overflow-wrap\", \"overflow-x\", \"overflow-y\",\n \"padding\", \"padding-bottom\", \"padding-left\", \"padding-right\", \"padding-top\",\n \"page\", \"page-break-after\", \"page-break-before\", \"page-break-inside\",\n \"page-policy\", \"pause\", \"pause-after\", \"pause-before\", \"perspective\",\n \"perspective-origin\", \"pitch\", \"pitch-range\", \"play-during\", \"position\",\n \"presentation-level\", \"punctuation-trim\", \"quotes\", \"region-break-after\",\n \"region-break-before\", \"region-break-inside\", \"region-fragment\",\n \"rendering-intent\", \"resize\", \"rest\", \"rest-after\", \"rest-before\", \"richness\",\n \"right\", \"rotation\", \"rotation-point\", \"ruby-align\", \"ruby-overhang\",\n \"ruby-position\", \"ruby-span\", \"shape-image-threshold\", \"shape-inside\", \"shape-margin\",\n \"shape-outside\", \"size\", \"speak\", \"speak-as\", \"speak-header\",\n \"speak-numeral\", \"speak-punctuation\", \"speech-rate\", \"stress\", \"string-set\",\n \"tab-size\", \"table-layout\", \"target\", \"target-name\", \"target-new\",\n \"target-position\", \"text-align\", \"text-align-last\", \"text-decoration\",\n \"text-decoration-color\", \"text-decoration-line\", \"text-decoration-skip\",\n \"text-decoration-style\", \"text-emphasis\", \"text-emphasis-color\",\n \"text-emphasis-position\", \"text-emphasis-style\", \"text-height\",\n \"text-indent\", \"text-justify\", \"text-outline\", \"text-overflow\", \"text-shadow\",\n \"text-size-adjust\", \"text-space-collapse\", \"text-transform\", \"text-underline-position\",\n \"text-wrap\", \"top\", \"transform\", \"transform-origin\", \"transform-style\",\n \"transition\", \"transition-delay\", \"transition-duration\",\n \"transition-property\", \"transition-timing-function\", \"unicode-bidi\",\n \"vertical-align\", \"visibility\", \"voice-balance\", \"voice-duration\",\n \"voice-family\", \"voice-pitch\", \"voice-range\", \"voice-rate\", \"voice-stress\",\n \"voice-volume\", \"volume\", \"white-space\", \"widows\", \"width\", \"word-break\",\n \"word-spacing\", \"word-wrap\", \"z-index\",\n // SVG-specific\n \"clip-path\", \"clip-rule\", \"mask\", \"enable-background\", \"filter\", \"flood-color\",\n \"flood-opacity\", \"lighting-color\", \"stop-color\", \"stop-opacity\", \"pointer-events\",\n \"color-interpolation\", \"color-interpolation-filters\",\n \"color-rendering\", \"fill\", \"fill-opacity\", \"fill-rule\", \"image-rendering\",\n \"marker\", \"marker-end\", \"marker-mid\", \"marker-start\", \"shape-rendering\", \"stroke\",\n \"stroke-dasharray\", \"stroke-dashoffset\", \"stroke-linecap\", \"stroke-linejoin\",\n \"stroke-miterlimit\", \"stroke-opacity\", \"stroke-width\", \"text-rendering\",\n \"baseline-shift\", \"dominant-baseline\", \"glyph-orientation-horizontal\",\n \"glyph-orientation-vertical\", \"text-anchor\", \"writing-mode\"\n ], propertyKeywords = keySet(propertyKeywords_);\n\n var nonStandardPropertyKeywords_ = [\n \"scrollbar-arrow-color\", \"scrollbar-base-color\", \"scrollbar-dark-shadow-color\",\n \"scrollbar-face-color\", \"scrollbar-highlight-color\", \"scrollbar-shadow-color\",\n \"scrollbar-3d-light-color\", \"scrollbar-track-color\", \"shape-inside\",\n \"searchfield-cancel-button\", \"searchfield-decoration\", \"searchfield-results-button\",\n \"searchfield-results-decoration\", \"zoom\"\n ], nonStandardPropertyKeywords = keySet(nonStandardPropertyKeywords_);\n\n var fontProperties_ = [\n \"font-family\", \"src\", \"unicode-range\", \"font-variant\", \"font-feature-settings\",\n \"font-stretch\", \"font-weight\", \"font-style\"\n ], fontProperties = keySet(fontProperties_);\n\n var counterDescriptors_ = [\n \"additive-symbols\", \"fallback\", \"negative\", \"pad\", \"prefix\", \"range\",\n \"speak-as\", \"suffix\", \"symbols\", \"system\"\n ], counterDescriptors = keySet(counterDescriptors_);\n\n var colorKeywords_ = [\n \"aliceblue\", \"antiquewhite\", \"aqua\", \"aquamarine\", \"azure\", \"beige\",\n \"bisque\", \"black\", \"blanchedalmond\", \"blue\", \"blueviolet\", \"brown\",\n \"burlywood\", \"cadetblue\", \"chartreuse\", \"chocolate\", \"coral\", \"cornflowerblue\",\n \"cornsilk\", \"crimson\", \"cyan\", \"darkblue\", \"darkcyan\", \"darkgoldenrod\",\n \"darkgray\", \"darkgreen\", \"darkkhaki\", \"darkmagenta\", \"darkolivegreen\",\n \"darkorange\", \"darkorchid\", \"darkred\", \"darksalmon\", \"darkseagreen\",\n \"darkslateblue\", \"darkslategray\", \"darkturquoise\", \"darkviolet\",\n \"deeppink\", \"deepskyblue\", \"dimgray\", \"dodgerblue\", \"firebrick\",\n \"floralwhite\", \"forestgreen\", \"fuchsia\", \"gainsboro\", \"ghostwhite\",\n \"gold\", \"goldenrod\", \"gray\", \"grey\", \"green\", \"greenyellow\", \"honeydew\",\n \"hotpink\", \"indianred\", \"indigo\", \"ivory\", \"khaki\", \"lavender\",\n \"lavenderblush\", \"lawngreen\", \"lemonchiffon\", \"lightblue\", \"lightcoral\",\n \"lightcyan\", \"lightgoldenrodyellow\", \"lightgray\", \"lightgreen\", \"lightpink\",\n \"lightsalmon\", \"lightseagreen\", \"lightskyblue\", \"lightslategray\",\n \"lightsteelblue\", \"lightyellow\", \"lime\", \"limegreen\", \"linen\", \"magenta\",\n \"maroon\", \"mediumaquamarine\", \"mediumblue\", \"mediumorchid\", \"mediumpurple\",\n \"mediumseagreen\", \"mediumslateblue\", \"mediumspringgreen\", \"mediumturquoise\",\n \"mediumvioletred\", \"midnightblue\", \"mintcream\", \"mistyrose\", \"moccasin\",\n \"navajowhite\", \"navy\", \"oldlace\", \"olive\", \"olivedrab\", \"orange\", \"orangered\",\n \"orchid\", \"palegoldenrod\", \"palegreen\", \"paleturquoise\", \"palevioletred\",\n \"papayawhip\", \"peachpuff\", \"peru\", \"pink\", \"plum\", \"powderblue\",\n \"purple\", \"rebeccapurple\", \"red\", \"rosybrown\", \"royalblue\", \"saddlebrown\",\n \"salmon\", \"sandybrown\", \"seagreen\", \"seashell\", \"sienna\", \"silver\", \"skyblue\",\n \"slateblue\", \"slategray\", \"snow\", \"springgreen\", \"steelblue\", \"tan\",\n \"teal\", \"thistle\", \"tomato\", \"turquoise\", \"violet\", \"wheat\", \"white\",\n \"whitesmoke\", \"yellow\", \"yellowgreen\"\n ], colorKeywords = keySet(colorKeywords_);\n\n var valueKeywords_ = [\n \"above\", \"absolute\", \"activeborder\", \"additive\", \"activecaption\", \"afar\",\n \"after-white-space\", \"ahead\", \"alias\", \"all\", \"all-scroll\", \"alphabetic\", \"alternate\",\n \"always\", \"amharic\", \"amharic-abegede\", \"antialiased\", \"appworkspace\",\n \"arabic-indic\", \"armenian\", \"asterisks\", \"attr\", \"auto\", \"avoid\", \"avoid-column\", \"avoid-page\",\n \"avoid-region\", \"background\", \"backwards\", \"baseline\", \"below\", \"bidi-override\", \"binary\",\n \"bengali\", \"blink\", \"block\", \"block-axis\", \"bold\", \"bolder\", \"border\", \"border-box\",\n \"both\", \"bottom\", \"break\", \"break-all\", \"break-word\", \"bullets\", \"button\", \"button-bevel\",\n \"buttonface\", \"buttonhighlight\", \"buttonshadow\", \"buttontext\", \"calc\", \"cambodian\",\n \"capitalize\", \"caps-lock-indicator\", \"caption\", \"captiontext\", \"caret\",\n \"cell\", \"center\", \"checkbox\", \"circle\", \"cjk-decimal\", \"cjk-earthly-branch\",\n \"cjk-heavenly-stem\", \"cjk-ideographic\", \"clear\", \"clip\", \"close-quote\",\n \"col-resize\", \"collapse\", \"color\", \"color-burn\", \"color-dodge\", \"column\", \"column-reverse\",\n \"compact\", \"condensed\", \"contain\", \"content\",\n \"content-box\", \"context-menu\", \"continuous\", \"copy\", \"counter\", \"counters\", \"cover\", \"crop\",\n \"cross\", \"crosshair\", \"currentcolor\", \"cursive\", \"cyclic\", \"darken\", \"dashed\", \"decimal\",\n \"decimal-leading-zero\", \"default\", \"default-button\", \"destination-atop\",\n \"destination-in\", \"destination-out\", \"destination-over\", \"devanagari\", \"difference\",\n \"disc\", \"discard\", \"disclosure-closed\", \"disclosure-open\", \"document\",\n \"dot-dash\", \"dot-dot-dash\",\n \"dotted\", \"double\", \"down\", \"e-resize\", \"ease\", \"ease-in\", \"ease-in-out\", \"ease-out\",\n \"element\", \"ellipse\", \"ellipsis\", \"embed\", \"end\", \"ethiopic\", \"ethiopic-abegede\",\n \"ethiopic-abegede-am-et\", \"ethiopic-abegede-gez\", \"ethiopic-abegede-ti-er\",\n \"ethiopic-abegede-ti-et\", \"ethiopic-halehame-aa-er\",\n \"ethiopic-halehame-aa-et\", \"ethiopic-halehame-am-et\",\n \"ethiopic-halehame-gez\", \"ethiopic-halehame-om-et\",\n \"ethiopic-halehame-sid-et\", \"ethiopic-halehame-so-et\",\n \"ethiopic-halehame-ti-er\", \"ethiopic-halehame-ti-et\", \"ethiopic-halehame-tig\",\n \"ethiopic-numeric\", \"ew-resize\", \"exclusion\", \"expanded\", \"extends\", \"extra-condensed\",\n \"extra-expanded\", \"fantasy\", \"fast\", \"fill\", \"fixed\", \"flat\", \"flex\", \"flex-end\", \"flex-start\", \"footnotes\",\n \"forwards\", \"from\", \"geometricPrecision\", \"georgian\", \"graytext\", \"groove\",\n \"gujarati\", \"gurmukhi\", \"hand\", \"hangul\", \"hangul-consonant\", \"hard-light\", \"hebrew\",\n \"help\", \"hidden\", \"hide\", \"higher\", \"highlight\", \"highlighttext\",\n \"hiragana\", \"hiragana-iroha\", \"horizontal\", \"hsl\", \"hsla\", \"hue\", \"icon\", \"ignore\",\n \"inactiveborder\", \"inactivecaption\", \"inactivecaptiontext\", \"infinite\",\n \"infobackground\", \"infotext\", \"inherit\", \"initial\", \"inline\", \"inline-axis\",\n \"inline-block\", \"inline-flex\", \"inline-table\", \"inset\", \"inside\", \"intrinsic\", \"invert\",\n \"italic\", \"japanese-formal\", \"japanese-informal\", \"justify\", \"kannada\",\n \"katakana\", \"katakana-iroha\", \"keep-all\", \"khmer\",\n \"korean-hangul-formal\", \"korean-hanja-formal\", \"korean-hanja-informal\",\n \"landscape\", \"lao\", \"large\", \"larger\", \"left\", \"level\", \"lighter\", \"lighten\",\n \"line-through\", \"linear\", \"linear-gradient\", \"lines\", \"list-item\", \"listbox\", \"listitem\",\n \"local\", \"logical\", \"loud\", \"lower\", \"lower-alpha\", \"lower-armenian\",\n \"lower-greek\", \"lower-hexadecimal\", \"lower-latin\", \"lower-norwegian\",\n \"lower-roman\", \"lowercase\", \"ltr\", \"luminosity\", \"malayalam\", \"match\", \"matrix\", \"matrix3d\",\n \"media-controls-background\", \"media-current-time-display\",\n \"media-fullscreen-button\", \"media-mute-button\", \"media-play-button\",\n \"media-return-to-realtime-button\", \"media-rewind-button\",\n \"media-seek-back-button\", \"media-seek-forward-button\", \"media-slider\",\n \"media-sliderthumb\", \"media-time-remaining-display\", \"media-volume-slider\",\n \"media-volume-slider-container\", \"media-volume-sliderthumb\", \"medium\",\n \"menu\", \"menulist\", \"menulist-button\", \"menulist-text\",\n \"menulist-textfield\", \"menutext\", \"message-box\", \"middle\", \"min-intrinsic\",\n \"mix\", \"mongolian\", \"monospace\", \"move\", \"multiple\", \"multiply\", \"myanmar\", \"n-resize\",\n \"narrower\", \"ne-resize\", \"nesw-resize\", \"no-close-quote\", \"no-drop\",\n \"no-open-quote\", \"no-repeat\", \"none\", \"normal\", \"not-allowed\", \"nowrap\",\n \"ns-resize\", \"numbers\", \"numeric\", \"nw-resize\", \"nwse-resize\", \"oblique\", \"octal\", \"open-quote\",\n \"optimizeLegibility\", \"optimizeSpeed\", \"oriya\", \"oromo\", \"outset\",\n \"outside\", \"outside-shape\", \"overlay\", \"overline\", \"padding\", \"padding-box\",\n \"painted\", \"page\", \"paused\", \"persian\", \"perspective\", \"plus-darker\", \"plus-lighter\",\n \"pointer\", \"polygon\", \"portrait\", \"pre\", \"pre-line\", \"pre-wrap\", \"preserve-3d\",\n \"progress\", \"push-button\", \"radial-gradient\", \"radio\", \"read-only\",\n \"read-write\", \"read-write-plaintext-only\", \"rectangle\", \"region\",\n \"relative\", \"repeat\", \"repeating-linear-gradient\",\n \"repeating-radial-gradient\", \"repeat-x\", \"repeat-y\", \"reset\", \"reverse\",\n \"rgb\", \"rgba\", \"ridge\", \"right\", \"rotate\", \"rotate3d\", \"rotateX\", \"rotateY\",\n \"rotateZ\", \"round\", \"row\", \"row-resize\", \"row-reverse\", \"rtl\", \"run-in\", \"running\",\n \"s-resize\", \"sans-serif\", \"saturation\", \"scale\", \"scale3d\", \"scaleX\", \"scaleY\", \"scaleZ\", \"screen\",\n \"scroll\", \"scrollbar\", \"se-resize\", \"searchfield\",\n \"searchfield-cancel-button\", \"searchfield-decoration\",\n \"searchfield-results-button\", \"searchfield-results-decoration\",\n \"semi-condensed\", \"semi-expanded\", \"separate\", \"serif\", \"show\", \"sidama\",\n \"simp-chinese-formal\", \"simp-chinese-informal\", \"single\",\n \"skew\", \"skewX\", \"skewY\", \"skip-white-space\", \"slide\", \"slider-horizontal\",\n \"slider-vertical\", \"sliderthumb-horizontal\", \"sliderthumb-vertical\", \"slow\",\n \"small\", \"small-caps\", \"small-caption\", \"smaller\", \"soft-light\", \"solid\", \"somali\",\n \"source-atop\", \"source-in\", \"source-out\", \"source-over\", \"space\", \"space-around\", \"space-between\", \"spell-out\", \"square\",\n \"square-button\", \"start\", \"static\", \"status-bar\", \"stretch\", \"stroke\", \"sub\",\n \"subpixel-antialiased\", \"super\", \"sw-resize\", \"symbolic\", \"symbols\", \"table\",\n \"table-caption\", \"table-cell\", \"table-column\", \"table-column-group\",\n \"table-footer-group\", \"table-header-group\", \"table-row\", \"table-row-group\",\n \"tamil\",\n \"telugu\", \"text\", \"text-bottom\", \"text-top\", \"textarea\", \"textfield\", \"thai\",\n \"thick\", \"thin\", \"threeddarkshadow\", \"threedface\", \"threedhighlight\",\n \"threedlightshadow\", \"threedshadow\", \"tibetan\", \"tigre\", \"tigrinya-er\",\n \"tigrinya-er-abegede\", \"tigrinya-et\", \"tigrinya-et-abegede\", \"to\", \"top\",\n \"trad-chinese-formal\", \"trad-chinese-informal\",\n \"translate\", \"translate3d\", \"translateX\", \"translateY\", \"translateZ\",\n \"transparent\", \"ultra-condensed\", \"ultra-expanded\", \"underline\", \"up\",\n \"upper-alpha\", \"upper-armenian\", \"upper-greek\", \"upper-hexadecimal\",\n \"upper-latin\", \"upper-norwegian\", \"upper-roman\", \"uppercase\", \"urdu\", \"url\",\n \"var\", \"vertical\", \"vertical-text\", \"visible\", \"visibleFill\", \"visiblePainted\",\n \"visibleStroke\", \"visual\", \"w-resize\", \"wait\", \"wave\", \"wider\",\n \"window\", \"windowframe\", \"windowtext\", \"words\", \"wrap\", \"wrap-reverse\", \"x-large\", \"x-small\", \"xor\",\n \"xx-large\", \"xx-small\"\n ], valueKeywords = keySet(valueKeywords_);\n\n var allWords = documentTypes_.concat(mediaTypes_).concat(mediaFeatures_).concat(mediaValueKeywords_)\n .concat(propertyKeywords_).concat(nonStandardPropertyKeywords_).concat(colorKeywords_)\n .concat(valueKeywords_);\n CodeMirror.registerHelper(\"hintWords\", \"css\", allWords);\n\n function tokenCComment(stream, state) {\n var maybeEnd = false, ch;\n while ((ch = stream.next()) != null) {\n if (maybeEnd && ch == \"/\") {\n state.tokenize = null;\n break;\n }\n maybeEnd = (ch == \"*\");\n }\n return [\"comment\", \"comment\"];\n }\n\n CodeMirror.defineMIME(\"text/css\", {\n documentTypes: documentTypes,\n mediaTypes: mediaTypes,\n mediaFeatures: mediaFeatures,\n mediaValueKeywords: mediaValueKeywords,\n propertyKeywords: propertyKeywords,\n nonStandardPropertyKeywords: nonStandardPropertyKeywords,\n fontProperties: fontProperties,\n counterDescriptors: counterDescriptors,\n colorKeywords: colorKeywords,\n valueKeywords: valueKeywords,\n tokenHooks: {\n \"/\": function(stream, state) {\n if (!stream.eat(\"*\")) return false;\n state.tokenize = tokenCComment;\n return tokenCComment(stream, state);\n }\n },\n name: \"css\"\n });\n\n CodeMirror.defineMIME(\"text/x-scss\", {\n mediaTypes: mediaTypes,\n mediaFeatures: mediaFeatures,\n mediaValueKeywords: mediaValueKeywords,\n propertyKeywords: propertyKeywords,\n nonStandardPropertyKeywords: nonStandardPropertyKeywords,\n colorKeywords: colorKeywords,\n valueKeywords: valueKeywords,\n fontProperties: fontProperties,\n allowNested: true,\n tokenHooks: {\n \"/\": function(stream, state) {\n if (stream.eat(\"/\")) {\n stream.skipToEnd();\n return [\"comment\", \"comment\"];\n } else if (stream.eat(\"*\")) {\n state.tokenize = tokenCComment;\n return tokenCComment(stream, state);\n } else {\n return [\"operator\", \"operator\"];\n }\n },\n \":\": function(stream) {\n if (stream.match(/\\s*\\{/))\n return [null, \"{\"];\n return false;\n },\n \"$\": function(stream) {\n stream.match(/^[\\w-]+/);\n if (stream.match(/^\\s*:/, false))\n return [\"variable-2\", \"variable-definition\"];\n return [\"variable-2\", \"variable\"];\n },\n \"#\": function(stream) {\n if (!stream.eat(\"{\")) return false;\n return [null, \"interpolation\"];\n }\n },\n name: \"css\",\n helperType: \"scss\"\n });\n\n CodeMirror.defineMIME(\"text/x-less\", {\n mediaTypes: mediaTypes,\n mediaFeatures: mediaFeatures,\n mediaValueKeywords: mediaValueKeywords,\n propertyKeywords: propertyKeywords,\n nonStandardPropertyKeywords: nonStandardPropertyKeywords,\n colorKeywords: colorKeywords,\n valueKeywords: valueKeywords,\n fontProperties: fontProperties,\n allowNested: true,\n tokenHooks: {\n \"/\": function(stream, state) {\n if (stream.eat(\"/\")) {\n stream.skipToEnd();\n return [\"comment\", \"comment\"];\n } else if (stream.eat(\"*\")) {\n state.tokenize = tokenCComment;\n return tokenCComment(stream, state);\n } else {\n return [\"operator\", \"operator\"];\n }\n },\n \"@\": function(stream) {\n if (stream.eat(\"{\")) return [null, \"interpolation\"];\n if (stream.match(/^(charset|document|font-face|import|(-(moz|ms|o|webkit)-)?keyframes|media|namespace|page|supports)\\b/, false)) return false;\n stream.eatWhile(/[\\w\\\\\\-]/);\n if (stream.match(/^\\s*:/, false))\n return [\"variable-2\", \"variable-definition\"];\n return [\"variable-2\", \"variable\"];\n },\n \"&\": function() {\n return [\"atom\", \"atom\"];\n }\n },\n name: \"css\",\n helperType: \"less\"\n });\n\n CodeMirror.defineMIME(\"text/x-gss\", {\n documentTypes: documentTypes,\n mediaTypes: mediaTypes,\n mediaFeatures: mediaFeatures,\n propertyKeywords: propertyKeywords,\n nonStandardPropertyKeywords: nonStandardPropertyKeywords,\n fontProperties: fontProperties,\n counterDescriptors: counterDescriptors,\n colorKeywords: colorKeywords,\n valueKeywords: valueKeywords,\n supportsAtComponent: true,\n tokenHooks: {\n \"/\": function(stream, state) {\n if (!stream.eat(\"*\")) return false;\n state.tokenize = tokenCComment;\n return tokenCComment(stream, state);\n }\n },\n name: \"css\",\n helperType: \"gss\"\n });\n\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/css/css.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/mode/htmlembedded/htmlembedded.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"), require(\"../htmlmixed/htmlmixed\"),\n require(\"../../addon/mode/multiplex\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\", \"../htmlmixed/htmlmixed\",\n \"../../addon/mode/multiplex\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n \"use strict\";\n\n CodeMirror.defineMode(\"htmlembedded\", function(config, parserConfig) {\n return CodeMirror.multiplexingMode(CodeMirror.getMode(config, \"htmlmixed\"), {\n open: parserConfig.open || parserConfig.scriptStartRegex || \"<%\",\n close: parserConfig.close || parserConfig.scriptEndRegex || \"%>\",\n mode: CodeMirror.getMode(config, parserConfig.scriptingModeSpec)\n });\n }, \"htmlmixed\");\n\n CodeMirror.defineMIME(\"application/x-ejs\", {name: \"htmlembedded\", scriptingModeSpec:\"javascript\"});\n CodeMirror.defineMIME(\"application/x-aspx\", {name: \"htmlembedded\", scriptingModeSpec:\"text/x-csharp\"});\n CodeMirror.defineMIME(\"application/x-jsp\", {name: \"htmlembedded\", scriptingModeSpec:\"text/x-java\"});\n CodeMirror.defineMIME(\"application/x-erb\", {name: \"htmlembedded\", scriptingModeSpec:\"ruby\"});\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/htmlembedded/htmlembedded.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/mode/htmlmixed/htmlmixed.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"), require(\"../xml/xml\"), require(\"../javascript/javascript\"), require(\"../css/css\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\", \"../xml/xml\", \"../javascript/javascript\", \"../css/css\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n \"use strict\";\n\n var defaultTags = {\n script: [\n [\"lang\", /(javascript|babel)/i, \"javascript\"],\n [\"type\", /^(?:text|application)\\/(?:x-)?(?:java|ecma)script$|^$/i, \"javascript\"],\n [\"type\", /./, \"text/plain\"],\n [null, null, \"javascript\"]\n ],\n style: [\n [\"lang\", /^css$/i, \"css\"],\n [\"type\", /^(text\\/)?(x-)?(stylesheet|css)$/i, \"css\"],\n [\"type\", /./, \"text/plain\"],\n [null, null, \"css\"]\n ]\n };\n\n function maybeBackup(stream, pat, style) {\n var cur = stream.current(), close = cur.search(pat);\n if (close > -1) {\n stream.backUp(cur.length - close);\n } else if (cur.match(/<\\/?$/)) {\n stream.backUp(cur.length);\n if (!stream.match(pat, false)) stream.match(cur);\n }\n return style;\n }\n\n var attrRegexpCache = {};\n function getAttrRegexp(attr) {\n var regexp = attrRegexpCache[attr];\n if (regexp) return regexp;\n return attrRegexpCache[attr] = new RegExp(\"\\\\s+\" + attr + \"\\\\s*=\\\\s*('|\\\")?([^'\\\"]+)('|\\\")?\\\\s*\");\n }\n\n function getAttrValue(text, attr) {\n var match = text.match(getAttrRegexp(attr))\n return match ? match[2] : \"\"\n }\n\n function getTagRegexp(tagName, anchored) {\n return new RegExp((anchored ? \"^\" : \"\") + \"<\\/\\s*\" + tagName + \"\\s*>\", \"i\");\n }\n\n function addTags(from, to) {\n for (var tag in from) {\n var dest = to[tag] || (to[tag] = []);\n var source = from[tag];\n for (var i = source.length - 1; i >= 0; i--)\n dest.unshift(source[i])\n }\n }\n\n function findMatchingMode(tagInfo, tagText) {\n for (var i = 0; i < tagInfo.length; i++) {\n var spec = tagInfo[i];\n if (!spec[0] || spec[1].test(getAttrValue(tagText, spec[0]))) return spec[2];\n }\n }\n\n CodeMirror.defineMode(\"htmlmixed\", function (config, parserConfig) {\n var htmlMode = CodeMirror.getMode(config, {\n name: \"xml\",\n htmlMode: true,\n multilineTagIndentFactor: parserConfig.multilineTagIndentFactor,\n multilineTagIndentPastTag: parserConfig.multilineTagIndentPastTag\n });\n\n var tags = {};\n var configTags = parserConfig && parserConfig.tags, configScript = parserConfig && parserConfig.scriptTypes;\n addTags(defaultTags, tags);\n if (configTags) addTags(configTags, tags);\n if (configScript) for (var i = configScript.length - 1; i >= 0; i--)\n tags.script.unshift([\"type\", configScript[i].matches, configScript[i].mode])\n\n function html(stream, state) {\n var style = htmlMode.token(stream, state.htmlState), tag = /\\btag\\b/.test(style), tagName\n if (tag && !/[<>\\s\\/]/.test(stream.current()) &&\n (tagName = state.htmlState.tagName && state.htmlState.tagName.toLowerCase()) &&\n tags.hasOwnProperty(tagName)) {\n state.inTag = tagName + \" \"\n } else if (state.inTag && tag && />$/.test(stream.current())) {\n var inTag = /^([\\S]+) (.*)/.exec(state.inTag)\n state.inTag = null\n var modeSpec = stream.current() == \">\" && findMatchingMode(tags[inTag[1]], inTag[2])\n var mode = CodeMirror.getMode(config, modeSpec)\n var endTagA = getTagRegexp(inTag[1], true), endTag = getTagRegexp(inTag[1], false);\n state.token = function (stream, state) {\n if (stream.match(endTagA, false)) {\n state.token = html;\n state.localState = state.localMode = null;\n return null;\n }\n return maybeBackup(stream, endTag, state.localMode.token(stream, state.localState));\n };\n state.localMode = mode;\n state.localState = CodeMirror.startState(mode, htmlMode.indent(state.htmlState, \"\"));\n } else if (state.inTag) {\n state.inTag += stream.current()\n if (stream.eol()) state.inTag += \" \"\n }\n return style;\n };\n\n return {\n startState: function () {\n var state = htmlMode.startState();\n return {token: html, inTag: null, localMode: null, localState: null, htmlState: state};\n },\n\n copyState: function (state) {\n var local;\n if (state.localState) {\n local = CodeMirror.copyState(state.localMode, state.localState);\n }\n return {token: state.token, inTag: state.inTag,\n localMode: state.localMode, localState: local,\n htmlState: CodeMirror.copyState(htmlMode, state.htmlState)};\n },\n\n token: function (stream, state) {\n return state.token(stream, state);\n },\n\n indent: function (state, textAfter) {\n if (!state.localMode || /^\\s*<\\//.test(textAfter))\n return htmlMode.indent(state.htmlState, textAfter);\n else if (state.localMode.indent)\n return state.localMode.indent(state.localState, textAfter);\n else\n return CodeMirror.Pass;\n },\n\n innerMode: function (state) {\n return {state: state.localState || state.htmlState, mode: state.localMode || htmlMode};\n }\n };\n }, \"xml\", \"javascript\", \"css\");\n\n CodeMirror.defineMIME(\"text/html\", \"htmlmixed\");\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/htmlmixed/htmlmixed.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n// TODO actually recognize syntax of TypeScript constructs\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n\"use strict\";\n\nfunction expressionAllowed(stream, state, backUp) {\n return /^(?:operator|sof|keyword c|case|new|[\\[{}\\(,;:]|=>)$/.test(state.lastType) ||\n (state.lastType == \"quasi\" && /\\{\\s*$/.test(stream.string.slice(0, stream.pos - (backUp || 0))))\n}\n\nCodeMirror.defineMode(\"javascript\", function(config, parserConfig) {\n var indentUnit = config.indentUnit;\n var statementIndent = parserConfig.statementIndent;\n var jsonldMode = parserConfig.jsonld;\n var jsonMode = parserConfig.json || jsonldMode;\n var isTS = parserConfig.typescript;\n var wordRE = parserConfig.wordCharacters || /[\\w$\\xa1-\\uffff]/;\n\n // Tokenizer\n\n var keywords = function(){\n function kw(type) {return {type: type, style: \"keyword\"};}\n var A = kw(\"keyword a\"), B = kw(\"keyword b\"), C = kw(\"keyword c\");\n var operator = kw(\"operator\"), atom = {type: \"atom\", style: \"atom\"};\n\n var jsKeywords = {\n \"if\": kw(\"if\"), \"while\": A, \"with\": A, \"else\": B, \"do\": B, \"try\": B, \"finally\": B,\n \"return\": C, \"break\": C, \"continue\": C, \"new\": kw(\"new\"), \"delete\": C, \"throw\": C, \"debugger\": C,\n \"var\": kw(\"var\"), \"const\": kw(\"var\"), \"let\": kw(\"var\"),\n \"function\": kw(\"function\"), \"catch\": kw(\"catch\"),\n \"for\": kw(\"for\"), \"switch\": kw(\"switch\"), \"case\": kw(\"case\"), \"default\": kw(\"default\"),\n \"in\": operator, \"typeof\": operator, \"instanceof\": operator,\n \"true\": atom, \"false\": atom, \"null\": atom, \"undefined\": atom, \"NaN\": atom, \"Infinity\": atom,\n \"this\": kw(\"this\"), \"class\": kw(\"class\"), \"super\": kw(\"atom\"),\n \"yield\": C, \"export\": kw(\"export\"), \"import\": kw(\"import\"), \"extends\": C\n };\n\n // Extend the 'normal' keywords with the TypeScript language extensions\n if (isTS) {\n var type = {type: \"variable\", style: \"variable-3\"};\n var tsKeywords = {\n // object-like things\n \"interface\": kw(\"class\"),\n \"implements\": C,\n \"namespace\": C,\n \"module\": kw(\"module\"),\n \"enum\": kw(\"module\"),\n\n // scope modifiers\n \"public\": kw(\"modifier\"),\n \"private\": kw(\"modifier\"),\n \"protected\": kw(\"modifier\"),\n \"abstract\": kw(\"modifier\"),\n\n // operators\n \"as\": operator,\n\n // types\n \"string\": type, \"number\": type, \"boolean\": type, \"any\": type\n };\n\n for (var attr in tsKeywords) {\n jsKeywords[attr] = tsKeywords[attr];\n }\n }\n\n return jsKeywords;\n }();\n\n var isOperatorChar = /[+\\-*&%=<>!?|~^]/;\n var isJsonldKeyword = /^@(context|id|value|language|type|container|list|set|reverse|index|base|vocab|graph)\"/;\n\n function readRegexp(stream) {\n var escaped = false, next, inSet = false;\n while ((next = stream.next()) != null) {\n if (!escaped) {\n if (next == \"/\" && !inSet) return;\n if (next == \"[\") inSet = true;\n else if (inSet && next == \"]\") inSet = false;\n }\n escaped = !escaped && next == \"\\\\\";\n }\n }\n\n // Used as scratch variables to communicate multiple values without\n // consing up tons of objects.\n var type, content;\n function ret(tp, style, cont) {\n type = tp; content = cont;\n return style;\n }\n function tokenBase(stream, state) {\n var ch = stream.next();\n if (ch == '\"' || ch == \"'\") {\n state.tokenize = tokenString(ch);\n return state.tokenize(stream, state);\n } else if (ch == \".\" && stream.match(/^\\d+(?:[eE][+\\-]?\\d+)?/)) {\n return ret(\"number\", \"number\");\n } else if (ch == \".\" && stream.match(\"..\")) {\n return ret(\"spread\", \"meta\");\n } else if (/[\\[\\]{}\\(\\),;\\:\\.]/.test(ch)) {\n return ret(ch);\n } else if (ch == \"=\" && stream.eat(\">\")) {\n return ret(\"=>\", \"operator\");\n } else if (ch == \"0\" && stream.eat(/x/i)) {\n stream.eatWhile(/[\\da-f]/i);\n return ret(\"number\", \"number\");\n } else if (ch == \"0\" && stream.eat(/o/i)) {\n stream.eatWhile(/[0-7]/i);\n return ret(\"number\", \"number\");\n } else if (ch == \"0\" && stream.eat(/b/i)) {\n stream.eatWhile(/[01]/i);\n return ret(\"number\", \"number\");\n } else if (/\\d/.test(ch)) {\n stream.match(/^\\d*(?:\\.\\d*)?(?:[eE][+\\-]?\\d+)?/);\n return ret(\"number\", \"number\");\n } else if (ch == \"/\") {\n if (stream.eat(\"*\")) {\n state.tokenize = tokenComment;\n return tokenComment(stream, state);\n } else if (stream.eat(\"/\")) {\n stream.skipToEnd();\n return ret(\"comment\", \"comment\");\n } else if (expressionAllowed(stream, state, 1)) {\n readRegexp(stream);\n stream.match(/^\\b(([gimyu])(?![gimyu]*\\2))+\\b/);\n return ret(\"regexp\", \"string-2\");\n } else {\n stream.eatWhile(isOperatorChar);\n return ret(\"operator\", \"operator\", stream.current());\n }\n } else if (ch == \"`\") {\n state.tokenize = tokenQuasi;\n return tokenQuasi(stream, state);\n } else if (ch == \"#\") {\n stream.skipToEnd();\n return ret(\"error\", \"error\");\n } else if (isOperatorChar.test(ch)) {\n stream.eatWhile(isOperatorChar);\n return ret(\"operator\", \"operator\", stream.current());\n } else if (wordRE.test(ch)) {\n stream.eatWhile(wordRE);\n var word = stream.current(), known = keywords.propertyIsEnumerable(word) && keywords[word];\n return (known && state.lastType != \".\") ? ret(known.type, known.style, word) :\n ret(\"variable\", \"variable\", word);\n }\n }\n\n function tokenString(quote) {\n return function(stream, state) {\n var escaped = false, next;\n if (jsonldMode && stream.peek() == \"@\" && stream.match(isJsonldKeyword)){\n state.tokenize = tokenBase;\n return ret(\"jsonld-keyword\", \"meta\");\n }\n while ((next = stream.next()) != null) {\n if (next == quote && !escaped) break;\n escaped = !escaped && next == \"\\\\\";\n }\n if (!escaped) state.tokenize = tokenBase;\n return ret(\"string\", \"string\");\n };\n }\n\n function tokenComment(stream, state) {\n var maybeEnd = false, ch;\n while (ch = stream.next()) {\n if (ch == \"/\" && maybeEnd) {\n state.tokenize = tokenBase;\n break;\n }\n maybeEnd = (ch == \"*\");\n }\n return ret(\"comment\", \"comment\");\n }\n\n function tokenQuasi(stream, state) {\n var escaped = false, next;\n while ((next = stream.next()) != null) {\n if (!escaped && (next == \"`\" || next == \"$\" && stream.eat(\"{\"))) {\n state.tokenize = tokenBase;\n break;\n }\n escaped = !escaped && next == \"\\\\\";\n }\n return ret(\"quasi\", \"string-2\", stream.current());\n }\n\n var brackets = \"([{}])\";\n // This is a crude lookahead trick to try and notice that we're\n // parsing the argument patterns for a fat-arrow function before we\n // actually hit the arrow token. It only works if the arrow is on\n // the same line as the arguments and there's no strange noise\n // (comments) in between. Fallback is to only notice when we hit the\n // arrow, and not declare the arguments as locals for the arrow\n // body.\n function findFatArrow(stream, state) {\n if (state.fatArrowAt) state.fatArrowAt = null;\n var arrow = stream.string.indexOf(\"=>\", stream.start);\n if (arrow < 0) return;\n\n var depth = 0, sawSomething = false;\n for (var pos = arrow - 1; pos >= 0; --pos) {\n var ch = stream.string.charAt(pos);\n var bracket = brackets.indexOf(ch);\n if (bracket >= 0 && bracket < 3) {\n if (!depth) { ++pos; break; }\n if (--depth == 0) break;\n } else if (bracket >= 3 && bracket < 6) {\n ++depth;\n } else if (wordRE.test(ch)) {\n sawSomething = true;\n } else if (/[\"'\\/]/.test(ch)) {\n return;\n } else if (sawSomething && !depth) {\n ++pos;\n break;\n }\n }\n if (sawSomething && !depth) state.fatArrowAt = pos;\n }\n\n // Parser\n\n var atomicTypes = {\"atom\": true, \"number\": true, \"variable\": true, \"string\": true, \"regexp\": true, \"this\": true, \"jsonld-keyword\": true};\n\n function JSLexical(indented, column, type, align, prev, info) {\n this.indented = indented;\n this.column = column;\n this.type = type;\n this.prev = prev;\n this.info = info;\n if (align != null) this.align = align;\n }\n\n function inScope(state, varname) {\n for (var v = state.localVars; v; v = v.next)\n if (v.name == varname) return true;\n for (var cx = state.context; cx; cx = cx.prev) {\n for (var v = cx.vars; v; v = v.next)\n if (v.name == varname) return true;\n }\n }\n\n function parseJS(state, style, type, content, stream) {\n var cc = state.cc;\n // Communicate our context to the combinators.\n // (Less wasteful than consing up a hundred closures on every call.)\n cx.state = state; cx.stream = stream; cx.marked = null, cx.cc = cc; cx.style = style;\n\n if (!state.lexical.hasOwnProperty(\"align\"))\n state.lexical.align = true;\n\n while(true) {\n var combinator = cc.length ? cc.pop() : jsonMode ? expression : statement;\n if (combinator(type, content)) {\n while(cc.length && cc[cc.length - 1].lex)\n cc.pop()();\n if (cx.marked) return cx.marked;\n if (type == \"variable\" && inScope(state, content)) return \"variable-2\";\n return style;\n }\n }\n }\n\n // Combinator utils\n\n var cx = {state: null, column: null, marked: null, cc: null};\n function pass() {\n for (var i = arguments.length - 1; i >= 0; i--) cx.cc.push(arguments[i]);\n }\n function cont() {\n pass.apply(null, arguments);\n return true;\n }\n function register(varname) {\n function inList(list) {\n for (var v = list; v; v = v.next)\n if (v.name == varname) return true;\n return false;\n }\n var state = cx.state;\n cx.marked = \"def\";\n if (state.context) {\n if (inList(state.localVars)) return;\n state.localVars = {name: varname, next: state.localVars};\n } else {\n if (inList(state.globalVars)) return;\n if (parserConfig.globalVars)\n state.globalVars = {name: varname, next: state.globalVars};\n }\n }\n\n // Combinators\n\n var defaultVars = {name: \"this\", next: {name: \"arguments\"}};\n function pushcontext() {\n cx.state.context = {prev: cx.state.context, vars: cx.state.localVars};\n cx.state.localVars = defaultVars;\n }\n function popcontext() {\n cx.state.localVars = cx.state.context.vars;\n cx.state.context = cx.state.context.prev;\n }\n function pushlex(type, info) {\n var result = function() {\n var state = cx.state, indent = state.indented;\n if (state.lexical.type == \"stat\") indent = state.lexical.indented;\n else for (var outer = state.lexical; outer && outer.type == \")\" && outer.align; outer = outer.prev)\n indent = outer.indented;\n state.lexical = new JSLexical(indent, cx.stream.column(), type, null, state.lexical, info);\n };\n result.lex = true;\n return result;\n }\n function poplex() {\n var state = cx.state;\n if (state.lexical.prev) {\n if (state.lexical.type == \")\")\n state.indented = state.lexical.indented;\n state.lexical = state.lexical.prev;\n }\n }\n poplex.lex = true;\n\n function expect(wanted) {\n function exp(type) {\n if (type == wanted) return cont();\n else if (wanted == \";\") return pass();\n else return cont(exp);\n };\n return exp;\n }\n\n function statement(type, value) {\n if (type == \"var\") return cont(pushlex(\"vardef\", value.length), vardef, expect(\";\"), poplex);\n if (type == \"keyword a\") return cont(pushlex(\"form\"), expression, statement, poplex);\n if (type == \"keyword b\") return cont(pushlex(\"form\"), statement, poplex);\n if (type == \"{\") return cont(pushlex(\"}\"), block, poplex);\n if (type == \";\") return cont();\n if (type == \"if\") {\n if (cx.state.lexical.info == \"else\" && cx.state.cc[cx.state.cc.length - 1] == poplex)\n cx.state.cc.pop()();\n return cont(pushlex(\"form\"), expression, statement, poplex, maybeelse);\n }\n if (type == \"function\") return cont(functiondef);\n if (type == \"for\") return cont(pushlex(\"form\"), forspec, statement, poplex);\n if (type == \"variable\") return cont(pushlex(\"stat\"), maybelabel);\n if (type == \"switch\") return cont(pushlex(\"form\"), expression, pushlex(\"}\", \"switch\"), expect(\"{\"),\n block, poplex, poplex);\n if (type == \"case\") return cont(expression, expect(\":\"));\n if (type == \"default\") return cont(expect(\":\"));\n if (type == \"catch\") return cont(pushlex(\"form\"), pushcontext, expect(\"(\"), funarg, expect(\")\"),\n statement, poplex, popcontext);\n if (type == \"class\") return cont(pushlex(\"form\"), className, poplex);\n if (type == \"export\") return cont(pushlex(\"stat\"), afterExport, poplex);\n if (type == \"import\") return cont(pushlex(\"stat\"), afterImport, poplex);\n if (type == \"module\") return cont(pushlex(\"form\"), pattern, pushlex(\"}\"), expect(\"{\"), block, poplex, poplex)\n return pass(pushlex(\"stat\"), expression, expect(\";\"), poplex);\n }\n function expression(type) {\n return expressionInner(type, false);\n }\n function expressionNoComma(type) {\n return expressionInner(type, true);\n }\n function expressionInner(type, noComma) {\n if (cx.state.fatArrowAt == cx.stream.start) {\n var body = noComma ? arrowBodyNoComma : arrowBody;\n if (type == \"(\") return cont(pushcontext, pushlex(\")\"), commasep(pattern, \")\"), poplex, expect(\"=>\"), body, popcontext);\n else if (type == \"variable\") return pass(pushcontext, pattern, expect(\"=>\"), body, popcontext);\n }\n\n var maybeop = noComma ? maybeoperatorNoComma : maybeoperatorComma;\n if (atomicTypes.hasOwnProperty(type)) return cont(maybeop);\n if (type == \"function\") return cont(functiondef, maybeop);\n if (type == \"keyword c\") return cont(noComma ? maybeexpressionNoComma : maybeexpression);\n if (type == \"(\") return cont(pushlex(\")\"), maybeexpression, comprehension, expect(\")\"), poplex, maybeop);\n if (type == \"operator\" || type == \"spread\") return cont(noComma ? expressionNoComma : expression);\n if (type == \"[\") return cont(pushlex(\"]\"), arrayLiteral, poplex, maybeop);\n if (type == \"{\") return contCommasep(objprop, \"}\", null, maybeop);\n if (type == \"quasi\") return pass(quasi, maybeop);\n if (type == \"new\") return cont(maybeTarget(noComma));\n return cont();\n }\n function maybeexpression(type) {\n if (type.match(/[;\\}\\)\\],]/)) return pass();\n return pass(expression);\n }\n function maybeexpressionNoComma(type) {\n if (type.match(/[;\\}\\)\\],]/)) return pass();\n return pass(expressionNoComma);\n }\n\n function maybeoperatorComma(type, value) {\n if (type == \",\") return cont(expression);\n return maybeoperatorNoComma(type, value, false);\n }\n function maybeoperatorNoComma(type, value, noComma) {\n var me = noComma == false ? maybeoperatorComma : maybeoperatorNoComma;\n var expr = noComma == false ? expression : expressionNoComma;\n if (type == \"=>\") return cont(pushcontext, noComma ? arrowBodyNoComma : arrowBody, popcontext);\n if (type == \"operator\") {\n if (/\\+\\+|--/.test(value)) return cont(me);\n if (value == \"?\") return cont(expression, expect(\":\"), expr);\n return cont(expr);\n }\n if (type == \"quasi\") { return pass(quasi, me); }\n if (type == \";\") return;\n if (type == \"(\") return contCommasep(expressionNoComma, \")\", \"call\", me);\n if (type == \".\") return cont(property, me);\n if (type == \"[\") return cont(pushlex(\"]\"), maybeexpression, expect(\"]\"), poplex, me);\n }\n function quasi(type, value) {\n if (type != \"quasi\") return pass();\n if (value.slice(value.length - 2) != \"${\") return cont(quasi);\n return cont(expression, continueQuasi);\n }\n function continueQuasi(type) {\n if (type == \"}\") {\n cx.marked = \"string-2\";\n cx.state.tokenize = tokenQuasi;\n return cont(quasi);\n }\n }\n function arrowBody(type) {\n findFatArrow(cx.stream, cx.state);\n return pass(type == \"{\" ? statement : expression);\n }\n function arrowBodyNoComma(type) {\n findFatArrow(cx.stream, cx.state);\n return pass(type == \"{\" ? statement : expressionNoComma);\n }\n function maybeTarget(noComma) {\n return function(type) {\n if (type == \".\") return cont(noComma ? targetNoComma : target);\n else return pass(noComma ? expressionNoComma : expression);\n };\n }\n function target(_, value) {\n if (value == \"target\") { cx.marked = \"keyword\"; return cont(maybeoperatorComma); }\n }\n function targetNoComma(_, value) {\n if (value == \"target\") { cx.marked = \"keyword\"; return cont(maybeoperatorNoComma); }\n }\n function maybelabel(type) {\n if (type == \":\") return cont(poplex, statement);\n return pass(maybeoperatorComma, expect(\";\"), poplex);\n }\n function property(type) {\n if (type == \"variable\") {cx.marked = \"property\"; return cont();}\n }\n function objprop(type, value) {\n if (type == \"variable\" || cx.style == \"keyword\") {\n cx.marked = \"property\";\n if (value == \"get\" || value == \"set\") return cont(getterSetter);\n return cont(afterprop);\n } else if (type == \"number\" || type == \"string\") {\n cx.marked = jsonldMode ? \"property\" : (cx.style + \" property\");\n return cont(afterprop);\n } else if (type == \"jsonld-keyword\") {\n return cont(afterprop);\n } else if (type == \"modifier\") {\n return cont(objprop)\n } else if (type == \"[\") {\n return cont(expression, expect(\"]\"), afterprop);\n } else if (type == \"spread\") {\n return cont(expression);\n }\n }\n function getterSetter(type) {\n if (type != \"variable\") return pass(afterprop);\n cx.marked = \"property\";\n return cont(functiondef);\n }\n function afterprop(type) {\n if (type == \":\") return cont(expressionNoComma);\n if (type == \"(\") return pass(functiondef);\n }\n function commasep(what, end) {\n function proceed(type) {\n if (type == \",\") {\n var lex = cx.state.lexical;\n if (lex.info == \"call\") lex.pos = (lex.pos || 0) + 1;\n return cont(what, proceed);\n }\n if (type == end) return cont();\n return cont(expect(end));\n }\n return function(type) {\n if (type == end) return cont();\n return pass(what, proceed);\n };\n }\n function contCommasep(what, end, info) {\n for (var i = 3; i < arguments.length; i++)\n cx.cc.push(arguments[i]);\n return cont(pushlex(end, info), commasep(what, end), poplex);\n }\n function block(type) {\n if (type == \"}\") return cont();\n return pass(statement, block);\n }\n function maybetype(type) {\n if (isTS && type == \":\") return cont(typedef);\n }\n function maybedefault(_, value) {\n if (value == \"=\") return cont(expressionNoComma);\n }\n function typedef(type) {\n if (type == \"variable\") {cx.marked = \"variable-3\"; return cont();}\n }\n function vardef() {\n return pass(pattern, maybetype, maybeAssign, vardefCont);\n }\n function pattern(type, value) {\n if (type == \"modifier\") return cont(pattern)\n if (type == \"variable\") { register(value); return cont(); }\n if (type == \"spread\") return cont(pattern);\n if (type == \"[\") return contCommasep(pattern, \"]\");\n if (type == \"{\") return contCommasep(proppattern, \"}\");\n }\n function proppattern(type, value) {\n if (type == \"variable\" && !cx.stream.match(/^\\s*:/, false)) {\n register(value);\n return cont(maybeAssign);\n }\n if (type == \"variable\") cx.marked = \"property\";\n if (type == \"spread\") return cont(pattern);\n if (type == \"}\") return pass();\n return cont(expect(\":\"), pattern, maybeAssign);\n }\n function maybeAssign(_type, value) {\n if (value == \"=\") return cont(expressionNoComma);\n }\n function vardefCont(type) {\n if (type == \",\") return cont(vardef);\n }\n function maybeelse(type, value) {\n if (type == \"keyword b\" && value == \"else\") return cont(pushlex(\"form\", \"else\"), statement, poplex);\n }\n function forspec(type) {\n if (type == \"(\") return cont(pushlex(\")\"), forspec1, expect(\")\"), poplex);\n }\n function forspec1(type) {\n if (type == \"var\") return cont(vardef, expect(\";\"), forspec2);\n if (type == \";\") return cont(forspec2);\n if (type == \"variable\") return cont(formaybeinof);\n return pass(expression, expect(\";\"), forspec2);\n }\n function formaybeinof(_type, value) {\n if (value == \"in\" || value == \"of\") { cx.marked = \"keyword\"; return cont(expression); }\n return cont(maybeoperatorComma, forspec2);\n }\n function forspec2(type, value) {\n if (type == \";\") return cont(forspec3);\n if (value == \"in\" || value == \"of\") { cx.marked = \"keyword\"; return cont(expression); }\n return pass(expression, expect(\";\"), forspec3);\n }\n function forspec3(type) {\n if (type != \")\") cont(expression);\n }\n function functiondef(type, value) {\n if (value == \"*\") {cx.marked = \"keyword\"; return cont(functiondef);}\n if (type == \"variable\") {register(value); return cont(functiondef);}\n if (type == \"(\") return cont(pushcontext, pushlex(\")\"), commasep(funarg, \")\"), poplex, statement, popcontext);\n }\n function funarg(type) {\n if (type == \"spread\") return cont(funarg);\n return pass(pattern, maybetype, maybedefault);\n }\n function className(type, value) {\n if (type == \"variable\") {register(value); return cont(classNameAfter);}\n }\n function classNameAfter(type, value) {\n if (value == \"extends\") return cont(expression, classNameAfter);\n if (type == \"{\") return cont(pushlex(\"}\"), classBody, poplex);\n }\n function classBody(type, value) {\n if (type == \"variable\" || cx.style == \"keyword\") {\n if (value == \"static\") {\n cx.marked = \"keyword\";\n return cont(classBody);\n }\n cx.marked = \"property\";\n if (value == \"get\" || value == \"set\") return cont(classGetterSetter, functiondef, classBody);\n return cont(functiondef, classBody);\n }\n if (value == \"*\") {\n cx.marked = \"keyword\";\n return cont(classBody);\n }\n if (type == \";\") return cont(classBody);\n if (type == \"}\") return cont();\n }\n function classGetterSetter(type) {\n if (type != \"variable\") return pass();\n cx.marked = \"property\";\n return cont();\n }\n function afterExport(_type, value) {\n if (value == \"*\") { cx.marked = \"keyword\"; return cont(maybeFrom, expect(\";\")); }\n if (value == \"default\") { cx.marked = \"keyword\"; return cont(expression, expect(\";\")); }\n return pass(statement);\n }\n function afterImport(type) {\n if (type == \"string\") return cont();\n return pass(importSpec, maybeFrom);\n }\n function importSpec(type, value) {\n if (type == \"{\") return contCommasep(importSpec, \"}\");\n if (type == \"variable\") register(value);\n if (value == \"*\") cx.marked = \"keyword\";\n return cont(maybeAs);\n }\n function maybeAs(_type, value) {\n if (value == \"as\") { cx.marked = \"keyword\"; return cont(importSpec); }\n }\n function maybeFrom(_type, value) {\n if (value == \"from\") { cx.marked = \"keyword\"; return cont(expression); }\n }\n function arrayLiteral(type) {\n if (type == \"]\") return cont();\n return pass(expressionNoComma, maybeArrayComprehension);\n }\n function maybeArrayComprehension(type) {\n if (type == \"for\") return pass(comprehension, expect(\"]\"));\n if (type == \",\") return cont(commasep(maybeexpressionNoComma, \"]\"));\n return pass(commasep(expressionNoComma, \"]\"));\n }\n function comprehension(type) {\n if (type == \"for\") return cont(forspec, comprehension);\n if (type == \"if\") return cont(expression, comprehension);\n }\n\n function isContinuedStatement(state, textAfter) {\n return state.lastType == \"operator\" || state.lastType == \",\" ||\n isOperatorChar.test(textAfter.charAt(0)) ||\n /[,.]/.test(textAfter.charAt(0));\n }\n\n // Interface\n\n return {\n startState: function(basecolumn) {\n var state = {\n tokenize: tokenBase,\n lastType: \"sof\",\n cc: [],\n lexical: new JSLexical((basecolumn || 0) - indentUnit, 0, \"block\", false),\n localVars: parserConfig.localVars,\n context: parserConfig.localVars && {vars: parserConfig.localVars},\n indented: basecolumn || 0\n };\n if (parserConfig.globalVars && typeof parserConfig.globalVars == \"object\")\n state.globalVars = parserConfig.globalVars;\n return state;\n },\n\n token: function(stream, state) {\n if (stream.sol()) {\n if (!state.lexical.hasOwnProperty(\"align\"))\n state.lexical.align = false;\n state.indented = stream.indentation();\n findFatArrow(stream, state);\n }\n if (state.tokenize != tokenComment && stream.eatSpace()) return null;\n var style = state.tokenize(stream, state);\n if (type == \"comment\") return style;\n state.lastType = type == \"operator\" && (content == \"++\" || content == \"--\") ? \"incdec\" : type;\n return parseJS(state, style, type, content, stream);\n },\n\n indent: function(state, textAfter) {\n if (state.tokenize == tokenComment) return CodeMirror.Pass;\n if (state.tokenize != tokenBase) return 0;\n var firstChar = textAfter && textAfter.charAt(0), lexical = state.lexical;\n // Kludge to prevent 'maybelse' from blocking lexical scope pops\n if (!/^\\s*else\\b/.test(textAfter)) for (var i = state.cc.length - 1; i >= 0; --i) {\n var c = state.cc[i];\n if (c == poplex) lexical = lexical.prev;\n else if (c != maybeelse) break;\n }\n if (lexical.type == \"stat\" && firstChar == \"}\") lexical = lexical.prev;\n if (statementIndent && lexical.type == \")\" && lexical.prev.type == \"stat\")\n lexical = lexical.prev;\n var type = lexical.type, closing = firstChar == type;\n\n if (type == \"vardef\") return lexical.indented + (state.lastType == \"operator\" || state.lastType == \",\" ? lexical.info + 1 : 0);\n else if (type == \"form\" && firstChar == \"{\") return lexical.indented;\n else if (type == \"form\") return lexical.indented + indentUnit;\n else if (type == \"stat\")\n return lexical.indented + (isContinuedStatement(state, textAfter) ? statementIndent || indentUnit : 0);\n else if (lexical.info == \"switch\" && !closing && parserConfig.doubleIndentSwitch != false)\n return lexical.indented + (/^(?:case|default)\\b/.test(textAfter) ? indentUnit : 2 * indentUnit);\n else if (lexical.align) return lexical.column + (closing ? 0 : 1);\n else return lexical.indented + (closing ? 0 : indentUnit);\n },\n\n electricInput: /^\\s*(?:case .*?:|default:|\\{|\\})$/,\n blockCommentStart: jsonMode ? null : \"/*\",\n blockCommentEnd: jsonMode ? null : \"*/\",\n lineComment: jsonMode ? null : \"//\",\n fold: \"brace\",\n closeBrackets: \"()[]{}''\\\"\\\"``\",\n\n helperType: jsonMode ? \"json\" : \"javascript\",\n jsonldMode: jsonldMode,\n jsonMode: jsonMode,\n\n expressionAllowed: expressionAllowed,\n skipExpression: function(state) {\n var top = state.cc[state.cc.length - 1]\n if (top == expression || top == expressionNoComma) state.cc.pop()\n }\n };\n});\n\nCodeMirror.registerHelper(\"wordChars\", \"javascript\", /[\\w$]/);\n\nCodeMirror.defineMIME(\"text/javascript\", \"javascript\");\nCodeMirror.defineMIME(\"text/ecmascript\", \"javascript\");\nCodeMirror.defineMIME(\"application/javascript\", \"javascript\");\nCodeMirror.defineMIME(\"application/x-javascript\", \"javascript\");\nCodeMirror.defineMIME(\"application/ecmascript\", \"javascript\");\nCodeMirror.defineMIME(\"application/json\", {name: \"javascript\", json: true});\nCodeMirror.defineMIME(\"application/x-json\", {name: \"javascript\", json: true});\nCodeMirror.defineMIME(\"application/ld+json\", {name: \"javascript\", jsonld: true});\nCodeMirror.defineMIME(\"text/typescript\", { name: \"javascript\", typescript: true });\nCodeMirror.defineMIME(\"application/typescript\", { name: \"javascript\", typescript: true });\n\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/mode/markdown/markdown.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"), require(\"../xml/xml\"), require(\"../meta\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\", \"../xml/xml\", \"../meta\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n\"use strict\";\n\nCodeMirror.defineMode(\"markdown\", function(cmCfg, modeCfg) {\n\n var htmlMode = CodeMirror.getMode(cmCfg, \"text/html\");\n var htmlModeMissing = htmlMode.name == \"null\"\n\n function getMode(name) {\n if (CodeMirror.findModeByName) {\n var found = CodeMirror.findModeByName(name);\n if (found) name = found.mime || found.mimes[0];\n }\n var mode = CodeMirror.getMode(cmCfg, name);\n return mode.name == \"null\" ? null : mode;\n }\n\n // Should characters that affect highlighting be highlighted separate?\n // Does not include characters that will be output (such as `1.` and `-` for lists)\n if (modeCfg.highlightFormatting === undefined)\n modeCfg.highlightFormatting = false;\n\n // Maximum number of nested blockquotes. Set to 0 for infinite nesting.\n // Excess `>` will emit `error` token.\n if (modeCfg.maxBlockquoteDepth === undefined)\n modeCfg.maxBlockquoteDepth = 0;\n\n // Should underscores in words open/close em/strong?\n if (modeCfg.underscoresBreakWords === undefined)\n modeCfg.underscoresBreakWords = true;\n\n // Use `fencedCodeBlocks` to configure fenced code blocks. false to\n // disable, string to specify a precise regexp that the fence should\n // match, and true to allow three or more backticks or tildes (as\n // per CommonMark).\n\n // Turn on task lists? (\"- [ ] \" and \"- [x] \")\n if (modeCfg.taskLists === undefined) modeCfg.taskLists = false;\n\n // Turn on strikethrough syntax\n if (modeCfg.strikethrough === undefined)\n modeCfg.strikethrough = false;\n\n // Allow token types to be overridden by user-provided token types.\n if (modeCfg.tokenTypeOverrides === undefined)\n modeCfg.tokenTypeOverrides = {};\n\n var tokenTypes = {\n header: \"header\",\n code: \"comment\",\n quote: \"quote\",\n list1: \"variable-2\",\n list2: \"variable-3\",\n list3: \"keyword\",\n hr: \"hr\",\n image: \"tag\",\n formatting: \"formatting\",\n linkInline: \"link\",\n linkEmail: \"link\",\n linkText: \"link\",\n linkHref: \"string\",\n em: \"em\",\n strong: \"strong\",\n strikethrough: \"strikethrough\"\n };\n\n for (var tokenType in tokenTypes) {\n if (tokenTypes.hasOwnProperty(tokenType) && modeCfg.tokenTypeOverrides[tokenType]) {\n tokenTypes[tokenType] = modeCfg.tokenTypeOverrides[tokenType];\n }\n }\n\n var hrRE = /^([*\\-_])(?:\\s*\\1){2,}\\s*$/\n , ulRE = /^[*\\-+]\\s+/\n , olRE = /^[0-9]+([.)])\\s+/\n , taskListRE = /^\\[(x| )\\](?=\\s)/ // Must follow ulRE or olRE\n , atxHeaderRE = modeCfg.allowAtxHeaderWithoutSpace ? /^(#+)/ : /^(#+)(?: |$)/\n , setextHeaderRE = /^ *(?:\\={1,}|-{1,})\\s*$/\n , textRE = /^[^#!\\[\\]*_\\\\<>` \"'(~]+/\n , fencedCodeRE = new RegExp(\"^(\" + (modeCfg.fencedCodeBlocks === true ? \"~~~+|```+\" : modeCfg.fencedCodeBlocks) +\n \")[ \\\\t]*([\\\\w+#]*)\");\n\n function switchInline(stream, state, f) {\n state.f = state.inline = f;\n return f(stream, state);\n }\n\n function switchBlock(stream, state, f) {\n state.f = state.block = f;\n return f(stream, state);\n }\n\n function lineIsEmpty(line) {\n return !line || !/\\S/.test(line.string)\n }\n\n // Blocks\n\n function blankLine(state) {\n // Reset linkTitle state\n state.linkTitle = false;\n // Reset EM state\n state.em = false;\n // Reset STRONG state\n state.strong = false;\n // Reset strikethrough state\n state.strikethrough = false;\n // Reset state.quote\n state.quote = 0;\n // Reset state.indentedCode\n state.indentedCode = false;\n if (htmlModeMissing && state.f == htmlBlock) {\n state.f = inlineNormal;\n state.block = blockNormal;\n }\n // Reset state.trailingSpace\n state.trailingSpace = 0;\n state.trailingSpaceNewLine = false;\n // Mark this line as blank\n state.prevLine = state.thisLine\n state.thisLine = null\n return null;\n }\n\n function blockNormal(stream, state) {\n\n var sol = stream.sol();\n\n var prevLineIsList = state.list !== false,\n prevLineIsIndentedCode = state.indentedCode;\n\n state.indentedCode = false;\n\n if (prevLineIsList) {\n if (state.indentationDiff >= 0) { // Continued list\n if (state.indentationDiff < 4) { // Only adjust indentation if *not* a code block\n state.indentation -= state.indentationDiff;\n }\n state.list = null;\n } else if (state.indentation > 0) {\n state.list = null;\n } else { // No longer a list\n state.list = false;\n }\n }\n\n var match = null;\n if (state.indentationDiff >= 4) {\n stream.skipToEnd();\n if (prevLineIsIndentedCode || lineIsEmpty(state.prevLine)) {\n state.indentation -= 4;\n state.indentedCode = true;\n return tokenTypes.code;\n } else {\n return null;\n }\n } else if (stream.eatSpace()) {\n return null;\n } else if ((match = stream.match(atxHeaderRE)) && match[1].length <= 6) {\n state.header = match[1].length;\n if (modeCfg.highlightFormatting) state.formatting = \"header\";\n state.f = state.inline;\n return getType(state);\n } else if (!lineIsEmpty(state.prevLine) && !state.quote && !prevLineIsList &&\n !prevLineIsIndentedCode && (match = stream.match(setextHeaderRE))) {\n state.header = match[0].charAt(0) == '=' ? 1 : 2;\n if (modeCfg.highlightFormatting) state.formatting = \"header\";\n state.f = state.inline;\n return getType(state);\n } else if (stream.eat('>')) {\n state.quote = sol ? 1 : state.quote + 1;\n if (modeCfg.highlightFormatting) state.formatting = \"quote\";\n stream.eatSpace();\n return getType(state);\n } else if (stream.peek() === '[') {\n return switchInline(stream, state, footnoteLink);\n } else if (stream.match(hrRE, true)) {\n state.hr = true;\n return tokenTypes.hr;\n } else if ((lineIsEmpty(state.prevLine) || prevLineIsList) && (stream.match(ulRE, false) || stream.match(olRE, false))) {\n var listType = null;\n if (stream.match(ulRE, true)) {\n listType = 'ul';\n } else {\n stream.match(olRE, true);\n listType = 'ol';\n }\n state.indentation = stream.column() + stream.current().length;\n state.list = true;\n\n // While this list item's marker's indentation\n // is less than the deepest list item's content's indentation,\n // pop the deepest list item indentation off the stack.\n while (state.listStack && stream.column() < state.listStack[state.listStack.length - 1]) {\n state.listStack.pop();\n }\n\n // Add this list item's content's indentation to the stack\n state.listStack.push(state.indentation);\n\n if (modeCfg.taskLists && stream.match(taskListRE, false)) {\n state.taskList = true;\n }\n state.f = state.inline;\n if (modeCfg.highlightFormatting) state.formatting = [\"list\", \"list-\" + listType];\n return getType(state);\n } else if (modeCfg.fencedCodeBlocks && (match = stream.match(fencedCodeRE, true))) {\n state.fencedChars = match[1]\n // try switching mode\n state.localMode = getMode(match[2]);\n if (state.localMode) state.localState = state.localMode.startState();\n state.f = state.block = local;\n if (modeCfg.highlightFormatting) state.formatting = \"code-block\";\n state.code = -1\n return getType(state);\n }\n\n return switchInline(stream, state, state.inline);\n }\n\n function htmlBlock(stream, state) {\n var style = htmlMode.token(stream, state.htmlState);\n if (!htmlModeMissing) {\n var inner = CodeMirror.innerMode(htmlMode, state.htmlState)\n if ((inner.mode.name == \"xml\" && inner.state.tagStart === null &&\n (!inner.state.context && inner.state.tokenize.isInText)) ||\n (state.md_inside && stream.current().indexOf(\">\") > -1)) {\n state.f = inlineNormal;\n state.block = blockNormal;\n state.htmlState = null;\n }\n }\n return style;\n }\n\n function local(stream, state) {\n if (state.fencedChars && stream.match(state.fencedChars, false)) {\n state.localMode = state.localState = null;\n state.f = state.block = leavingLocal;\n return null;\n } else if (state.localMode) {\n return state.localMode.token(stream, state.localState);\n } else {\n stream.skipToEnd();\n return tokenTypes.code;\n }\n }\n\n function leavingLocal(stream, state) {\n stream.match(state.fencedChars);\n state.block = blockNormal;\n state.f = inlineNormal;\n state.fencedChars = null;\n if (modeCfg.highlightFormatting) state.formatting = \"code-block\";\n state.code = 1\n var returnType = getType(state);\n state.code = 0\n return returnType;\n }\n\n // Inline\n function getType(state) {\n var styles = [];\n\n if (state.formatting) {\n styles.push(tokenTypes.formatting);\n\n if (typeof state.formatting === \"string\") state.formatting = [state.formatting];\n\n for (var i = 0; i < state.formatting.length; i++) {\n styles.push(tokenTypes.formatting + \"-\" + state.formatting[i]);\n\n if (state.formatting[i] === \"header\") {\n styles.push(tokenTypes.formatting + \"-\" + state.formatting[i] + \"-\" + state.header);\n }\n\n // Add `formatting-quote` and `formatting-quote-#` for blockquotes\n // Add `error` instead if the maximum blockquote nesting depth is passed\n if (state.formatting[i] === \"quote\") {\n if (!modeCfg.maxBlockquoteDepth || modeCfg.maxBlockquoteDepth >= state.quote) {\n styles.push(tokenTypes.formatting + \"-\" + state.formatting[i] + \"-\" + state.quote);\n } else {\n styles.push(\"error\");\n }\n }\n }\n }\n\n if (state.taskOpen) {\n styles.push(\"meta\");\n return styles.length ? styles.join(' ') : null;\n }\n if (state.taskClosed) {\n styles.push(\"property\");\n return styles.length ? styles.join(' ') : null;\n }\n\n if (state.linkHref) {\n styles.push(tokenTypes.linkHref, \"url\");\n } else { // Only apply inline styles to non-url text\n if (state.strong) { styles.push(tokenTypes.strong); }\n if (state.em) { styles.push(tokenTypes.em); }\n if (state.strikethrough) { styles.push(tokenTypes.strikethrough); }\n if (state.linkText) { styles.push(tokenTypes.linkText); }\n if (state.code) { styles.push(tokenTypes.code); }\n }\n\n if (state.header) { styles.push(tokenTypes.header, tokenTypes.header + \"-\" + state.header); }\n\n if (state.quote) {\n styles.push(tokenTypes.quote);\n\n // Add `quote-#` where the maximum for `#` is modeCfg.maxBlockquoteDepth\n if (!modeCfg.maxBlockquoteDepth || modeCfg.maxBlockquoteDepth >= state.quote) {\n styles.push(tokenTypes.quote + \"-\" + state.quote);\n } else {\n styles.push(tokenTypes.quote + \"-\" + modeCfg.maxBlockquoteDepth);\n }\n }\n\n if (state.list !== false) {\n var listMod = (state.listStack.length - 1) % 3;\n if (!listMod) {\n styles.push(tokenTypes.list1);\n } else if (listMod === 1) {\n styles.push(tokenTypes.list2);\n } else {\n styles.push(tokenTypes.list3);\n }\n }\n\n if (state.trailingSpaceNewLine) {\n styles.push(\"trailing-space-new-line\");\n } else if (state.trailingSpace) {\n styles.push(\"trailing-space-\" + (state.trailingSpace % 2 ? \"a\" : \"b\"));\n }\n\n return styles.length ? styles.join(' ') : null;\n }\n\n function handleText(stream, state) {\n if (stream.match(textRE, true)) {\n return getType(state);\n }\n return undefined;\n }\n\n function inlineNormal(stream, state) {\n var style = state.text(stream, state);\n if (typeof style !== 'undefined')\n return style;\n\n if (state.list) { // List marker (*, +, -, 1., etc)\n state.list = null;\n return getType(state);\n }\n\n if (state.taskList) {\n var taskOpen = stream.match(taskListRE, true)[1] !== \"x\";\n if (taskOpen) state.taskOpen = true;\n else state.taskClosed = true;\n if (modeCfg.highlightFormatting) state.formatting = \"task\";\n state.taskList = false;\n return getType(state);\n }\n\n state.taskOpen = false;\n state.taskClosed = false;\n\n if (state.header && stream.match(/^#+$/, true)) {\n if (modeCfg.highlightFormatting) state.formatting = \"header\";\n return getType(state);\n }\n\n // Get sol() value now, before character is consumed\n var sol = stream.sol();\n\n var ch = stream.next();\n\n // Matches link titles present on next line\n if (state.linkTitle) {\n state.linkTitle = false;\n var matchCh = ch;\n if (ch === '(') {\n matchCh = ')';\n }\n matchCh = (matchCh+'').replace(/([.?*+^$[\\]\\\\(){}|-])/g, \"\\\\$1\");\n var regex = '^\\\\s*(?:[^' + matchCh + '\\\\\\\\]+|\\\\\\\\\\\\\\\\|\\\\\\\\.)' + matchCh;\n if (stream.match(new RegExp(regex), true)) {\n return tokenTypes.linkHref;\n }\n }\n\n // If this block is changed, it may need to be updated in GFM mode\n if (ch === '`') {\n var previousFormatting = state.formatting;\n if (modeCfg.highlightFormatting) state.formatting = \"code\";\n stream.eatWhile('`');\n var count = stream.current().length\n if (state.code == 0) {\n state.code = count\n return getType(state)\n } else if (count == state.code) { // Must be exact\n var t = getType(state)\n state.code = 0\n return t\n } else {\n state.formatting = previousFormatting\n return getType(state)\n }\n } else if (state.code) {\n return getType(state);\n }\n\n if (ch === '\\\\') {\n stream.next();\n if (modeCfg.highlightFormatting) {\n var type = getType(state);\n var formattingEscape = tokenTypes.formatting + \"-escape\";\n return type ? type + \" \" + formattingEscape : formattingEscape;\n }\n }\n\n if (ch === '!' && stream.match(/\\[[^\\]]*\\] ?(?:\\(|\\[)/, false)) {\n stream.match(/\\[[^\\]]*\\]/);\n state.inline = state.f = linkHref;\n return tokenTypes.image;\n }\n\n if (ch === '[' && stream.match(/.*\\](\\(.*\\)| ?\\[.*\\])/, false)) {\n state.linkText = true;\n if (modeCfg.highlightFormatting) state.formatting = \"link\";\n return getType(state);\n }\n\n if (ch === ']' && state.linkText && stream.match(/\\(.*\\)| ?\\[.*\\]/, false)) {\n if (modeCfg.highlightFormatting) state.formatting = \"link\";\n var type = getType(state);\n state.linkText = false;\n state.inline = state.f = linkHref;\n return type;\n }\n\n if (ch === '<' && stream.match(/^(https?|ftps?):\\/\\/(?:[^\\\\>]|\\\\.)+>/, false)) {\n state.f = state.inline = linkInline;\n if (modeCfg.highlightFormatting) state.formatting = \"link\";\n var type = getType(state);\n if (type){\n type += \" \";\n } else {\n type = \"\";\n }\n return type + tokenTypes.linkInline;\n }\n\n if (ch === '<' && stream.match(/^[^> \\\\]+@(?:[^\\\\>]|\\\\.)+>/, false)) {\n state.f = state.inline = linkInline;\n if (modeCfg.highlightFormatting) state.formatting = \"link\";\n var type = getType(state);\n if (type){\n type += \" \";\n } else {\n type = \"\";\n }\n return type + tokenTypes.linkEmail;\n }\n\n if (ch === '<' && stream.match(/^(!--|\\w)/, false)) {\n var end = stream.string.indexOf(\">\", stream.pos);\n if (end != -1) {\n var atts = stream.string.substring(stream.start, end);\n if (/markdown\\s*=\\s*('|\"){0,1}1('|\"){0,1}/.test(atts)) state.md_inside = true;\n }\n stream.backUp(1);\n state.htmlState = CodeMirror.startState(htmlMode);\n return switchBlock(stream, state, htmlBlock);\n }\n\n if (ch === '<' && stream.match(/^\\/\\w*?>/)) {\n state.md_inside = false;\n return \"tag\";\n }\n\n var ignoreUnderscore = false;\n if (!modeCfg.underscoresBreakWords) {\n if (ch === '_' && stream.peek() !== '_' && stream.match(/(\\w)/, false)) {\n var prevPos = stream.pos - 2;\n if (prevPos >= 0) {\n var prevCh = stream.string.charAt(prevPos);\n if (prevCh !== '_' && prevCh.match(/(\\w)/, false)) {\n ignoreUnderscore = true;\n }\n }\n }\n }\n if (ch === '*' || (ch === '_' && !ignoreUnderscore)) {\n if (sol && stream.peek() === ' ') {\n // Do nothing, surrounded by newline and space\n } else if (state.strong === ch && stream.eat(ch)) { // Remove STRONG\n if (modeCfg.highlightFormatting) state.formatting = \"strong\";\n var t = getType(state);\n state.strong = false;\n return t;\n } else if (!state.strong && stream.eat(ch)) { // Add STRONG\n state.strong = ch;\n if (modeCfg.highlightFormatting) state.formatting = \"strong\";\n return getType(state);\n } else if (state.em === ch) { // Remove EM\n if (modeCfg.highlightFormatting) state.formatting = \"em\";\n var t = getType(state);\n state.em = false;\n return t;\n } else if (!state.em) { // Add EM\n state.em = ch;\n if (modeCfg.highlightFormatting) state.formatting = \"em\";\n return getType(state);\n }\n } else if (ch === ' ') {\n if (stream.eat('*') || stream.eat('_')) { // Probably surrounded by spaces\n if (stream.peek() === ' ') { // Surrounded by spaces, ignore\n return getType(state);\n } else { // Not surrounded by spaces, back up pointer\n stream.backUp(1);\n }\n }\n }\n\n if (modeCfg.strikethrough) {\n if (ch === '~' && stream.eatWhile(ch)) {\n if (state.strikethrough) {// Remove strikethrough\n if (modeCfg.highlightFormatting) state.formatting = \"strikethrough\";\n var t = getType(state);\n state.strikethrough = false;\n return t;\n } else if (stream.match(/^[^\\s]/, false)) {// Add strikethrough\n state.strikethrough = true;\n if (modeCfg.highlightFormatting) state.formatting = \"strikethrough\";\n return getType(state);\n }\n } else if (ch === ' ') {\n if (stream.match(/^~~/, true)) { // Probably surrounded by space\n if (stream.peek() === ' ') { // Surrounded by spaces, ignore\n return getType(state);\n } else { // Not surrounded by spaces, back up pointer\n stream.backUp(2);\n }\n }\n }\n }\n\n if (ch === ' ') {\n if (stream.match(/ +$/, false)) {\n state.trailingSpace++;\n } else if (state.trailingSpace) {\n state.trailingSpaceNewLine = true;\n }\n }\n\n return getType(state);\n }\n\n function linkInline(stream, state) {\n var ch = stream.next();\n\n if (ch === \">\") {\n state.f = state.inline = inlineNormal;\n if (modeCfg.highlightFormatting) state.formatting = \"link\";\n var type = getType(state);\n if (type){\n type += \" \";\n } else {\n type = \"\";\n }\n return type + tokenTypes.linkInline;\n }\n\n stream.match(/^[^>]+/, true);\n\n return tokenTypes.linkInline;\n }\n\n function linkHref(stream, state) {\n // Check if space, and return NULL if so (to avoid marking the space)\n if(stream.eatSpace()){\n return null;\n }\n var ch = stream.next();\n if (ch === '(' || ch === '[') {\n state.f = state.inline = getLinkHrefInside(ch === \"(\" ? \")\" : \"]\");\n if (modeCfg.highlightFormatting) state.formatting = \"link-string\";\n state.linkHref = true;\n return getType(state);\n }\n return 'error';\n }\n\n function getLinkHrefInside(endChar) {\n return function(stream, state) {\n var ch = stream.next();\n\n if (ch === endChar) {\n state.f = state.inline = inlineNormal;\n if (modeCfg.highlightFormatting) state.formatting = \"link-string\";\n var returnState = getType(state);\n state.linkHref = false;\n return returnState;\n }\n\n if (stream.match(inlineRE(endChar), true)) {\n stream.backUp(1);\n }\n\n state.linkHref = true;\n return getType(state);\n };\n }\n\n function footnoteLink(stream, state) {\n if (stream.match(/^([^\\]\\\\]|\\\\.)*\\]:/, false)) {\n state.f = footnoteLinkInside;\n stream.next(); // Consume [\n if (modeCfg.highlightFormatting) state.formatting = \"link\";\n state.linkText = true;\n return getType(state);\n }\n return switchInline(stream, state, inlineNormal);\n }\n\n function footnoteLinkInside(stream, state) {\n if (stream.match(/^\\]:/, true)) {\n state.f = state.inline = footnoteUrl;\n if (modeCfg.highlightFormatting) state.formatting = \"link\";\n var returnType = getType(state);\n state.linkText = false;\n return returnType;\n }\n\n stream.match(/^([^\\]\\\\]|\\\\.)+/, true);\n\n return tokenTypes.linkText;\n }\n\n function footnoteUrl(stream, state) {\n // Check if space, and return NULL if so (to avoid marking the space)\n if(stream.eatSpace()){\n return null;\n }\n // Match URL\n stream.match(/^[^\\s]+/, true);\n // Check for link title\n if (stream.peek() === undefined) { // End of line, set flag to check next line\n state.linkTitle = true;\n } else { // More content on line, check if link title\n stream.match(/^(?:\\s+(?:\"(?:[^\"\\\\]|\\\\\\\\|\\\\.)+\"|'(?:[^'\\\\]|\\\\\\\\|\\\\.)+'|\\((?:[^)\\\\]|\\\\\\\\|\\\\.)+\\)))?/, true);\n }\n state.f = state.inline = inlineNormal;\n return tokenTypes.linkHref + \" url\";\n }\n\n var savedInlineRE = [];\n function inlineRE(endChar) {\n if (!savedInlineRE[endChar]) {\n // Escape endChar for RegExp (taken from http://stackoverflow.com/a/494122/526741)\n endChar = (endChar+'').replace(/([.?*+^$[\\]\\\\(){}|-])/g, \"\\\\$1\");\n // Match any non-endChar, escaped character, as well as the closing\n // endChar.\n savedInlineRE[endChar] = new RegExp('^(?:[^\\\\\\\\]|\\\\\\\\.)*?(' + endChar + ')');\n }\n return savedInlineRE[endChar];\n }\n\n var mode = {\n startState: function() {\n return {\n f: blockNormal,\n\n prevLine: null,\n thisLine: null,\n\n block: blockNormal,\n htmlState: null,\n indentation: 0,\n\n inline: inlineNormal,\n text: handleText,\n\n formatting: false,\n linkText: false,\n linkHref: false,\n linkTitle: false,\n code: 0,\n em: false,\n strong: false,\n header: 0,\n hr: false,\n taskList: false,\n list: false,\n listStack: [],\n quote: 0,\n trailingSpace: 0,\n trailingSpaceNewLine: false,\n strikethrough: false,\n fencedChars: null\n };\n },\n\n copyState: function(s) {\n return {\n f: s.f,\n\n prevLine: s.prevLine,\n thisLine: s.thisLine,\n\n block: s.block,\n htmlState: s.htmlState && CodeMirror.copyState(htmlMode, s.htmlState),\n indentation: s.indentation,\n\n localMode: s.localMode,\n localState: s.localMode ? CodeMirror.copyState(s.localMode, s.localState) : null,\n\n inline: s.inline,\n text: s.text,\n formatting: false,\n linkTitle: s.linkTitle,\n code: s.code,\n em: s.em,\n strong: s.strong,\n strikethrough: s.strikethrough,\n header: s.header,\n hr: s.hr,\n taskList: s.taskList,\n list: s.list,\n listStack: s.listStack.slice(0),\n quote: s.quote,\n indentedCode: s.indentedCode,\n trailingSpace: s.trailingSpace,\n trailingSpaceNewLine: s.trailingSpaceNewLine,\n md_inside: s.md_inside,\n fencedChars: s.fencedChars\n };\n },\n\n token: function(stream, state) {\n\n // Reset state.formatting\n state.formatting = false;\n\n if (stream != state.thisLine) {\n var forceBlankLine = state.header || state.hr;\n\n // Reset state.header and state.hr\n state.header = 0;\n state.hr = false;\n\n if (stream.match(/^\\s*$/, true) || forceBlankLine) {\n blankLine(state);\n if (!forceBlankLine) return null\n state.prevLine = null\n }\n\n state.prevLine = state.thisLine\n state.thisLine = stream\n\n // Reset state.taskList\n state.taskList = false;\n\n // Reset state.trailingSpace\n state.trailingSpace = 0;\n state.trailingSpaceNewLine = false;\n\n state.f = state.block;\n var indentation = stream.match(/^\\s*/, true)[0].replace(/\\t/g, ' ').length;\n state.indentationDiff = Math.min(indentation - state.indentation, 4);\n state.indentation = state.indentation + state.indentationDiff;\n if (indentation > 0) return null;\n }\n return state.f(stream, state);\n },\n\n innerMode: function(state) {\n if (state.block == htmlBlock) return {state: state.htmlState, mode: htmlMode};\n if (state.localState) return {state: state.localState, mode: state.localMode};\n return {state: state, mode: mode};\n },\n\n blankLine: blankLine,\n\n getType: getType,\n\n fold: \"markdown\"\n };\n return mode;\n}, \"xml\");\n\nCodeMirror.defineMIME(\"text/x-markdown\", \"markdown\");\n\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/markdown/markdown.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/mode/meta.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n \"use strict\";\n\n CodeMirror.modeInfo = [\n {name: \"APL\", mime: \"text/apl\", mode: \"apl\", ext: [\"dyalog\", \"apl\"]},\n {name: \"PGP\", mimes: [\"application/pgp\", \"application/pgp-keys\", \"application/pgp-signature\"], mode: \"asciiarmor\", ext: [\"pgp\"]},\n {name: \"ASN.1\", mime: \"text/x-ttcn-asn\", mode: \"asn.1\", ext: [\"asn\", \"asn1\"]},\n {name: \"Asterisk\", mime: \"text/x-asterisk\", mode: \"asterisk\", file: /^extensions\\.conf$/i},\n {name: \"Brainfuck\", mime: \"text/x-brainfuck\", mode: \"brainfuck\", ext: [\"b\", \"bf\"]},\n {name: \"C\", mime: \"text/x-csrc\", mode: \"clike\", ext: [\"c\", \"h\"]},\n {name: \"C++\", mime: \"text/x-c++src\", mode: \"clike\", ext: [\"cpp\", \"c++\", \"cc\", \"cxx\", \"hpp\", \"h++\", \"hh\", \"hxx\"], alias: [\"cpp\"]},\n {name: \"Cobol\", mime: \"text/x-cobol\", mode: \"cobol\", ext: [\"cob\", \"cpy\"]},\n {name: \"C#\", mime: \"text/x-csharp\", mode: \"clike\", ext: [\"cs\"], alias: [\"csharp\"]},\n {name: \"Clojure\", mime: \"text/x-clojure\", mode: \"clojure\", ext: [\"clj\", \"cljc\", \"cljx\"]},\n {name: \"ClojureScript\", mime: \"text/x-clojurescript\", mode: \"clojure\", ext: [\"cljs\"]},\n {name: \"Closure Stylesheets (GSS)\", mime: \"text/x-gss\", mode: \"css\", ext: [\"gss\"]},\n {name: \"CMake\", mime: \"text/x-cmake\", mode: \"cmake\", ext: [\"cmake\", \"cmake.in\"], file: /^CMakeLists.txt$/},\n {name: \"CoffeeScript\", mime: \"text/x-coffeescript\", mode: \"coffeescript\", ext: [\"coffee\"], alias: [\"coffee\", \"coffee-script\"]},\n {name: \"Common Lisp\", mime: \"text/x-common-lisp\", mode: \"commonlisp\", ext: [\"cl\", \"lisp\", \"el\"], alias: [\"lisp\"]},\n {name: \"Cypher\", mime: \"application/x-cypher-query\", mode: \"cypher\", ext: [\"cyp\", \"cypher\"]},\n {name: \"Cython\", mime: \"text/x-cython\", mode: \"python\", ext: [\"pyx\", \"pxd\", \"pxi\"]},\n {name: \"Crystal\", mime: \"text/x-crystal\", mode: \"crystal\", ext: [\"cr\"]},\n {name: \"CSS\", mime: \"text/css\", mode: \"css\", ext: [\"css\"]},\n {name: \"CQL\", mime: \"text/x-cassandra\", mode: \"sql\", ext: [\"cql\"]},\n {name: \"D\", mime: \"text/x-d\", mode: \"d\", ext: [\"d\"]},\n {name: \"Dart\", mimes: [\"application/dart\", \"text/x-dart\"], mode: \"dart\", ext: [\"dart\"]},\n {name: \"diff\", mime: \"text/x-diff\", mode: \"diff\", ext: [\"diff\", \"patch\"]},\n {name: \"Django\", mime: \"text/x-django\", mode: \"django\"},\n {name: \"Dockerfile\", mime: \"text/x-dockerfile\", mode: \"dockerfile\", file: /^Dockerfile$/},\n {name: \"DTD\", mime: \"application/xml-dtd\", mode: \"dtd\", ext: [\"dtd\"]},\n {name: \"Dylan\", mime: \"text/x-dylan\", mode: \"dylan\", ext: [\"dylan\", \"dyl\", \"intr\"]},\n {name: \"EBNF\", mime: \"text/x-ebnf\", mode: \"ebnf\"},\n {name: \"ECL\", mime: \"text/x-ecl\", mode: \"ecl\", ext: [\"ecl\"]},\n {name: \"edn\", mime: \"application/edn\", mode: \"clojure\", ext: [\"edn\"]},\n {name: \"Eiffel\", mime: \"text/x-eiffel\", mode: \"eiffel\", ext: [\"e\"]},\n {name: \"Elm\", mime: \"text/x-elm\", mode: \"elm\", ext: [\"elm\"]},\n {name: \"Embedded Javascript\", mime: \"application/x-ejs\", mode: \"htmlembedded\", ext: [\"ejs\"]},\n {name: \"Embedded Ruby\", mime: \"application/x-erb\", mode: \"htmlembedded\", ext: [\"erb\"]},\n {name: \"Erlang\", mime: \"text/x-erlang\", mode: \"erlang\", ext: [\"erl\"]},\n {name: \"Factor\", mime: \"text/x-factor\", mode: \"factor\", ext: [\"factor\"]},\n {name: \"FCL\", mime: \"text/x-fcl\", mode: \"fcl\"},\n {name: \"Forth\", mime: \"text/x-forth\", mode: \"forth\", ext: [\"forth\", \"fth\", \"4th\"]},\n {name: \"Fortran\", mime: \"text/x-fortran\", mode: \"fortran\", ext: [\"f\", \"for\", \"f77\", \"f90\"]},\n {name: \"F#\", mime: \"text/x-fsharp\", mode: \"mllike\", ext: [\"fs\"], alias: [\"fsharp\"]},\n {name: \"Gas\", mime: \"text/x-gas\", mode: \"gas\", ext: [\"s\"]},\n {name: \"Gherkin\", mime: \"text/x-feature\", mode: \"gherkin\", ext: [\"feature\"]},\n {name: \"GitHub Flavored Markdown\", mime: \"text/x-gfm\", mode: \"gfm\", file: /^(readme|contributing|history).md$/i},\n {name: \"Go\", mime: \"text/x-go\", mode: \"go\", ext: [\"go\"]},\n {name: \"Groovy\", mime: \"text/x-groovy\", mode: \"groovy\", ext: [\"groovy\", \"gradle\"]},\n {name: \"HAML\", mime: \"text/x-haml\", mode: \"haml\", ext: [\"haml\"]},\n {name: \"Haskell\", mime: \"text/x-haskell\", mode: \"haskell\", ext: [\"hs\"]},\n {name: \"Haskell (Literate)\", mime: \"text/x-literate-haskell\", mode: \"haskell-literate\", ext: [\"lhs\"]},\n {name: \"Haxe\", mime: \"text/x-haxe\", mode: \"haxe\", ext: [\"hx\"]},\n {name: \"HXML\", mime: \"text/x-hxml\", mode: \"haxe\", ext: [\"hxml\"]},\n {name: \"ASP.NET\", mime: \"application/x-aspx\", mode: \"htmlembedded\", ext: [\"aspx\"], alias: [\"asp\", \"aspx\"]},\n {name: \"HTML\", mime: \"text/html\", mode: \"htmlmixed\", ext: [\"html\", \"htm\"], alias: [\"xhtml\"]},\n {name: \"HTTP\", mime: \"message/http\", mode: \"http\"},\n {name: \"IDL\", mime: \"text/x-idl\", mode: \"idl\", ext: [\"pro\"]},\n {name: \"Jade\", mime: \"text/x-jade\", mode: \"jade\", ext: [\"jade\"]},\n {name: \"Java\", mime: \"text/x-java\", mode: \"clike\", ext: [\"java\"]},\n {name: \"Java Server Pages\", mime: \"application/x-jsp\", mode: \"htmlembedded\", ext: [\"jsp\"], alias: [\"jsp\"]},\n {name: \"JavaScript\", mimes: [\"text/javascript\", \"text/ecmascript\", \"application/javascript\", \"application/x-javascript\", \"application/ecmascript\"],\n mode: \"javascript\", ext: [\"js\"], alias: [\"ecmascript\", \"js\", \"node\"]},\n {name: \"JSON\", mimes: [\"application/json\", \"application/x-json\"], mode: \"javascript\", ext: [\"json\", \"map\"], alias: [\"json5\"]},\n {name: \"JSON-LD\", mime: \"application/ld+json\", mode: \"javascript\", ext: [\"jsonld\"], alias: [\"jsonld\"]},\n {name: \"JSX\", mime: \"text/jsx\", mode: \"jsx\", ext: [\"jsx\"]},\n {name: \"Jinja2\", mime: \"null\", mode: \"jinja2\"},\n {name: \"Julia\", mime: \"text/x-julia\", mode: \"julia\", ext: [\"jl\"]},\n {name: \"Kotlin\", mime: \"text/x-kotlin\", mode: \"clike\", ext: [\"kt\"]},\n {name: \"LESS\", mime: \"text/x-less\", mode: \"css\", ext: [\"less\"]},\n {name: \"LiveScript\", mime: \"text/x-livescript\", mode: \"livescript\", ext: [\"ls\"], alias: [\"ls\"]},\n {name: \"Lua\", mime: \"text/x-lua\", mode: \"lua\", ext: [\"lua\"]},\n {name: \"Markdown\", mime: \"text/x-markdown\", mode: \"markdown\", ext: [\"markdown\", \"md\", \"mkd\"]},\n {name: \"mIRC\", mime: \"text/mirc\", mode: \"mirc\"},\n {name: \"MariaDB SQL\", mime: \"text/x-mariadb\", mode: \"sql\"},\n {name: \"Mathematica\", mime: \"text/x-mathematica\", mode: \"mathematica\", ext: [\"m\", \"nb\"]},\n {name: \"Modelica\", mime: \"text/x-modelica\", mode: \"modelica\", ext: [\"mo\"]},\n {name: \"MUMPS\", mime: \"text/x-mumps\", mode: \"mumps\", ext: [\"mps\"]},\n {name: \"MS SQL\", mime: \"text/x-mssql\", mode: \"sql\"},\n {name: \"MySQL\", mime: \"text/x-mysql\", mode: \"sql\"},\n {name: \"Nginx\", mime: \"text/x-nginx-conf\", mode: \"nginx\", file: /nginx.*\\.conf$/i},\n {name: \"NSIS\", mime: \"text/x-nsis\", mode: \"nsis\", ext: [\"nsh\", \"nsi\"]},\n {name: \"NTriples\", mime: \"text/n-triples\", mode: \"ntriples\", ext: [\"nt\"]},\n {name: \"Objective C\", mime: \"text/x-objectivec\", mode: \"clike\", ext: [\"m\", \"mm\"]},\n {name: \"OCaml\", mime: \"text/x-ocaml\", mode: \"mllike\", ext: [\"ml\", \"mli\", \"mll\", \"mly\"]},\n {name: \"Octave\", mime: \"text/x-octave\", mode: \"octave\", ext: [\"m\"]},\n {name: \"Oz\", mime: \"text/x-oz\", mode: \"oz\", ext: [\"oz\"]},\n {name: \"Pascal\", mime: \"text/x-pascal\", mode: \"pascal\", ext: [\"p\", \"pas\"]},\n {name: \"PEG.js\", mime: \"null\", mode: \"pegjs\", ext: [\"jsonld\"]},\n {name: \"Perl\", mime: \"text/x-perl\", mode: \"perl\", ext: [\"pl\", \"pm\"]},\n {name: \"PHP\", mime: \"application/x-httpd-php\", mode: \"php\", ext: [\"php\", \"php3\", \"php4\", \"php5\", \"phtml\"]},\n {name: \"Pig\", mime: \"text/x-pig\", mode: \"pig\", ext: [\"pig\"]},\n {name: \"Plain Text\", mime: \"text/plain\", mode: \"null\", ext: [\"txt\", \"text\", \"conf\", \"def\", \"list\", \"log\"]},\n {name: \"PLSQL\", mime: \"text/x-plsql\", mode: \"sql\", ext: [\"pls\"]},\n {name: \"Properties files\", mime: \"text/x-properties\", mode: \"properties\", ext: [\"properties\", \"ini\", \"in\"], alias: [\"ini\", \"properties\"]},\n {name: \"ProtoBuf\", mime: \"text/x-protobuf\", mode: \"protobuf\", ext: [\"proto\"]},\n {name: \"Python\", mime: \"text/x-python\", mode: \"python\", ext: [\"py\", \"pyw\"]},\n {name: \"Puppet\", mime: \"text/x-puppet\", mode: \"puppet\", ext: [\"pp\"]},\n {name: \"Q\", mime: \"text/x-q\", mode: \"q\", ext: [\"q\"]},\n {name: \"R\", mime: \"text/x-rsrc\", mode: \"r\", ext: [\"r\"], alias: [\"rscript\"]},\n {name: \"reStructuredText\", mime: \"text/x-rst\", mode: \"rst\", ext: [\"rst\"], alias: [\"rst\"]},\n {name: \"RPM Changes\", mime: \"text/x-rpm-changes\", mode: \"rpm\"},\n {name: \"RPM Spec\", mime: \"text/x-rpm-spec\", mode: \"rpm\", ext: [\"spec\"]},\n {name: \"Ruby\", mime: \"text/x-ruby\", mode: \"ruby\", ext: [\"rb\"], alias: [\"jruby\", \"macruby\", \"rake\", \"rb\", \"rbx\"]},\n {name: \"Rust\", mime: \"text/x-rustsrc\", mode: \"rust\", ext: [\"rs\"]},\n {name: \"Sass\", mime: \"text/x-sass\", mode: \"sass\", ext: [\"sass\"]},\n {name: \"Scala\", mime: \"text/x-scala\", mode: \"clike\", ext: [\"scala\"]},\n {name: \"Scheme\", mime: \"text/x-scheme\", mode: \"scheme\", ext: [\"scm\", \"ss\"]},\n {name: \"SCSS\", mime: \"text/x-scss\", mode: \"css\", ext: [\"scss\"]},\n {name: \"Shell\", mime: \"text/x-sh\", mode: \"shell\", ext: [\"sh\", \"ksh\", \"bash\"], alias: [\"bash\", \"sh\", \"zsh\"], file: /^PKGBUILD$/},\n {name: \"Sieve\", mime: \"application/sieve\", mode: \"sieve\", ext: [\"siv\", \"sieve\"]},\n {name: \"Slim\", mimes: [\"text/x-slim\", \"application/x-slim\"], mode: \"slim\", ext: [\"slim\"]},\n {name: \"Smalltalk\", mime: \"text/x-stsrc\", mode: \"smalltalk\", ext: [\"st\"]},\n {name: \"Smarty\", mime: \"text/x-smarty\", mode: \"smarty\", ext: [\"tpl\"]},\n {name: \"Solr\", mime: \"text/x-solr\", mode: \"solr\"},\n {name: \"Soy\", mime: \"text/x-soy\", mode: \"soy\", ext: [\"soy\"], alias: [\"closure template\"]},\n {name: \"SPARQL\", mime: \"application/sparql-query\", mode: \"sparql\", ext: [\"rq\", \"sparql\"], alias: [\"sparul\"]},\n {name: \"Spreadsheet\", mime: \"text/x-spreadsheet\", mode: \"spreadsheet\", alias: [\"excel\", \"formula\"]},\n {name: \"SQL\", mime: \"text/x-sql\", mode: \"sql\", ext: [\"sql\"]},\n {name: \"Squirrel\", mime: \"text/x-squirrel\", mode: \"clike\", ext: [\"nut\"]},\n {name: \"Swift\", mime: \"text/x-swift\", mode: \"swift\", ext: [\"swift\"]},\n {name: \"sTeX\", mime: \"text/x-stex\", mode: \"stex\"},\n {name: \"LaTeX\", mime: \"text/x-latex\", mode: \"stex\", ext: [\"text\", \"ltx\"], alias: [\"tex\"]},\n {name: \"SystemVerilog\", mime: \"text/x-systemverilog\", mode: \"verilog\", ext: [\"v\"]},\n {name: \"Tcl\", mime: \"text/x-tcl\", mode: \"tcl\", ext: [\"tcl\"]},\n {name: \"Textile\", mime: \"text/x-textile\", mode: \"textile\", ext: [\"textile\"]},\n {name: \"TiddlyWiki \", mime: \"text/x-tiddlywiki\", mode: \"tiddlywiki\"},\n {name: \"Tiki wiki\", mime: \"text/tiki\", mode: \"tiki\"},\n {name: \"TOML\", mime: \"text/x-toml\", mode: \"toml\", ext: [\"toml\"]},\n {name: \"Tornado\", mime: \"text/x-tornado\", mode: \"tornado\"},\n {name: \"troff\", mime: \"text/troff\", mode: \"troff\", ext: [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"]},\n {name: \"TTCN\", mime: \"text/x-ttcn\", mode: \"ttcn\", ext: [\"ttcn\", \"ttcn3\", \"ttcnpp\"]},\n {name: \"TTCN_CFG\", mime: \"text/x-ttcn-cfg\", mode: \"ttcn-cfg\", ext: [\"cfg\"]},\n {name: \"Turtle\", mime: \"text/turtle\", mode: \"turtle\", ext: [\"ttl\"]},\n {name: \"TypeScript\", mime: \"application/typescript\", mode: \"javascript\", ext: [\"ts\"], alias: [\"ts\"]},\n {name: \"Twig\", mime: \"text/x-twig\", mode: \"twig\"},\n {name: \"VB.NET\", mime: \"text/x-vb\", mode: \"vb\", ext: [\"vb\"]},\n {name: \"VBScript\", mime: \"text/vbscript\", mode: \"vbscript\", ext: [\"vbs\"]},\n {name: \"Velocity\", mime: \"text/velocity\", mode: \"velocity\", ext: [\"vtl\"]},\n {name: \"Verilog\", mime: \"text/x-verilog\", mode: \"verilog\", ext: [\"v\"]},\n {name: \"VHDL\", mime: \"text/x-vhdl\", mode: \"vhdl\", ext: [\"vhd\", \"vhdl\"]},\n {name: \"XML\", mimes: [\"application/xml\", \"text/xml\"], mode: \"xml\", ext: [\"xml\", \"xsl\", \"xsd\"], alias: [\"rss\", \"wsdl\", \"xsd\"]},\n {name: \"XQuery\", mime: \"application/xquery\", mode: \"xquery\", ext: [\"xy\", \"xquery\"]},\n {name: \"YAML\", mime: \"text/x-yaml\", mode: \"yaml\", ext: [\"yaml\", \"yml\"], alias: [\"yml\"]},\n {name: \"Z80\", mime: \"text/x-z80\", mode: \"z80\", ext: [\"z80\"]},\n {name: \"mscgen\", mime: \"text/x-mscgen\", mode: \"mscgen\", ext: [\"mscgen\", \"mscin\", \"msc\"]},\n {name: \"xu\", mime: \"text/x-xu\", mode: \"mscgen\", ext: [\"xu\"]},\n {name: \"msgenny\", mime: \"text/x-msgenny\", mode: \"mscgen\", ext: [\"msgenny\"]}\n ];\n // Ensure all modes have a mime property for backwards compatibility\n for (var i = 0; i < CodeMirror.modeInfo.length; i++) {\n var info = CodeMirror.modeInfo[i];\n if (info.mimes) info.mime = info.mimes[0];\n }\n\n CodeMirror.findModeByMIME = function(mime) {\n mime = mime.toLowerCase();\n for (var i = 0; i < CodeMirror.modeInfo.length; i++) {\n var info = CodeMirror.modeInfo[i];\n if (info.mime == mime) return info;\n if (info.mimes) for (var j = 0; j < info.mimes.length; j++)\n if (info.mimes[j] == mime) return info;\n }\n };\n\n CodeMirror.findModeByExtension = function(ext) {\n for (var i = 0; i < CodeMirror.modeInfo.length; i++) {\n var info = CodeMirror.modeInfo[i];\n if (info.ext) for (var j = 0; j < info.ext.length; j++)\n if (info.ext[j] == ext) return info;\n }\n };\n\n CodeMirror.findModeByFileName = function(filename) {\n for (var i = 0; i < CodeMirror.modeInfo.length; i++) {\n var info = CodeMirror.modeInfo[i];\n if (info.file && info.file.test(filename)) return info;\n }\n var dot = filename.lastIndexOf(\".\");\n var ext = dot > -1 && filename.substring(dot + 1, filename.length);\n if (ext) return CodeMirror.findModeByExtension(ext);\n };\n\n CodeMirror.findModeByName = function(name) {\n name = name.toLowerCase();\n for (var i = 0; i < CodeMirror.modeInfo.length; i++) {\n var info = CodeMirror.modeInfo[i];\n if (info.name.toLowerCase() == name) return info;\n if (info.alias) for (var j = 0; j < info.alias.length; j++)\n if (info.alias[j].toLowerCase() == name) return info;\n }\n };\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/meta.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/mode/tiddlywiki/tiddlywiki.css": {
"text": "span.cm-underlined {\n text-decoration: underline;\n}\nspan.cm-strikethrough {\n text-decoration: line-through;\n}\nspan.cm-brace {\n color: #170;\n font-weight: bold;\n}\nspan.cm-table {\n color: blue;\n font-weight: bold;\n}\n",
"type": "text/css",
"title": "$:/plugins/tiddlywiki/codemirror/mode/tiddlywiki/tiddlywiki.css",
"tags": "[[$:/tags/Stylesheet]]"
},
"$:/plugins/tiddlywiki/codemirror/mode/tiddlywiki/tiddlywiki.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n/***\n |''Name''|tiddlywiki.js|\n |''Description''|Enables TiddlyWikiy syntax highlighting using CodeMirror|\n |''Author''|PMario|\n |''Version''|0.1.7|\n |''Status''|''stable''|\n |''Source''|[[GitHub|https://github.com/pmario/CodeMirror2/blob/tw-syntax/mode/tiddlywiki]]|\n |''Documentation''|http://codemirror.tiddlyspace.com/|\n |''License''|[[MIT License|http://www.opensource.org/licenses/mit-license.php]]|\n |''CoreVersion''|2.5.0|\n |''Requires''|codemirror.js|\n |''Keywords''|syntax highlighting color code mirror codemirror|\n ! Info\n CoreVersion parameter is needed for TiddlyWiki only!\n***/\n//{{{\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n\"use strict\";\n\nCodeMirror.defineMode(\"tiddlywiki\", function () {\n // Tokenizer\n var textwords = {};\n\n var keywords = function () {\n function kw(type) {\n return { type: type, style: \"macro\"};\n }\n return {\n \"allTags\": kw('allTags'), \"closeAll\": kw('closeAll'), \"list\": kw('list'),\n \"newJournal\": kw('newJournal'), \"newTiddler\": kw('newTiddler'),\n \"permaview\": kw('permaview'), \"saveChanges\": kw('saveChanges'),\n \"search\": kw('search'), \"slider\": kw('slider'), \"tabs\": kw('tabs'),\n \"tag\": kw('tag'), \"tagging\": kw('tagging'), \"tags\": kw('tags'),\n \"tiddler\": kw('tiddler'), \"timeline\": kw('timeline'),\n \"today\": kw('today'), \"version\": kw('version'), \"option\": kw('option'),\n\n \"with\": kw('with'),\n \"filter\": kw('filter')\n };\n }();\n\n var isSpaceName = /[\\w_\\-]/i,\n reHR = /^\\-\\-\\-\\-+$/, // <hr>\n reWikiCommentStart = /^\\/\\*\\*\\*$/, // /***\n reWikiCommentStop = /^\\*\\*\\*\\/$/, // ***/\n reBlockQuote = /^<<<$/,\n\n reJsCodeStart = /^\\/\\/\\{\\{\\{$/, // //{{{ js block start\n reJsCodeStop = /^\\/\\/\\}\\}\\}$/, // //}}} js stop\n reXmlCodeStart = /^<!--\\{\\{\\{-->$/, // xml block start\n reXmlCodeStop = /^<!--\\}\\}\\}-->$/, // xml stop\n\n reCodeBlockStart = /^\\{\\{\\{$/, // {{{ TW text div block start\n reCodeBlockStop = /^\\}\\}\\}$/, // }}} TW text stop\n\n reUntilCodeStop = /.*?\\}\\}\\}/;\n\n function chain(stream, state, f) {\n state.tokenize = f;\n return f(stream, state);\n }\n\n function jsTokenBase(stream, state) {\n var sol = stream.sol(), ch;\n\n state.block = false; // indicates the start of a code block.\n\n ch = stream.peek(); // don't eat, to make matching simpler\n\n // check start of blocks\n if (sol && /[<\\/\\*{}\\-]/.test(ch)) {\n if (stream.match(reCodeBlockStart)) {\n state.block = true;\n return chain(stream, state, twTokenCode);\n }\n if (stream.match(reBlockQuote)) {\n return 'quote';\n }\n if (stream.match(reWikiCommentStart) || stream.match(reWikiCommentStop)) {\n return 'comment';\n }\n if (stream.match(reJsCodeStart) || stream.match(reJsCodeStop) || stream.match(reXmlCodeStart) || stream.match(reXmlCodeStop)) {\n return 'comment';\n }\n if (stream.match(reHR)) {\n return 'hr';\n }\n } // sol\n ch = stream.next();\n\n if (sol && /[\\/\\*!#;:>|]/.test(ch)) {\n if (ch == \"!\") { // tw header\n stream.skipToEnd();\n return \"header\";\n }\n if (ch == \"*\") { // tw list\n stream.eatWhile('*');\n return \"comment\";\n }\n if (ch == \"#\") { // tw numbered list\n stream.eatWhile('#');\n return \"comment\";\n }\n if (ch == \";\") { // definition list, term\n stream.eatWhile(';');\n return \"comment\";\n }\n if (ch == \":\") { // definition list, description\n stream.eatWhile(':');\n return \"comment\";\n }\n if (ch == \">\") { // single line quote\n stream.eatWhile(\">\");\n return \"quote\";\n }\n if (ch == '|') {\n return 'header';\n }\n }\n\n if (ch == '{' && stream.match(/\\{\\{/)) {\n return chain(stream, state, twTokenCode);\n }\n\n // rudimentary html:// file:// link matching. TW knows much more ...\n if (/[hf]/i.test(ch)) {\n if (/[ti]/i.test(stream.peek()) && stream.match(/\\b(ttps?|tp|ile):\\/\\/[\\-A-Z0-9+&@#\\/%?=~_|$!:,.;]*[A-Z0-9+&@#\\/%=~_|$]/i)) {\n return \"link\";\n }\n }\n // just a little string indicator, don't want to have the whole string covered\n if (ch == '\"') {\n return 'string';\n }\n if (ch == '~') { // _no_ CamelCase indicator should be bold\n return 'brace';\n }\n if (/[\\[\\]]/.test(ch)) { // check for [[..]]\n if (stream.peek() == ch) {\n stream.next();\n return 'brace';\n }\n }\n if (ch == \"@\") { // check for space link. TODO fix @@...@@ highlighting\n stream.eatWhile(isSpaceName);\n return \"link\";\n }\n if (/\\d/.test(ch)) { // numbers\n stream.eatWhile(/\\d/);\n return \"number\";\n }\n if (ch == \"/\") { // tw invisible comment\n if (stream.eat(\"%\")) {\n return chain(stream, state, twTokenComment);\n }\n else if (stream.eat(\"/\")) { //\n return chain(stream, state, twTokenEm);\n }\n }\n if (ch == \"_\") { // tw underline\n if (stream.eat(\"_\")) {\n return chain(stream, state, twTokenUnderline);\n }\n }\n // strikethrough and mdash handling\n if (ch == \"-\") {\n if (stream.eat(\"-\")) {\n // if strikethrough looks ugly, change CSS.\n if (stream.peek() != ' ')\n return chain(stream, state, twTokenStrike);\n // mdash\n if (stream.peek() == ' ')\n return 'brace';\n }\n }\n if (ch == \"'\") { // tw bold\n if (stream.eat(\"'\")) {\n return chain(stream, state, twTokenStrong);\n }\n }\n if (ch == \"<\") { // tw macro\n if (stream.eat(\"<\")) {\n return chain(stream, state, twTokenMacro);\n }\n }\n else {\n return null;\n }\n\n // core macro handling\n stream.eatWhile(/[\\w\\$_]/);\n var word = stream.current(),\n known = textwords.propertyIsEnumerable(word) && textwords[word];\n\n return known ? known.style : null;\n } // jsTokenBase()\n\n // tw invisible comment\n function twTokenComment(stream, state) {\n var maybeEnd = false,\n ch;\n while (ch = stream.next()) {\n if (ch == \"/\" && maybeEnd) {\n state.tokenize = jsTokenBase;\n break;\n }\n maybeEnd = (ch == \"%\");\n }\n return \"comment\";\n }\n\n // tw strong / bold\n function twTokenStrong(stream, state) {\n var maybeEnd = false,\n ch;\n while (ch = stream.next()) {\n if (ch == \"'\" && maybeEnd) {\n state.tokenize = jsTokenBase;\n break;\n }\n maybeEnd = (ch == \"'\");\n }\n return \"strong\";\n }\n\n // tw code\n function twTokenCode(stream, state) {\n var sb = state.block;\n\n if (sb && stream.current()) {\n return \"comment\";\n }\n\n if (!sb && stream.match(reUntilCodeStop)) {\n state.tokenize = jsTokenBase;\n return \"comment\";\n }\n\n if (sb && stream.sol() && stream.match(reCodeBlockStop)) {\n state.tokenize = jsTokenBase;\n return \"comment\";\n }\n\n stream.next();\n return \"comment\";\n }\n\n // tw em / italic\n function twTokenEm(stream, state) {\n var maybeEnd = false,\n ch;\n while (ch = stream.next()) {\n if (ch == \"/\" && maybeEnd) {\n state.tokenize = jsTokenBase;\n break;\n }\n maybeEnd = (ch == \"/\");\n }\n return \"em\";\n }\n\n // tw underlined text\n function twTokenUnderline(stream, state) {\n var maybeEnd = false,\n ch;\n while (ch = stream.next()) {\n if (ch == \"_\" && maybeEnd) {\n state.tokenize = jsTokenBase;\n break;\n }\n maybeEnd = (ch == \"_\");\n }\n return \"underlined\";\n }\n\n // tw strike through text looks ugly\n // change CSS if needed\n function twTokenStrike(stream, state) {\n var maybeEnd = false, ch;\n\n while (ch = stream.next()) {\n if (ch == \"-\" && maybeEnd) {\n state.tokenize = jsTokenBase;\n break;\n }\n maybeEnd = (ch == \"-\");\n }\n return \"strikethrough\";\n }\n\n // macro\n function twTokenMacro(stream, state) {\n var ch, word, known;\n\n if (stream.current() == '<<') {\n return 'macro';\n }\n\n ch = stream.next();\n if (!ch) {\n state.tokenize = jsTokenBase;\n return null;\n }\n if (ch == \">\") {\n if (stream.peek() == '>') {\n stream.next();\n state.tokenize = jsTokenBase;\n return \"macro\";\n }\n }\n\n stream.eatWhile(/[\\w\\$_]/);\n word = stream.current();\n known = keywords.propertyIsEnumerable(word) && keywords[word];\n\n if (known) {\n return known.style, word;\n }\n else {\n return null, word;\n }\n }\n\n // Interface\n return {\n startState: function () {\n return {\n tokenize: jsTokenBase,\n indented: 0,\n level: 0\n };\n },\n\n token: function (stream, state) {\n if (stream.eatSpace()) return null;\n var style = state.tokenize(stream, state);\n return style;\n },\n\n electricChars: \"\"\n };\n});\n\nCodeMirror.defineMIME(\"text/x-tiddlywiki\", \"tiddlywiki\");\n});\n\n//}}}\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/tiddlywiki/tiddlywiki.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n\"use strict\";\n\nvar htmlConfig = {\n autoSelfClosers: {'area': true, 'base': true, 'br': true, 'col': true, 'command': true,\n 'embed': true, 'frame': true, 'hr': true, 'img': true, 'input': true,\n 'keygen': true, 'link': true, 'meta': true, 'param': true, 'source': true,\n 'track': true, 'wbr': true, 'menuitem': true},\n implicitlyClosed: {'dd': true, 'li': true, 'optgroup': true, 'option': true, 'p': true,\n 'rp': true, 'rt': true, 'tbody': true, 'td': true, 'tfoot': true,\n 'th': true, 'tr': true},\n contextGrabbers: {\n 'dd': {'dd': true, 'dt': true},\n 'dt': {'dd': true, 'dt': true},\n 'li': {'li': true},\n 'option': {'option': true, 'optgroup': true},\n 'optgroup': {'optgroup': true},\n 'p': {'address': true, 'article': true, 'aside': true, 'blockquote': true, 'dir': true,\n 'div': true, 'dl': true, 'fieldset': true, 'footer': true, 'form': true,\n 'h1': true, 'h2': true, 'h3': true, 'h4': true, 'h5': true, 'h6': true,\n 'header': true, 'hgroup': true, 'hr': true, 'menu': true, 'nav': true, 'ol': true,\n 'p': true, 'pre': true, 'section': true, 'table': true, 'ul': true},\n 'rp': {'rp': true, 'rt': true},\n 'rt': {'rp': true, 'rt': true},\n 'tbody': {'tbody': true, 'tfoot': true},\n 'td': {'td': true, 'th': true},\n 'tfoot': {'tbody': true},\n 'th': {'td': true, 'th': true},\n 'thead': {'tbody': true, 'tfoot': true},\n 'tr': {'tr': true}\n },\n doNotIndent: {\"pre\": true},\n allowUnquoted: true,\n allowMissing: true,\n caseFold: true\n}\n\nvar xmlConfig = {\n autoSelfClosers: {},\n implicitlyClosed: {},\n contextGrabbers: {},\n doNotIndent: {},\n allowUnquoted: false,\n allowMissing: false,\n caseFold: false\n}\n\nCodeMirror.defineMode(\"xml\", function(editorConf, config_) {\n var indentUnit = editorConf.indentUnit\n var config = {}\n var defaults = config_.htmlMode ? htmlConfig : xmlConfig\n for (var prop in defaults) config[prop] = defaults[prop]\n for (var prop in config_) config[prop] = config_[prop]\n\n // Return variables for tokenizers\n var type, setStyle;\n\n function inText(stream, state) {\n function chain(parser) {\n state.tokenize = parser;\n return parser(stream, state);\n }\n\n var ch = stream.next();\n if (ch == \"<\") {\n if (stream.eat(\"!\")) {\n if (stream.eat(\"[\")) {\n if (stream.match(\"CDATA[\")) return chain(inBlock(\"atom\", \"]]>\"));\n else return null;\n } else if (stream.match(\"--\")) {\n return chain(inBlock(\"comment\", \"-->\"));\n } else if (stream.match(\"DOCTYPE\", true, true)) {\n stream.eatWhile(/[\\w\\._\\-]/);\n return chain(doctype(1));\n } else {\n return null;\n }\n } else if (stream.eat(\"?\")) {\n stream.eatWhile(/[\\w\\._\\-]/);\n state.tokenize = inBlock(\"meta\", \"?>\");\n return \"meta\";\n } else {\n type = stream.eat(\"/\") ? \"closeTag\" : \"openTag\";\n state.tokenize = inTag;\n return \"tag bracket\";\n }\n } else if (ch == \"&\") {\n var ok;\n if (stream.eat(\"#\")) {\n if (stream.eat(\"x\")) {\n ok = stream.eatWhile(/[a-fA-F\\d]/) && stream.eat(\";\");\n } else {\n ok = stream.eatWhile(/[\\d]/) && stream.eat(\";\");\n }\n } else {\n ok = stream.eatWhile(/[\\w\\.\\-:]/) && stream.eat(\";\");\n }\n return ok ? \"atom\" : \"error\";\n } else {\n stream.eatWhile(/[^&<]/);\n return null;\n }\n }\n inText.isInText = true;\n\n function inTag(stream, state) {\n var ch = stream.next();\n if (ch == \">\" || (ch == \"/\" && stream.eat(\">\"))) {\n state.tokenize = inText;\n type = ch == \">\" ? \"endTag\" : \"selfcloseTag\";\n return \"tag bracket\";\n } else if (ch == \"=\") {\n type = \"equals\";\n return null;\n } else if (ch == \"<\") {\n state.tokenize = inText;\n state.state = baseState;\n state.tagName = state.tagStart = null;\n var next = state.tokenize(stream, state);\n return next ? next + \" tag error\" : \"tag error\";\n } else if (/[\\'\\\"]/.test(ch)) {\n state.tokenize = inAttribute(ch);\n state.stringStartCol = stream.column();\n return state.tokenize(stream, state);\n } else {\n stream.match(/^[^\\s\\u00a0=<>\\\"\\']*[^\\s\\u00a0=<>\\\"\\'\\/]/);\n return \"word\";\n }\n }\n\n function inAttribute(quote) {\n var closure = function(stream, state) {\n while (!stream.eol()) {\n if (stream.next() == quote) {\n state.tokenize = inTag;\n break;\n }\n }\n return \"string\";\n };\n closure.isInAttribute = true;\n return closure;\n }\n\n function inBlock(style, terminator) {\n return function(stream, state) {\n while (!stream.eol()) {\n if (stream.match(terminator)) {\n state.tokenize = inText;\n break;\n }\n stream.next();\n }\n return style;\n };\n }\n function doctype(depth) {\n return function(stream, state) {\n var ch;\n while ((ch = stream.next()) != null) {\n if (ch == \"<\") {\n state.tokenize = doctype(depth + 1);\n return state.tokenize(stream, state);\n } else if (ch == \">\") {\n if (depth == 1) {\n state.tokenize = inText;\n break;\n } else {\n state.tokenize = doctype(depth - 1);\n return state.tokenize(stream, state);\n }\n }\n }\n return \"meta\";\n };\n }\n\n function Context(state, tagName, startOfLine) {\n this.prev = state.context;\n this.tagName = tagName;\n this.indent = state.indented;\n this.startOfLine = startOfLine;\n if (config.doNotIndent.hasOwnProperty(tagName) || (state.context && state.context.noIndent))\n this.noIndent = true;\n }\n function popContext(state) {\n if (state.context) state.context = state.context.prev;\n }\n function maybePopContext(state, nextTagName) {\n var parentTagName;\n while (true) {\n if (!state.context) {\n return;\n }\n parentTagName = state.context.tagName;\n if (!config.contextGrabbers.hasOwnProperty(parentTagName) ||\n !config.contextGrabbers[parentTagName].hasOwnProperty(nextTagName)) {\n return;\n }\n popContext(state);\n }\n }\n\n function baseState(type, stream, state) {\n if (type == \"openTag\") {\n state.tagStart = stream.column();\n return tagNameState;\n } else if (type == \"closeTag\") {\n return closeTagNameState;\n } else {\n return baseState;\n }\n }\n function tagNameState(type, stream, state) {\n if (type == \"word\") {\n state.tagName = stream.current();\n setStyle = \"tag\";\n return attrState;\n } else {\n setStyle = \"error\";\n return tagNameState;\n }\n }\n function closeTagNameState(type, stream, state) {\n if (type == \"word\") {\n var tagName = stream.current();\n if (state.context && state.context.tagName != tagName &&\n config.implicitlyClosed.hasOwnProperty(state.context.tagName))\n popContext(state);\n if ((state.context && state.context.tagName == tagName) || config.matchClosing === false) {\n setStyle = \"tag\";\n return closeState;\n } else {\n setStyle = \"tag error\";\n return closeStateErr;\n }\n } else {\n setStyle = \"error\";\n return closeStateErr;\n }\n }\n\n function closeState(type, _stream, state) {\n if (type != \"endTag\") {\n setStyle = \"error\";\n return closeState;\n }\n popContext(state);\n return baseState;\n }\n function closeStateErr(type, stream, state) {\n setStyle = \"error\";\n return closeState(type, stream, state);\n }\n\n function attrState(type, _stream, state) {\n if (type == \"word\") {\n setStyle = \"attribute\";\n return attrEqState;\n } else if (type == \"endTag\" || type == \"selfcloseTag\") {\n var tagName = state.tagName, tagStart = state.tagStart;\n state.tagName = state.tagStart = null;\n if (type == \"selfcloseTag\" ||\n config.autoSelfClosers.hasOwnProperty(tagName)) {\n maybePopContext(state, tagName);\n } else {\n maybePopContext(state, tagName);\n state.context = new Context(state, tagName, tagStart == state.indented);\n }\n return baseState;\n }\n setStyle = \"error\";\n return attrState;\n }\n function attrEqState(type, stream, state) {\n if (type == \"equals\") return attrValueState;\n if (!config.allowMissing) setStyle = \"error\";\n return attrState(type, stream, state);\n }\n function attrValueState(type, stream, state) {\n if (type == \"string\") return attrContinuedState;\n if (type == \"word\" && config.allowUnquoted) {setStyle = \"string\"; return attrState;}\n setStyle = \"error\";\n return attrState(type, stream, state);\n }\n function attrContinuedState(type, stream, state) {\n if (type == \"string\") return attrContinuedState;\n return attrState(type, stream, state);\n }\n\n return {\n startState: function(baseIndent) {\n var state = {tokenize: inText,\n state: baseState,\n indented: baseIndent || 0,\n tagName: null, tagStart: null,\n context: null}\n if (baseIndent != null) state.baseIndent = baseIndent\n return state\n },\n\n token: function(stream, state) {\n if (!state.tagName && stream.sol())\n state.indented = stream.indentation();\n\n if (stream.eatSpace()) return null;\n type = null;\n var style = state.tokenize(stream, state);\n if ((style || type) && style != \"comment\") {\n setStyle = null;\n state.state = state.state(type || style, stream, state);\n if (setStyle)\n style = setStyle == \"error\" ? style + \" error\" : setStyle;\n }\n return style;\n },\n\n indent: function(state, textAfter, fullLine) {\n var context = state.context;\n // Indent multi-line strings (e.g. css).\n if (state.tokenize.isInAttribute) {\n if (state.tagStart == state.indented)\n return state.stringStartCol + 1;\n else\n return state.indented + indentUnit;\n }\n if (context && context.noIndent) return CodeMirror.Pass;\n if (state.tokenize != inTag && state.tokenize != inText)\n return fullLine ? fullLine.match(/^(\\s*)/)[0].length : 0;\n // Indent the starts of attribute names.\n if (state.tagName) {\n if (config.multilineTagIndentPastTag !== false)\n return state.tagStart + state.tagName.length + 2;\n else\n return state.tagStart + indentUnit * (config.multilineTagIndentFactor || 1);\n }\n if (config.alignCDATA && /<!\\[CDATA\\[/.test(textAfter)) return 0;\n var tagAfter = textAfter && /^<(\\/)?([\\w_:\\.-]*)/.exec(textAfter);\n if (tagAfter && tagAfter[1]) { // Closing tag spotted\n while (context) {\n if (context.tagName == tagAfter[2]) {\n context = context.prev;\n break;\n } else if (config.implicitlyClosed.hasOwnProperty(context.tagName)) {\n context = context.prev;\n } else {\n break;\n }\n }\n } else if (tagAfter) { // Opening tag spotted\n while (context) {\n var grabbers = config.contextGrabbers[context.tagName];\n if (grabbers && grabbers.hasOwnProperty(tagAfter[2]))\n context = context.prev;\n else\n break;\n }\n }\n while (context && context.prev && !context.startOfLine)\n context = context.prev;\n if (context) return context.indent + indentUnit;\n else return state.baseIndent || 0;\n },\n\n electricInput: /<\\/[\\s\\w:]+>$/,\n blockCommentStart: \"<!--\",\n blockCommentEnd: \"-->\",\n\n configuration: config.htmlMode ? \"html\" : \"xml\",\n helperType: config.htmlMode ? \"html\" : \"xml\",\n\n skipAttribute: function(state) {\n if (state.state == attrValueState)\n state.state = attrState\n }\n };\n});\n\nCodeMirror.defineMIME(\"text/xml\", \"xml\");\nCodeMirror.defineMIME(\"application/xml\", \"xml\");\nif (!CodeMirror.mimeModes.hasOwnProperty(\"text/html\"))\n CodeMirror.defineMIME(\"text/html\", {name: \"xml\", htmlMode: true});\n\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/keymap/vim.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n/**\n * Supported keybindings:\n * Too many to list. Refer to defaultKeyMap below.\n *\n * Supported Ex commands:\n * Refer to defaultExCommandMap below.\n *\n * Registers: unnamed, -, a-z, A-Z, 0-9\n * (Does not respect the special case for number registers when delete\n * operator is made with these commands: %, (, ), , /, ?, n, N, {, } )\n * TODO: Implement the remaining registers.\n *\n * Marks: a-z, A-Z, and 0-9\n * TODO: Implement the remaining special marks. They have more complex\n * behavior.\n *\n * Events:\n * 'vim-mode-change' - raised on the editor anytime the current mode changes,\n * Event object: {mode: \"visual\", subMode: \"linewise\"}\n *\n * Code structure:\n * 1. Default keymap\n * 2. Variable declarations and short basic helpers\n * 3. Instance (External API) implementation\n * 4. Internal state tracking objects (input state, counter) implementation\n * and instanstiation\n * 5. Key handler (the main command dispatcher) implementation\n * 6. Motion, operator, and action implementations\n * 7. Helper functions for the key handler, motions, operators, and actions\n * 8. Set up Vim to work as a keymap for CodeMirror.\n * 9. Ex command implementations.\n */\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../lib/codemirror\"), require(\"../addon/search/searchcursor\"), require(\"../addon/dialog/dialog\"), require(\"../addon/edit/matchbrackets.js\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../lib/codemirror\", \"../addon/search/searchcursor\", \"../addon/dialog/dialog\", \"../addon/edit/matchbrackets\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n 'use strict';\n\n var defaultKeymap = [\n // Key to key mapping. This goes first to make it possible to override\n // existing mappings.\n { keys: '<Left>', type: 'keyToKey', toKeys: 'h' },\n { keys: '<Right>', type: 'keyToKey', toKeys: 'l' },\n { keys: '<Up>', type: 'keyToKey', toKeys: 'k' },\n { keys: '<Down>', type: 'keyToKey', toKeys: 'j' },\n { keys: '<Space>', type: 'keyToKey', toKeys: 'l' },\n { keys: '<BS>', type: 'keyToKey', toKeys: 'h', context: 'normal'},\n { keys: '<C-Space>', type: 'keyToKey', toKeys: 'W' },\n { keys: '<C-BS>', type: 'keyToKey', toKeys: 'B', context: 'normal' },\n { keys: '<S-Space>', type: 'keyToKey', toKeys: 'w' },\n { keys: '<S-BS>', type: 'keyToKey', toKeys: 'b', context: 'normal' },\n { keys: '<C-n>', type: 'keyToKey', toKeys: 'j' },\n { keys: '<C-p>', type: 'keyToKey', toKeys: 'k' },\n { keys: '<C-[>', type: 'keyToKey', toKeys: '<Esc>' },\n { keys: '<C-c>', type: 'keyToKey', toKeys: '<Esc>' },\n { keys: '<C-[>', type: 'keyToKey', toKeys: '<Esc>', context: 'insert' },\n { keys: '<C-c>', type: 'keyToKey', toKeys: '<Esc>', context: 'insert' },\n { keys: 's', type: 'keyToKey', toKeys: 'cl', context: 'normal' },\n { keys: 's', type: 'keyToKey', toKeys: 'c', context: 'visual'},\n { keys: 'S', type: 'keyToKey', toKeys: 'cc', context: 'normal' },\n { keys: 'S', type: 'keyToKey', toKeys: 'VdO', context: 'visual' },\n { keys: '<Home>', type: 'keyToKey', toKeys: '0' },\n { keys: '<End>', type: 'keyToKey', toKeys: '$' },\n { keys: '<PageUp>', type: 'keyToKey', toKeys: '<C-b>' },\n { keys: '<PageDown>', type: 'keyToKey', toKeys: '<C-f>' },\n { keys: '<CR>', type: 'keyToKey', toKeys: 'j^', context: 'normal' },\n // Motions\n { keys: 'H', type: 'motion', motion: 'moveToTopLine', motionArgs: { linewise: true, toJumplist: true }},\n { keys: 'M', type: 'motion', motion: 'moveToMiddleLine', motionArgs: { linewise: true, toJumplist: true }},\n { keys: 'L', type: 'motion', motion: 'moveToBottomLine', motionArgs: { linewise: true, toJumplist: true }},\n { keys: 'h', type: 'motion', motion: 'moveByCharacters', motionArgs: { forward: false }},\n { keys: 'l', type: 'motion', motion: 'moveByCharacters', motionArgs: { forward: true }},\n { keys: 'j', type: 'motion', motion: 'moveByLines', motionArgs: { forward: true, linewise: true }},\n { keys: 'k', type: 'motion', motion: 'moveByLines', motionArgs: { forward: false, linewise: true }},\n { keys: 'gj', type: 'motion', motion: 'moveByDisplayLines', motionArgs: { forward: true }},\n { keys: 'gk', type: 'motion', motion: 'moveByDisplayLines', motionArgs: { forward: false }},\n { keys: 'w', type: 'motion', motion: 'moveByWords', motionArgs: { forward: true, wordEnd: false }},\n { keys: 'W', type: 'motion', motion: 'moveByWords', motionArgs: { forward: true, wordEnd: false, bigWord: true }},\n { keys: 'e', type: 'motion', motion: 'moveByWords', motionArgs: { forward: true, wordEnd: true, inclusive: true }},\n { keys: 'E', type: 'motion', motion: 'moveByWords', motionArgs: { forward: true, wordEnd: true, bigWord: true, inclusive: true }},\n { keys: 'b', type: 'motion', motion: 'moveByWords', motionArgs: { forward: false, wordEnd: false }},\n { keys: 'B', type: 'motion', motion: 'moveByWords', motionArgs: { forward: false, wordEnd: false, bigWord: true }},\n { keys: 'ge', type: 'motion', motion: 'moveByWords', motionArgs: { forward: false, wordEnd: true, inclusive: true }},\n { keys: 'gE', type: 'motion', motion: 'moveByWords', motionArgs: { forward: false, wordEnd: true, bigWord: true, inclusive: true }},\n { keys: '{', type: 'motion', motion: 'moveByParagraph', motionArgs: { forward: false, toJumplist: true }},\n { keys: '}', type: 'motion', motion: 'moveByParagraph', motionArgs: { forward: true, toJumplist: true }},\n { keys: '<C-f>', type: 'motion', motion: 'moveByPage', motionArgs: { forward: true }},\n { keys: '<C-b>', type: 'motion', motion: 'moveByPage', motionArgs: { forward: false }},\n { keys: '<C-d>', type: 'motion', motion: 'moveByScroll', motionArgs: { forward: true, explicitRepeat: true }},\n { keys: '<C-u>', type: 'motion', motion: 'moveByScroll', motionArgs: { forward: false, explicitRepeat: true }},\n { keys: 'gg', type: 'motion', motion: 'moveToLineOrEdgeOfDocument', motionArgs: { forward: false, explicitRepeat: true, linewise: true, toJumplist: true }},\n { keys: 'G', type: 'motion', motion: 'moveToLineOrEdgeOfDocument', motionArgs: { forward: true, explicitRepeat: true, linewise: true, toJumplist: true }},\n { keys: '0', type: 'motion', motion: 'moveToStartOfLine' },\n { keys: '^', type: 'motion', motion: 'moveToFirstNonWhiteSpaceCharacter' },\n { keys: '+', type: 'motion', motion: 'moveByLines', motionArgs: { forward: true, toFirstChar:true }},\n { keys: '-', type: 'motion', motion: 'moveByLines', motionArgs: { forward: false, toFirstChar:true }},\n { keys: '_', type: 'motion', motion: 'moveByLines', motionArgs: { forward: true, toFirstChar:true, repeatOffset:-1 }},\n { keys: '$', type: 'motion', motion: 'moveToEol', motionArgs: { inclusive: true }},\n { keys: '%', type: 'motion', motion: 'moveToMatchedSymbol', motionArgs: { inclusive: true, toJumplist: true }},\n { keys: 'f<character>', type: 'motion', motion: 'moveToCharacter', motionArgs: { forward: true , inclusive: true }},\n { keys: 'F<character>', type: 'motion', motion: 'moveToCharacter', motionArgs: { forward: false }},\n { keys: 't<character>', type: 'motion', motion: 'moveTillCharacter', motionArgs: { forward: true, inclusive: true }},\n { keys: 'T<character>', type: 'motion', motion: 'moveTillCharacter', motionArgs: { forward: false }},\n { keys: ';', type: 'motion', motion: 'repeatLastCharacterSearch', motionArgs: { forward: true }},\n { keys: ',', type: 'motion', motion: 'repeatLastCharacterSearch', motionArgs: { forward: false }},\n { keys: '\\'<character>', type: 'motion', motion: 'goToMark', motionArgs: {toJumplist: true, linewise: true}},\n { keys: '`<character>', type: 'motion', motion: 'goToMark', motionArgs: {toJumplist: true}},\n { keys: ']`', type: 'motion', motion: 'jumpToMark', motionArgs: { forward: true } },\n { keys: '[`', type: 'motion', motion: 'jumpToMark', motionArgs: { forward: false } },\n { keys: ']\\'', type: 'motion', motion: 'jumpToMark', motionArgs: { forward: true, linewise: true } },\n { keys: '[\\'', type: 'motion', motion: 'jumpToMark', motionArgs: { forward: false, linewise: true } },\n // the next two aren't motions but must come before more general motion declarations\n { keys: ']p', type: 'action', action: 'paste', isEdit: true, actionArgs: { after: true, isEdit: true, matchIndent: true}},\n { keys: '[p', type: 'action', action: 'paste', isEdit: true, actionArgs: { after: false, isEdit: true, matchIndent: true}},\n { keys: ']<character>', type: 'motion', motion: 'moveToSymbol', motionArgs: { forward: true, toJumplist: true}},\n { keys: '[<character>', type: 'motion', motion: 'moveToSymbol', motionArgs: { forward: false, toJumplist: true}},\n { keys: '|', type: 'motion', motion: 'moveToColumn'},\n { keys: 'o', type: 'motion', motion: 'moveToOtherHighlightedEnd', context:'visual'},\n { keys: 'O', type: 'motion', motion: 'moveToOtherHighlightedEnd', motionArgs: {sameLine: true}, context:'visual'},\n // Operators\n { keys: 'd', type: 'operator', operator: 'delete' },\n { keys: 'y', type: 'operator', operator: 'yank' },\n { keys: 'c', type: 'operator', operator: 'change' },\n { keys: '>', type: 'operator', operator: 'indent', operatorArgs: { indentRight: true }},\n { keys: '<', type: 'operator', operator: 'indent', operatorArgs: { indentRight: false }},\n { keys: 'g~', type: 'operator', operator: 'changeCase' },\n { keys: 'gu', type: 'operator', operator: 'changeCase', operatorArgs: {toLower: true}, isEdit: true },\n { keys: 'gU', type: 'operator', operator: 'changeCase', operatorArgs: {toLower: false}, isEdit: true },\n { keys: 'n', type: 'motion', motion: 'findNext', motionArgs: { forward: true, toJumplist: true }},\n { keys: 'N', type: 'motion', motion: 'findNext', motionArgs: { forward: false, toJumplist: true }},\n // Operator-Motion dual commands\n { keys: 'x', type: 'operatorMotion', operator: 'delete', motion: 'moveByCharacters', motionArgs: { forward: true }, operatorMotionArgs: { visualLine: false }},\n { keys: 'X', type: 'operatorMotion', operator: 'delete', motion: 'moveByCharacters', motionArgs: { forward: false }, operatorMotionArgs: { visualLine: true }},\n { keys: 'D', type: 'operatorMotion', operator: 'delete', motion: 'moveToEol', motionArgs: { inclusive: true }, context: 'normal'},\n { keys: 'D', type: 'operator', operator: 'delete', operatorArgs: { linewise: true }, context: 'visual'},\n { keys: 'Y', type: 'operatorMotion', operator: 'yank', motion: 'moveToEol', motionArgs: { inclusive: true }, context: 'normal'},\n { keys: 'Y', type: 'operator', operator: 'yank', operatorArgs: { linewise: true }, context: 'visual'},\n { keys: 'C', type: 'operatorMotion', operator: 'change', motion: 'moveToEol', motionArgs: { inclusive: true }, context: 'normal'},\n { keys: 'C', type: 'operator', operator: 'change', operatorArgs: { linewise: true }, context: 'visual'},\n { keys: '~', type: 'operatorMotion', operator: 'changeCase', motion: 'moveByCharacters', motionArgs: { forward: true }, operatorArgs: { shouldMoveCursor: true }, context: 'normal'},\n { keys: '~', type: 'operator', operator: 'changeCase', context: 'visual'},\n { keys: '<C-w>', type: 'operatorMotion', operator: 'delete', motion: 'moveByWords', motionArgs: { forward: false, wordEnd: false }, context: 'insert' },\n // Actions\n { keys: '<C-i>', type: 'action', action: 'jumpListWalk', actionArgs: { forward: true }},\n { keys: '<C-o>', type: 'action', action: 'jumpListWalk', actionArgs: { forward: false }},\n { keys: '<C-e>', type: 'action', action: 'scroll', actionArgs: { forward: true, linewise: true }},\n { keys: '<C-y>', type: 'action', action: 'scroll', actionArgs: { forward: false, linewise: true }},\n { keys: 'a', type: 'action', action: 'enterInsertMode', isEdit: true, actionArgs: { insertAt: 'charAfter' }, context: 'normal' },\n { keys: 'A', type: 'action', action: 'enterInsertMode', isEdit: true, actionArgs: { insertAt: 'eol' }, context: 'normal' },\n { keys: 'A', type: 'action', action: 'enterInsertMode', isEdit: true, actionArgs: { insertAt: 'endOfSelectedArea' }, context: 'visual' },\n { keys: 'i', type: 'action', action: 'enterInsertMode', isEdit: true, actionArgs: { insertAt: 'inplace' }, context: 'normal' },\n { keys: 'I', type: 'action', action: 'enterInsertMode', isEdit: true, actionArgs: { insertAt: 'firstNonBlank'}, context: 'normal' },\n { keys: 'I', type: 'action', action: 'enterInsertMode', isEdit: true, actionArgs: { insertAt: 'startOfSelectedArea' }, context: 'visual' },\n { keys: 'o', type: 'action', action: 'newLineAndEnterInsertMode', isEdit: true, interlaceInsertRepeat: true, actionArgs: { after: true }, context: 'normal' },\n { keys: 'O', type: 'action', action: 'newLineAndEnterInsertMode', isEdit: true, interlaceInsertRepeat: true, actionArgs: { after: false }, context: 'normal' },\n { keys: 'v', type: 'action', action: 'toggleVisualMode' },\n { keys: 'V', type: 'action', action: 'toggleVisualMode', actionArgs: { linewise: true }},\n { keys: '<C-v>', type: 'action', action: 'toggleVisualMode', actionArgs: { blockwise: true }},\n { keys: '<C-q>', type: 'action', action: 'toggleVisualMode', actionArgs: { blockwise: true }},\n { keys: 'gv', type: 'action', action: 'reselectLastSelection' },\n { keys: 'J', type: 'action', action: 'joinLines', isEdit: true },\n { keys: 'p', type: 'action', action: 'paste', isEdit: true, actionArgs: { after: true, isEdit: true }},\n { keys: 'P', type: 'action', action: 'paste', isEdit: true, actionArgs: { after: false, isEdit: true }},\n { keys: 'r<character>', type: 'action', action: 'replace', isEdit: true },\n { keys: '@<character>', type: 'action', action: 'replayMacro' },\n { keys: 'q<character>', type: 'action', action: 'enterMacroRecordMode' },\n // Handle Replace-mode as a special case of insert mode.\n { keys: 'R', type: 'action', action: 'enterInsertMode', isEdit: true, actionArgs: { replace: true }},\n { keys: 'u', type: 'action', action: 'undo', context: 'normal' },\n { keys: 'u', type: 'operator', operator: 'changeCase', operatorArgs: {toLower: true}, context: 'visual', isEdit: true },\n { keys: 'U', type: 'operator', operator: 'changeCase', operatorArgs: {toLower: false}, context: 'visual', isEdit: true },\n { keys: '<C-r>', type: 'action', action: 'redo' },\n { keys: 'm<character>', type: 'action', action: 'setMark' },\n { keys: '\"<character>', type: 'action', action: 'setRegister' },\n { keys: 'zz', type: 'action', action: 'scrollToCursor', actionArgs: { position: 'center' }},\n { keys: 'z.', type: 'action', action: 'scrollToCursor', actionArgs: { position: 'center' }, motion: 'moveToFirstNonWhiteSpaceCharacter' },\n { keys: 'zt', type: 'action', action: 'scrollToCursor', actionArgs: { position: 'top' }},\n { keys: 'z<CR>', type: 'action', action: 'scrollToCursor', actionArgs: { position: 'top' }, motion: 'moveToFirstNonWhiteSpaceCharacter' },\n { keys: 'z-', type: 'action', action: 'scrollToCursor', actionArgs: { position: 'bottom' }},\n { keys: 'zb', type: 'action', action: 'scrollToCursor', actionArgs: { position: 'bottom' }, motion: 'moveToFirstNonWhiteSpaceCharacter' },\n { keys: '.', type: 'action', action: 'repeatLastEdit' },\n { keys: '<C-a>', type: 'action', action: 'incrementNumberToken', isEdit: true, actionArgs: {increase: true, backtrack: false}},\n { keys: '<C-x>', type: 'action', action: 'incrementNumberToken', isEdit: true, actionArgs: {increase: false, backtrack: false}},\n // Text object motions\n { keys: 'a<character>', type: 'motion', motion: 'textObjectManipulation' },\n { keys: 'i<character>', type: 'motion', motion: 'textObjectManipulation', motionArgs: { textObjectInner: true }},\n // Search\n { keys: '/', type: 'search', searchArgs: { forward: true, querySrc: 'prompt', toJumplist: true }},\n { keys: '?', type: 'search', searchArgs: { forward: false, querySrc: 'prompt', toJumplist: true }},\n { keys: '*', type: 'search', searchArgs: { forward: true, querySrc: 'wordUnderCursor', wholeWordOnly: true, toJumplist: true }},\n { keys: '#', type: 'search', searchArgs: { forward: false, querySrc: 'wordUnderCursor', wholeWordOnly: true, toJumplist: true }},\n { keys: 'g*', type: 'search', searchArgs: { forward: true, querySrc: 'wordUnderCursor', toJumplist: true }},\n { keys: 'g#', type: 'search', searchArgs: { forward: false, querySrc: 'wordUnderCursor', toJumplist: true }},\n // Ex command\n { keys: ':', type: 'ex' }\n ];\n\n /**\n * Ex commands\n * Care must be taken when adding to the default Ex command map. For any\n * pair of commands that have a shared prefix, at least one of their\n * shortNames must not match the prefix of the other command.\n */\n var defaultExCommandMap = [\n { name: 'colorscheme', shortName: 'colo' },\n { name: 'map' },\n { name: 'imap', shortName: 'im' },\n { name: 'nmap', shortName: 'nm' },\n { name: 'vmap', shortName: 'vm' },\n { name: 'unmap' },\n { name: 'write', shortName: 'w' },\n { name: 'undo', shortName: 'u' },\n { name: 'redo', shortName: 'red' },\n { name: 'set', shortName: 'se' },\n { name: 'set', shortName: 'se' },\n { name: 'setlocal', shortName: 'setl' },\n { name: 'setglobal', shortName: 'setg' },\n { name: 'sort', shortName: 'sor' },\n { name: 'substitute', shortName: 's', possiblyAsync: true },\n { name: 'nohlsearch', shortName: 'noh' },\n { name: 'delmarks', shortName: 'delm' },\n { name: 'registers', shortName: 'reg', excludeFromCommandHistory: true },\n { name: 'global', shortName: 'g' }\n ];\n\n var Pos = CodeMirror.Pos;\n\n var Vim = function() {\n function enterVimMode(cm) {\n cm.setOption('disableInput', true);\n cm.setOption('showCursorWhenSelecting', false);\n CodeMirror.signal(cm, \"vim-mode-change\", {mode: \"normal\"});\n cm.on('cursorActivity', onCursorActivity);\n maybeInitVimState(cm);\n CodeMirror.on(cm.getInputField(), 'paste', getOnPasteFn(cm));\n }\n\n function leaveVimMode(cm) {\n cm.setOption('disableInput', false);\n cm.off('cursorActivity', onCursorActivity);\n CodeMirror.off(cm.getInputField(), 'paste', getOnPasteFn(cm));\n cm.state.vim = null;\n }\n\n function detachVimMap(cm, next) {\n if (this == CodeMirror.keyMap.vim)\n CodeMirror.rmClass(cm.getWrapperElement(), \"cm-fat-cursor\");\n\n if (!next || next.attach != attachVimMap)\n leaveVimMode(cm, false);\n }\n function attachVimMap(cm, prev) {\n if (this == CodeMirror.keyMap.vim)\n CodeMirror.addClass(cm.getWrapperElement(), \"cm-fat-cursor\");\n\n if (!prev || prev.attach != attachVimMap)\n enterVimMode(cm);\n }\n\n // Deprecated, simply setting the keymap works again.\n CodeMirror.defineOption('vimMode', false, function(cm, val, prev) {\n if (val && cm.getOption(\"keyMap\") != \"vim\")\n cm.setOption(\"keyMap\", \"vim\");\n else if (!val && prev != CodeMirror.Init && /^vim/.test(cm.getOption(\"keyMap\")))\n cm.setOption(\"keyMap\", \"default\");\n });\n\n function cmKey(key, cm) {\n if (!cm) { return undefined; }\n var vimKey = cmKeyToVimKey(key);\n if (!vimKey) {\n return false;\n }\n var cmd = CodeMirror.Vim.findKey(cm, vimKey);\n if (typeof cmd == 'function') {\n CodeMirror.signal(cm, 'vim-keypress', vimKey);\n }\n return cmd;\n }\n\n var modifiers = {'Shift': 'S', 'Ctrl': 'C', 'Alt': 'A', 'Cmd': 'D', 'Mod': 'A'};\n var specialKeys = {Enter:'CR',Backspace:'BS',Delete:'Del'};\n function cmKeyToVimKey(key) {\n if (key.charAt(0) == '\\'') {\n // Keypress character binding of format \"'a'\"\n return key.charAt(1);\n }\n var pieces = key.split(/-(?!$)/);\n var lastPiece = pieces[pieces.length - 1];\n if (pieces.length == 1 && pieces[0].length == 1) {\n // No-modifier bindings use literal character bindings above. Skip.\n return false;\n } else if (pieces.length == 2 && pieces[0] == 'Shift' && lastPiece.length == 1) {\n // Ignore Shift+char bindings as they should be handled by literal character.\n return false;\n }\n var hasCharacter = false;\n for (var i = 0; i < pieces.length; i++) {\n var piece = pieces[i];\n if (piece in modifiers) { pieces[i] = modifiers[piece]; }\n else { hasCharacter = true; }\n if (piece in specialKeys) { pieces[i] = specialKeys[piece]; }\n }\n if (!hasCharacter) {\n // Vim does not support modifier only keys.\n return false;\n }\n // TODO: Current bindings expect the character to be lower case, but\n // it looks like vim key notation uses upper case.\n if (isUpperCase(lastPiece)) {\n pieces[pieces.length - 1] = lastPiece.toLowerCase();\n }\n return '<' + pieces.join('-') + '>';\n }\n\n function getOnPasteFn(cm) {\n var vim = cm.state.vim;\n if (!vim.onPasteFn) {\n vim.onPasteFn = function() {\n if (!vim.insertMode) {\n cm.setCursor(offsetCursor(cm.getCursor(), 0, 1));\n actions.enterInsertMode(cm, {}, vim);\n }\n };\n }\n return vim.onPasteFn;\n }\n\n var numberRegex = /[\\d]/;\n var wordCharTest = [CodeMirror.isWordChar, function(ch) {\n return ch && !CodeMirror.isWordChar(ch) && !/\\s/.test(ch);\n }], bigWordCharTest = [function(ch) {\n return /\\S/.test(ch);\n }];\n function makeKeyRange(start, size) {\n var keys = [];\n for (var i = start; i < start + size; i++) {\n keys.push(String.fromCharCode(i));\n }\n return keys;\n }\n var upperCaseAlphabet = makeKeyRange(65, 26);\n var lowerCaseAlphabet = makeKeyRange(97, 26);\n var numbers = makeKeyRange(48, 10);\n var validMarks = [].concat(upperCaseAlphabet, lowerCaseAlphabet, numbers, ['<', '>']);\n var validRegisters = [].concat(upperCaseAlphabet, lowerCaseAlphabet, numbers, ['-', '\"', '.', ':', '/']);\n\n function isLine(cm, line) {\n return line >= cm.firstLine() && line <= cm.lastLine();\n }\n function isLowerCase(k) {\n return (/^[a-z]$/).test(k);\n }\n function isMatchableSymbol(k) {\n return '()[]{}'.indexOf(k) != -1;\n }\n function isNumber(k) {\n return numberRegex.test(k);\n }\n function isUpperCase(k) {\n return (/^[A-Z]$/).test(k);\n }\n function isWhiteSpaceString(k) {\n return (/^\\s*$/).test(k);\n }\n function inArray(val, arr) {\n for (var i = 0; i < arr.length; i++) {\n if (arr[i] == val) {\n return true;\n }\n }\n return false;\n }\n\n var options = {};\n function defineOption(name, defaultValue, type, aliases, callback) {\n if (defaultValue === undefined && !callback) {\n throw Error('defaultValue is required unless callback is provided');\n }\n if (!type) { type = 'string'; }\n options[name] = {\n type: type,\n defaultValue: defaultValue,\n callback: callback\n };\n if (aliases) {\n for (var i = 0; i < aliases.length; i++) {\n options[aliases[i]] = options[name];\n }\n }\n if (defaultValue) {\n setOption(name, defaultValue);\n }\n }\n\n function setOption(name, value, cm, cfg) {\n var option = options[name];\n cfg = cfg || {};\n var scope = cfg.scope;\n if (!option) {\n throw Error('Unknown option: ' + name);\n }\n if (option.type == 'boolean') {\n if (value && value !== true) {\n throw Error('Invalid argument: ' + name + '=' + value);\n } else if (value !== false) {\n // Boolean options are set to true if value is not defined.\n value = true;\n }\n }\n if (option.callback) {\n if (scope !== 'local') {\n option.callback(value, undefined);\n }\n if (scope !== 'global' && cm) {\n option.callback(value, cm);\n }\n } else {\n if (scope !== 'local') {\n option.value = option.type == 'boolean' ? !!value : value;\n }\n if (scope !== 'global' && cm) {\n cm.state.vim.options[name] = {value: value};\n }\n }\n }\n\n function getOption(name, cm, cfg) {\n var option = options[name];\n cfg = cfg || {};\n var scope = cfg.scope;\n if (!option) {\n throw Error('Unknown option: ' + name);\n }\n if (option.callback) {\n var local = cm && option.callback(undefined, cm);\n if (scope !== 'global' && local !== undefined) {\n return local;\n }\n if (scope !== 'local') {\n return option.callback();\n }\n return;\n } else {\n var local = (scope !== 'global') && (cm && cm.state.vim.options[name]);\n return (local || (scope !== 'local') && option || {}).value;\n }\n }\n\n defineOption('filetype', undefined, 'string', ['ft'], function(name, cm) {\n // Option is local. Do nothing for global.\n if (cm === undefined) {\n return;\n }\n // The 'filetype' option proxies to the CodeMirror 'mode' option.\n if (name === undefined) {\n var mode = cm.getOption('mode');\n return mode == 'null' ? '' : mode;\n } else {\n var mode = name == '' ? 'null' : name;\n cm.setOption('mode', mode);\n }\n });\n\n var createCircularJumpList = function() {\n var size = 100;\n var pointer = -1;\n var head = 0;\n var tail = 0;\n var buffer = new Array(size);\n function add(cm, oldCur, newCur) {\n var current = pointer % size;\n var curMark = buffer[current];\n function useNextSlot(cursor) {\n var next = ++pointer % size;\n var trashMark = buffer[next];\n if (trashMark) {\n trashMark.clear();\n }\n buffer[next] = cm.setBookmark(cursor);\n }\n if (curMark) {\n var markPos = curMark.find();\n // avoid recording redundant cursor position\n if (markPos && !cursorEqual(markPos, oldCur)) {\n useNextSlot(oldCur);\n }\n } else {\n useNextSlot(oldCur);\n }\n useNextSlot(newCur);\n head = pointer;\n tail = pointer - size + 1;\n if (tail < 0) {\n tail = 0;\n }\n }\n function move(cm, offset) {\n pointer += offset;\n if (pointer > head) {\n pointer = head;\n } else if (pointer < tail) {\n pointer = tail;\n }\n var mark = buffer[(size + pointer) % size];\n // skip marks that are temporarily removed from text buffer\n if (mark && !mark.find()) {\n var inc = offset > 0 ? 1 : -1;\n var newCur;\n var oldCur = cm.getCursor();\n do {\n pointer += inc;\n mark = buffer[(size + pointer) % size];\n // skip marks that are the same as current position\n if (mark &&\n (newCur = mark.find()) &&\n !cursorEqual(oldCur, newCur)) {\n break;\n }\n } while (pointer < head && pointer > tail);\n }\n return mark;\n }\n return {\n cachedCursor: undefined, //used for # and * jumps\n add: add,\n move: move\n };\n };\n\n // Returns an object to track the changes associated insert mode. It\n // clones the object that is passed in, or creates an empty object one if\n // none is provided.\n var createInsertModeChanges = function(c) {\n if (c) {\n // Copy construction\n return {\n changes: c.changes,\n expectCursorActivityForChange: c.expectCursorActivityForChange\n };\n }\n return {\n // Change list\n changes: [],\n // Set to true on change, false on cursorActivity.\n expectCursorActivityForChange: false\n };\n };\n\n function MacroModeState() {\n this.latestRegister = undefined;\n this.isPlaying = false;\n this.isRecording = false;\n this.replaySearchQueries = [];\n this.onRecordingDone = undefined;\n this.lastInsertModeChanges = createInsertModeChanges();\n }\n MacroModeState.prototype = {\n exitMacroRecordMode: function() {\n var macroModeState = vimGlobalState.macroModeState;\n if (macroModeState.onRecordingDone) {\n macroModeState.onRecordingDone(); // close dialog\n }\n macroModeState.onRecordingDone = undefined;\n macroModeState.isRecording = false;\n },\n enterMacroRecordMode: function(cm, registerName) {\n var register =\n vimGlobalState.registerController.getRegister(registerName);\n if (register) {\n register.clear();\n this.latestRegister = registerName;\n if (cm.openDialog) {\n this.onRecordingDone = cm.openDialog(\n '(recording)['+registerName+']', null, {bottom:true});\n }\n this.isRecording = true;\n }\n }\n };\n\n function maybeInitVimState(cm) {\n if (!cm.state.vim) {\n // Store instance state in the CodeMirror object.\n cm.state.vim = {\n inputState: new InputState(),\n // Vim's input state that triggered the last edit, used to repeat\n // motions and operators with '.'.\n lastEditInputState: undefined,\n // Vim's action command before the last edit, used to repeat actions\n // with '.' and insert mode repeat.\n lastEditActionCommand: undefined,\n // When using jk for navigation, if you move from a longer line to a\n // shorter line, the cursor may clip to the end of the shorter line.\n // If j is pressed again and cursor goes to the next line, the\n // cursor should go back to its horizontal position on the longer\n // line if it can. This is to keep track of the horizontal position.\n lastHPos: -1,\n // Doing the same with screen-position for gj/gk\n lastHSPos: -1,\n // The last motion command run. Cleared if a non-motion command gets\n // executed in between.\n lastMotion: null,\n marks: {},\n // Mark for rendering fake cursor for visual mode.\n fakeCursor: null,\n insertMode: false,\n // Repeat count for changes made in insert mode, triggered by key\n // sequences like 3,i. Only exists when insertMode is true.\n insertModeRepeat: undefined,\n visualMode: false,\n // If we are in visual line mode. No effect if visualMode is false.\n visualLine: false,\n visualBlock: false,\n lastSelection: null,\n lastPastedText: null,\n sel: {},\n // Buffer-local/window-local values of vim options.\n options: {}\n };\n }\n return cm.state.vim;\n }\n var vimGlobalState;\n function resetVimGlobalState() {\n vimGlobalState = {\n // The current search query.\n searchQuery: null,\n // Whether we are searching backwards.\n searchIsReversed: false,\n // Replace part of the last substituted pattern\n lastSubstituteReplacePart: undefined,\n jumpList: createCircularJumpList(),\n macroModeState: new MacroModeState,\n // Recording latest f, t, F or T motion command.\n lastChararacterSearch: {increment:0, forward:true, selectedCharacter:''},\n registerController: new RegisterController({}),\n // search history buffer\n searchHistoryController: new HistoryController({}),\n // ex Command history buffer\n exCommandHistoryController : new HistoryController({})\n };\n for (var optionName in options) {\n var option = options[optionName];\n option.value = option.defaultValue;\n }\n }\n\n var lastInsertModeKeyTimer;\n var vimApi= {\n buildKeyMap: function() {\n // TODO: Convert keymap into dictionary format for fast lookup.\n },\n // Testing hook, though it might be useful to expose the register\n // controller anyways.\n getRegisterController: function() {\n return vimGlobalState.registerController;\n },\n // Testing hook.\n resetVimGlobalState_: resetVimGlobalState,\n\n // Testing hook.\n getVimGlobalState_: function() {\n return vimGlobalState;\n },\n\n // Testing hook.\n maybeInitVimState_: maybeInitVimState,\n\n suppressErrorLogging: false,\n\n InsertModeKey: InsertModeKey,\n map: function(lhs, rhs, ctx) {\n // Add user defined key bindings.\n exCommandDispatcher.map(lhs, rhs, ctx);\n },\n unmap: function(lhs, ctx) {\n exCommandDispatcher.unmap(lhs, ctx);\n },\n // TODO: Expose setOption and getOption as instance methods. Need to decide how to namespace\n // them, or somehow make them work with the existing CodeMirror setOption/getOption API.\n setOption: setOption,\n getOption: getOption,\n defineOption: defineOption,\n defineEx: function(name, prefix, func){\n if (!prefix) {\n prefix = name;\n } else if (name.indexOf(prefix) !== 0) {\n throw new Error('(Vim.defineEx) \"'+prefix+'\" is not a prefix of \"'+name+'\", command not registered');\n }\n exCommands[name]=func;\n exCommandDispatcher.commandMap_[prefix]={name:name, shortName:prefix, type:'api'};\n },\n handleKey: function (cm, key, origin) {\n var command = this.findKey(cm, key, origin);\n if (typeof command === 'function') {\n return command();\n }\n },\n /**\n * This is the outermost function called by CodeMirror, after keys have\n * been mapped to their Vim equivalents.\n *\n * Finds a command based on the key (and cached keys if there is a\n * multi-key sequence). Returns `undefined` if no key is matched, a noop\n * function if a partial match is found (multi-key), and a function to\n * execute the bound command if a a key is matched. The function always\n * returns true.\n */\n findKey: function(cm, key, origin) {\n var vim = maybeInitVimState(cm);\n function handleMacroRecording() {\n var macroModeState = vimGlobalState.macroModeState;\n if (macroModeState.isRecording) {\n if (key == 'q') {\n macroModeState.exitMacroRecordMode();\n clearInputState(cm);\n return true;\n }\n if (origin != 'mapping') {\n logKey(macroModeState, key);\n }\n }\n }\n function handleEsc() {\n if (key == '<Esc>') {\n // Clear input state and get back to normal mode.\n clearInputState(cm);\n if (vim.visualMode) {\n exitVisualMode(cm);\n } else if (vim.insertMode) {\n exitInsertMode(cm);\n }\n return true;\n }\n }\n function doKeyToKey(keys) {\n // TODO: prevent infinite recursion.\n var match;\n while (keys) {\n // Pull off one command key, which is either a single character\n // or a special sequence wrapped in '<' and '>', e.g. '<Space>'.\n match = (/<\\w+-.+?>|<\\w+>|./).exec(keys);\n key = match[0];\n keys = keys.substring(match.index + key.length);\n CodeMirror.Vim.handleKey(cm, key, 'mapping');\n }\n }\n\n function handleKeyInsertMode() {\n if (handleEsc()) { return true; }\n var keys = vim.inputState.keyBuffer = vim.inputState.keyBuffer + key;\n var keysAreChars = key.length == 1;\n var match = commandDispatcher.matchCommand(keys, defaultKeymap, vim.inputState, 'insert');\n // Need to check all key substrings in insert mode.\n while (keys.length > 1 && match.type != 'full') {\n var keys = vim.inputState.keyBuffer = keys.slice(1);\n var thisMatch = commandDispatcher.matchCommand(keys, defaultKeymap, vim.inputState, 'insert');\n if (thisMatch.type != 'none') { match = thisMatch; }\n }\n if (match.type == 'none') { clearInputState(cm); return false; }\n else if (match.type == 'partial') {\n if (lastInsertModeKeyTimer) { window.clearTimeout(lastInsertModeKeyTimer); }\n lastInsertModeKeyTimer = window.setTimeout(\n function() { if (vim.insertMode && vim.inputState.keyBuffer) { clearInputState(cm); } },\n getOption('insertModeEscKeysTimeout'));\n return !keysAreChars;\n }\n\n if (lastInsertModeKeyTimer) { window.clearTimeout(lastInsertModeKeyTimer); }\n if (keysAreChars) {\n var here = cm.getCursor();\n cm.replaceRange('', offsetCursor(here, 0, -(keys.length - 1)), here, '+input');\n }\n clearInputState(cm);\n return match.command;\n }\n\n function handleKeyNonInsertMode() {\n if (handleMacroRecording() || handleEsc()) { return true; };\n\n var keys = vim.inputState.keyBuffer = vim.inputState.keyBuffer + key;\n if (/^[1-9]\\d*$/.test(keys)) { return true; }\n\n var keysMatcher = /^(\\d*)(.*)$/.exec(keys);\n if (!keysMatcher) { clearInputState(cm); return false; }\n var context = vim.visualMode ? 'visual' :\n 'normal';\n var match = commandDispatcher.matchCommand(keysMatcher[2] || keysMatcher[1], defaultKeymap, vim.inputState, context);\n if (match.type == 'none') { clearInputState(cm); return false; }\n else if (match.type == 'partial') { return true; }\n\n vim.inputState.keyBuffer = '';\n var keysMatcher = /^(\\d*)(.*)$/.exec(keys);\n if (keysMatcher[1] && keysMatcher[1] != '0') {\n vim.inputState.pushRepeatDigit(keysMatcher[1]);\n }\n return match.command;\n }\n\n var command;\n if (vim.insertMode) { command = handleKeyInsertMode(); }\n else { command = handleKeyNonInsertMode(); }\n if (command === false) {\n return undefined;\n } else if (command === true) {\n // TODO: Look into using CodeMirror's multi-key handling.\n // Return no-op since we are caching the key. Counts as handled, but\n // don't want act on it just yet.\n return function() {};\n } else {\n return function() {\n return cm.operation(function() {\n cm.curOp.isVimOp = true;\n try {\n if (command.type == 'keyToKey') {\n doKeyToKey(command.toKeys);\n } else {\n commandDispatcher.processCommand(cm, vim, command);\n }\n } catch (e) {\n // clear VIM state in case it's in a bad state.\n cm.state.vim = undefined;\n maybeInitVimState(cm);\n if (!CodeMirror.Vim.suppressErrorLogging) {\n console['log'](e);\n }\n throw e;\n }\n return true;\n });\n };\n }\n },\n handleEx: function(cm, input) {\n exCommandDispatcher.processCommand(cm, input);\n },\n\n defineMotion: defineMotion,\n defineAction: defineAction,\n defineOperator: defineOperator,\n mapCommand: mapCommand,\n _mapCommand: _mapCommand,\n\n defineRegister: defineRegister,\n\n exitVisualMode: exitVisualMode,\n exitInsertMode: exitInsertMode\n };\n\n // Represents the current input state.\n function InputState() {\n this.prefixRepeat = [];\n this.motionRepeat = [];\n\n this.operator = null;\n this.operatorArgs = null;\n this.motion = null;\n this.motionArgs = null;\n this.keyBuffer = []; // For matching multi-key commands.\n this.registerName = null; // Defaults to the unnamed register.\n }\n InputState.prototype.pushRepeatDigit = function(n) {\n if (!this.operator) {\n this.prefixRepeat = this.prefixRepeat.concat(n);\n } else {\n this.motionRepeat = this.motionRepeat.concat(n);\n }\n };\n InputState.prototype.getRepeat = function() {\n var repeat = 0;\n if (this.prefixRepeat.length > 0 || this.motionRepeat.length > 0) {\n repeat = 1;\n if (this.prefixRepeat.length > 0) {\n repeat *= parseInt(this.prefixRepeat.join(''), 10);\n }\n if (this.motionRepeat.length > 0) {\n repeat *= parseInt(this.motionRepeat.join(''), 10);\n }\n }\n return repeat;\n };\n\n function clearInputState(cm, reason) {\n cm.state.vim.inputState = new InputState();\n CodeMirror.signal(cm, 'vim-command-done', reason);\n }\n\n /*\n * Register stores information about copy and paste registers. Besides\n * text, a register must store whether it is linewise (i.e., when it is\n * pasted, should it insert itself into a new line, or should the text be\n * inserted at the cursor position.)\n */\n function Register(text, linewise, blockwise) {\n this.clear();\n this.keyBuffer = [text || ''];\n this.insertModeChanges = [];\n this.searchQueries = [];\n this.linewise = !!linewise;\n this.blockwise = !!blockwise;\n }\n Register.prototype = {\n setText: function(text, linewise, blockwise) {\n this.keyBuffer = [text || ''];\n this.linewise = !!linewise;\n this.blockwise = !!blockwise;\n },\n pushText: function(text, linewise) {\n // if this register has ever been set to linewise, use linewise.\n if (linewise) {\n if (!this.linewise) {\n this.keyBuffer.push('\\n');\n }\n this.linewise = true;\n }\n this.keyBuffer.push(text);\n },\n pushInsertModeChanges: function(changes) {\n this.insertModeChanges.push(createInsertModeChanges(changes));\n },\n pushSearchQuery: function(query) {\n this.searchQueries.push(query);\n },\n clear: function() {\n this.keyBuffer = [];\n this.insertModeChanges = [];\n this.searchQueries = [];\n this.linewise = false;\n },\n toString: function() {\n return this.keyBuffer.join('');\n }\n };\n\n /**\n * Defines an external register.\n *\n * The name should be a single character that will be used to reference the register.\n * The register should support setText, pushText, clear, and toString(). See Register\n * for a reference implementation.\n */\n function defineRegister(name, register) {\n var registers = vimGlobalState.registerController.registers[name];\n if (!name || name.length != 1) {\n throw Error('Register name must be 1 character');\n }\n if (registers[name]) {\n throw Error('Register already defined ' + name);\n }\n registers[name] = register;\n validRegisters.push(name);\n }\n\n /*\n * vim registers allow you to keep many independent copy and paste buffers.\n * See http://usevim.com/2012/04/13/registers/ for an introduction.\n *\n * RegisterController keeps the state of all the registers. An initial\n * state may be passed in. The unnamed register '\"' will always be\n * overridden.\n */\n function RegisterController(registers) {\n this.registers = registers;\n this.unnamedRegister = registers['\"'] = new Register();\n registers['.'] = new Register();\n registers[':'] = new Register();\n registers['/'] = new Register();\n }\n RegisterController.prototype = {\n pushText: function(registerName, operator, text, linewise, blockwise) {\n if (linewise && text.charAt(0) == '\\n') {\n text = text.slice(1) + '\\n';\n }\n if (linewise && text.charAt(text.length - 1) !== '\\n'){\n text += '\\n';\n }\n // Lowercase and uppercase registers refer to the same register.\n // Uppercase just means append.\n var register = this.isValidRegister(registerName) ?\n this.getRegister(registerName) : null;\n // if no register/an invalid register was specified, things go to the\n // default registers\n if (!register) {\n switch (operator) {\n case 'yank':\n // The 0 register contains the text from the most recent yank.\n this.registers['0'] = new Register(text, linewise, blockwise);\n break;\n case 'delete':\n case 'change':\n if (text.indexOf('\\n') == -1) {\n // Delete less than 1 line. Update the small delete register.\n this.registers['-'] = new Register(text, linewise);\n } else {\n // Shift down the contents of the numbered registers and put the\n // deleted text into register 1.\n this.shiftNumericRegisters_();\n this.registers['1'] = new Register(text, linewise);\n }\n break;\n }\n // Make sure the unnamed register is set to what just happened\n this.unnamedRegister.setText(text, linewise, blockwise);\n return;\n }\n\n // If we've gotten to this point, we've actually specified a register\n var append = isUpperCase(registerName);\n if (append) {\n register.pushText(text, linewise);\n } else {\n register.setText(text, linewise, blockwise);\n }\n // The unnamed register always has the same value as the last used\n // register.\n this.unnamedRegister.setText(register.toString(), linewise);\n },\n // Gets the register named @name. If one of @name doesn't already exist,\n // create it. If @name is invalid, return the unnamedRegister.\n getRegister: function(name) {\n if (!this.isValidRegister(name)) {\n return this.unnamedRegister;\n }\n name = name.toLowerCase();\n if (!this.registers[name]) {\n this.registers[name] = new Register();\n }\n return this.registers[name];\n },\n isValidRegister: function(name) {\n return name && inArray(name, validRegisters);\n },\n shiftNumericRegisters_: function() {\n for (var i = 9; i >= 2; i--) {\n this.registers[i] = this.getRegister('' + (i - 1));\n }\n }\n };\n function HistoryController() {\n this.historyBuffer = [];\n this.iterator;\n this.initialPrefix = null;\n }\n HistoryController.prototype = {\n // the input argument here acts a user entered prefix for a small time\n // until we start autocompletion in which case it is the autocompleted.\n nextMatch: function (input, up) {\n var historyBuffer = this.historyBuffer;\n var dir = up ? -1 : 1;\n if (this.initialPrefix === null) this.initialPrefix = input;\n for (var i = this.iterator + dir; up ? i >= 0 : i < historyBuffer.length; i+= dir) {\n var element = historyBuffer[i];\n for (var j = 0; j <= element.length; j++) {\n if (this.initialPrefix == element.substring(0, j)) {\n this.iterator = i;\n return element;\n }\n }\n }\n // should return the user input in case we reach the end of buffer.\n if (i >= historyBuffer.length) {\n this.iterator = historyBuffer.length;\n return this.initialPrefix;\n }\n // return the last autocompleted query or exCommand as it is.\n if (i < 0 ) return input;\n },\n pushInput: function(input) {\n var index = this.historyBuffer.indexOf(input);\n if (index > -1) this.historyBuffer.splice(index, 1);\n if (input.length) this.historyBuffer.push(input);\n },\n reset: function() {\n this.initialPrefix = null;\n this.iterator = this.historyBuffer.length;\n }\n };\n var commandDispatcher = {\n matchCommand: function(keys, keyMap, inputState, context) {\n var matches = commandMatches(keys, keyMap, context, inputState);\n if (!matches.full && !matches.partial) {\n return {type: 'none'};\n } else if (!matches.full && matches.partial) {\n return {type: 'partial'};\n }\n\n var bestMatch;\n for (var i = 0; i < matches.full.length; i++) {\n var match = matches.full[i];\n if (!bestMatch) {\n bestMatch = match;\n }\n }\n if (bestMatch.keys.slice(-11) == '<character>') {\n inputState.selectedCharacter = lastChar(keys);\n }\n return {type: 'full', command: bestMatch};\n },\n processCommand: function(cm, vim, command) {\n vim.inputState.repeatOverride = command.repeatOverride;\n switch (command.type) {\n case 'motion':\n this.processMotion(cm, vim, command);\n break;\n case 'operator':\n this.processOperator(cm, vim, command);\n break;\n case 'operatorMotion':\n this.processOperatorMotion(cm, vim, command);\n break;\n case 'action':\n this.processAction(cm, vim, command);\n break;\n case 'search':\n this.processSearch(cm, vim, command);\n break;\n case 'ex':\n case 'keyToEx':\n this.processEx(cm, vim, command);\n break;\n default:\n break;\n }\n },\n processMotion: function(cm, vim, command) {\n vim.inputState.motion = command.motion;\n vim.inputState.motionArgs = copyArgs(command.motionArgs);\n this.evalInput(cm, vim);\n },\n processOperator: function(cm, vim, command) {\n var inputState = vim.inputState;\n if (inputState.operator) {\n if (inputState.operator == command.operator) {\n // Typing an operator twice like 'dd' makes the operator operate\n // linewise\n inputState.motion = 'expandToLine';\n inputState.motionArgs = { linewise: true };\n this.evalInput(cm, vim);\n return;\n } else {\n // 2 different operators in a row doesn't make sense.\n clearInputState(cm);\n }\n }\n inputState.operator = command.operator;\n inputState.operatorArgs = copyArgs(command.operatorArgs);\n if (vim.visualMode) {\n // Operating on a selection in visual mode. We don't need a motion.\n this.evalInput(cm, vim);\n }\n },\n processOperatorMotion: function(cm, vim, command) {\n var visualMode = vim.visualMode;\n var operatorMotionArgs = copyArgs(command.operatorMotionArgs);\n if (operatorMotionArgs) {\n // Operator motions may have special behavior in visual mode.\n if (visualMode && operatorMotionArgs.visualLine) {\n vim.visualLine = true;\n }\n }\n this.processOperator(cm, vim, command);\n if (!visualMode) {\n this.processMotion(cm, vim, command);\n }\n },\n processAction: function(cm, vim, command) {\n var inputState = vim.inputState;\n var repeat = inputState.getRepeat();\n var repeatIsExplicit = !!repeat;\n var actionArgs = copyArgs(command.actionArgs) || {};\n if (inputState.selectedCharacter) {\n actionArgs.selectedCharacter = inputState.selectedCharacter;\n }\n // Actions may or may not have motions and operators. Do these first.\n if (command.operator) {\n this.processOperator(cm, vim, command);\n }\n if (command.motion) {\n this.processMotion(cm, vim, command);\n }\n if (command.motion || command.operator) {\n this.evalInput(cm, vim);\n }\n actionArgs.repeat = repeat || 1;\n actionArgs.repeatIsExplicit = repeatIsExplicit;\n actionArgs.registerName = inputState.registerName;\n clearInputState(cm);\n vim.lastMotion = null;\n if (command.isEdit) {\n this.recordLastEdit(vim, inputState, command);\n }\n actions[command.action](cm, actionArgs, vim);\n },\n processSearch: function(cm, vim, command) {\n if (!cm.getSearchCursor) {\n // Search depends on SearchCursor.\n return;\n }\n var forward = command.searchArgs.forward;\n var wholeWordOnly = command.searchArgs.wholeWordOnly;\n getSearchState(cm).setReversed(!forward);\n var promptPrefix = (forward) ? '/' : '?';\n var originalQuery = getSearchState(cm).getQuery();\n var originalScrollPos = cm.getScrollInfo();\n function handleQuery(query, ignoreCase, smartCase) {\n vimGlobalState.searchHistoryController.pushInput(query);\n vimGlobalState.searchHistoryController.reset();\n try {\n updateSearchQuery(cm, query, ignoreCase, smartCase);\n } catch (e) {\n showConfirm(cm, 'Invalid regex: ' + query);\n clearInputState(cm);\n return;\n }\n commandDispatcher.processMotion(cm, vim, {\n type: 'motion',\n motion: 'findNext',\n motionArgs: { forward: true, toJumplist: command.searchArgs.toJumplist }\n });\n }\n function onPromptClose(query) {\n cm.scrollTo(originalScrollPos.left, originalScrollPos.top);\n handleQuery(query, true /** ignoreCase */, true /** smartCase */);\n var macroModeState = vimGlobalState.macroModeState;\n if (macroModeState.isRecording) {\n logSearchQuery(macroModeState, query);\n }\n }\n function onPromptKeyUp(e, query, close) {\n var keyName = CodeMirror.keyName(e), up;\n if (keyName == 'Up' || keyName == 'Down') {\n up = keyName == 'Up' ? true : false;\n query = vimGlobalState.searchHistoryController.nextMatch(query, up) || '';\n close(query);\n } else {\n if ( keyName != 'Left' && keyName != 'Right' && keyName != 'Ctrl' && keyName != 'Alt' && keyName != 'Shift')\n vimGlobalState.searchHistoryController.reset();\n }\n var parsedQuery;\n try {\n parsedQuery = updateSearchQuery(cm, query,\n true /** ignoreCase */, true /** smartCase */);\n } catch (e) {\n // Swallow bad regexes for incremental search.\n }\n if (parsedQuery) {\n cm.scrollIntoView(findNext(cm, !forward, parsedQuery), 30);\n } else {\n clearSearchHighlight(cm);\n cm.scrollTo(originalScrollPos.left, originalScrollPos.top);\n }\n }\n function onPromptKeyDown(e, query, close) {\n var keyName = CodeMirror.keyName(e);\n if (keyName == 'Esc' || keyName == 'Ctrl-C' || keyName == 'Ctrl-[' ||\n (keyName == 'Backspace' && query == '')) {\n vimGlobalState.searchHistoryController.pushInput(query);\n vimGlobalState.searchHistoryController.reset();\n updateSearchQuery(cm, originalQuery);\n clearSearchHighlight(cm);\n cm.scrollTo(originalScrollPos.left, originalScrollPos.top);\n CodeMirror.e_stop(e);\n clearInputState(cm);\n close();\n cm.focus();\n } else if (keyName == 'Ctrl-U') {\n // Ctrl-U clears input.\n CodeMirror.e_stop(e);\n close('');\n }\n }\n switch (command.searchArgs.querySrc) {\n case 'prompt':\n var macroModeState = vimGlobalState.macroModeState;\n if (macroModeState.isPlaying) {\n var query = macroModeState.replaySearchQueries.shift();\n handleQuery(query, true /** ignoreCase */, false /** smartCase */);\n } else {\n showPrompt(cm, {\n onClose: onPromptClose,\n prefix: promptPrefix,\n desc: searchPromptDesc,\n onKeyUp: onPromptKeyUp,\n onKeyDown: onPromptKeyDown\n });\n }\n break;\n case 'wordUnderCursor':\n var word = expandWordUnderCursor(cm, false /** inclusive */,\n true /** forward */, false /** bigWord */,\n true /** noSymbol */);\n var isKeyword = true;\n if (!word) {\n word = expandWordUnderCursor(cm, false /** inclusive */,\n true /** forward */, false /** bigWord */,\n false /** noSymbol */);\n isKeyword = false;\n }\n if (!word) {\n return;\n }\n var query = cm.getLine(word.start.line).substring(word.start.ch,\n word.end.ch);\n if (isKeyword && wholeWordOnly) {\n query = '\\\\b' + query + '\\\\b';\n } else {\n query = escapeRegex(query);\n }\n\n // cachedCursor is used to save the old position of the cursor\n // when * or # causes vim to seek for the nearest word and shift\n // the cursor before entering the motion.\n vimGlobalState.jumpList.cachedCursor = cm.getCursor();\n cm.setCursor(word.start);\n\n handleQuery(query, true /** ignoreCase */, false /** smartCase */);\n break;\n }\n },\n processEx: function(cm, vim, command) {\n function onPromptClose(input) {\n // Give the prompt some time to close so that if processCommand shows\n // an error, the elements don't overlap.\n vimGlobalState.exCommandHistoryController.pushInput(input);\n vimGlobalState.exCommandHistoryController.reset();\n exCommandDispatcher.processCommand(cm, input);\n }\n function onPromptKeyDown(e, input, close) {\n var keyName = CodeMirror.keyName(e), up;\n if (keyName == 'Esc' || keyName == 'Ctrl-C' || keyName == 'Ctrl-[' ||\n (keyName == 'Backspace' && input == '')) {\n vimGlobalState.exCommandHistoryController.pushInput(input);\n vimGlobalState.exCommandHistoryController.reset();\n CodeMirror.e_stop(e);\n clearInputState(cm);\n close();\n cm.focus();\n }\n if (keyName == 'Up' || keyName == 'Down') {\n up = keyName == 'Up' ? true : false;\n input = vimGlobalState.exCommandHistoryController.nextMatch(input, up) || '';\n close(input);\n } else if (keyName == 'Ctrl-U') {\n // Ctrl-U clears input.\n CodeMirror.e_stop(e);\n close('');\n } else {\n if ( keyName != 'Left' && keyName != 'Right' && keyName != 'Ctrl' && keyName != 'Alt' && keyName != 'Shift')\n vimGlobalState.exCommandHistoryController.reset();\n }\n }\n if (command.type == 'keyToEx') {\n // Handle user defined Ex to Ex mappings\n exCommandDispatcher.processCommand(cm, command.exArgs.input);\n } else {\n if (vim.visualMode) {\n showPrompt(cm, { onClose: onPromptClose, prefix: ':', value: '\\'<,\\'>',\n onKeyDown: onPromptKeyDown});\n } else {\n showPrompt(cm, { onClose: onPromptClose, prefix: ':',\n onKeyDown: onPromptKeyDown});\n }\n }\n },\n evalInput: function(cm, vim) {\n // If the motion comand is set, execute both the operator and motion.\n // Otherwise return.\n var inputState = vim.inputState;\n var motion = inputState.motion;\n var motionArgs = inputState.motionArgs || {};\n var operator = inputState.operator;\n var operatorArgs = inputState.operatorArgs || {};\n var registerName = inputState.registerName;\n var sel = vim.sel;\n // TODO: Make sure cm and vim selections are identical outside visual mode.\n var origHead = copyCursor(vim.visualMode ? clipCursorToContent(cm, sel.head): cm.getCursor('head'));\n var origAnchor = copyCursor(vim.visualMode ? clipCursorToContent(cm, sel.anchor) : cm.getCursor('anchor'));\n var oldHead = copyCursor(origHead);\n var oldAnchor = copyCursor(origAnchor);\n var newHead, newAnchor;\n var repeat;\n if (operator) {\n this.recordLastEdit(vim, inputState);\n }\n if (inputState.repeatOverride !== undefined) {\n // If repeatOverride is specified, that takes precedence over the\n // input state's repeat. Used by Ex mode and can be user defined.\n repeat = inputState.repeatOverride;\n } else {\n repeat = inputState.getRepeat();\n }\n if (repeat > 0 && motionArgs.explicitRepeat) {\n motionArgs.repeatIsExplicit = true;\n } else if (motionArgs.noRepeat ||\n (!motionArgs.explicitRepeat && repeat === 0)) {\n repeat = 1;\n motionArgs.repeatIsExplicit = false;\n }\n if (inputState.selectedCharacter) {\n // If there is a character input, stick it in all of the arg arrays.\n motionArgs.selectedCharacter = operatorArgs.selectedCharacter =\n inputState.selectedCharacter;\n }\n motionArgs.repeat = repeat;\n clearInputState(cm);\n if (motion) {\n var motionResult = motions[motion](cm, origHead, motionArgs, vim);\n vim.lastMotion = motions[motion];\n if (!motionResult) {\n return;\n }\n if (motionArgs.toJumplist) {\n var jumpList = vimGlobalState.jumpList;\n // if the current motion is # or *, use cachedCursor\n var cachedCursor = jumpList.cachedCursor;\n if (cachedCursor) {\n recordJumpPosition(cm, cachedCursor, motionResult);\n delete jumpList.cachedCursor;\n } else {\n recordJumpPosition(cm, origHead, motionResult);\n }\n }\n if (motionResult instanceof Array) {\n newAnchor = motionResult[0];\n newHead = motionResult[1];\n } else {\n newHead = motionResult;\n }\n // TODO: Handle null returns from motion commands better.\n if (!newHead) {\n newHead = copyCursor(origHead);\n }\n if (vim.visualMode) {\n if (!(vim.visualBlock && newHead.ch === Infinity)) {\n newHead = clipCursorToContent(cm, newHead, vim.visualBlock);\n }\n if (newAnchor) {\n newAnchor = clipCursorToContent(cm, newAnchor, true);\n }\n newAnchor = newAnchor || oldAnchor;\n sel.anchor = newAnchor;\n sel.head = newHead;\n updateCmSelection(cm);\n updateMark(cm, vim, '<',\n cursorIsBefore(newAnchor, newHead) ? newAnchor\n : newHead);\n updateMark(cm, vim, '>',\n cursorIsBefore(newAnchor, newHead) ? newHead\n : newAnchor);\n } else if (!operator) {\n newHead = clipCursorToContent(cm, newHead);\n cm.setCursor(newHead.line, newHead.ch);\n }\n }\n if (operator) {\n if (operatorArgs.lastSel) {\n // Replaying a visual mode operation\n newAnchor = oldAnchor;\n var lastSel = operatorArgs.lastSel;\n var lineOffset = Math.abs(lastSel.head.line - lastSel.anchor.line);\n var chOffset = Math.abs(lastSel.head.ch - lastSel.anchor.ch);\n if (lastSel.visualLine) {\n // Linewise Visual mode: The same number of lines.\n newHead = Pos(oldAnchor.line + lineOffset, oldAnchor.ch);\n } else if (lastSel.visualBlock) {\n // Blockwise Visual mode: The same number of lines and columns.\n newHead = Pos(oldAnchor.line + lineOffset, oldAnchor.ch + chOffset);\n } else if (lastSel.head.line == lastSel.anchor.line) {\n // Normal Visual mode within one line: The same number of characters.\n newHead = Pos(oldAnchor.line, oldAnchor.ch + chOffset);\n } else {\n // Normal Visual mode with several lines: The same number of lines, in the\n // last line the same number of characters as in the last line the last time.\n newHead = Pos(oldAnchor.line + lineOffset, oldAnchor.ch);\n }\n vim.visualMode = true;\n vim.visualLine = lastSel.visualLine;\n vim.visualBlock = lastSel.visualBlock;\n sel = vim.sel = {\n anchor: newAnchor,\n head: newHead\n };\n updateCmSelection(cm);\n } else if (vim.visualMode) {\n operatorArgs.lastSel = {\n anchor: copyCursor(sel.anchor),\n head: copyCursor(sel.head),\n visualBlock: vim.visualBlock,\n visualLine: vim.visualLine\n };\n }\n var curStart, curEnd, linewise, mode;\n var cmSel;\n if (vim.visualMode) {\n // Init visual op\n curStart = cursorMin(sel.head, sel.anchor);\n curEnd = cursorMax(sel.head, sel.anchor);\n linewise = vim.visualLine || operatorArgs.linewise;\n mode = vim.visualBlock ? 'block' :\n linewise ? 'line' :\n 'char';\n cmSel = makeCmSelection(cm, {\n anchor: curStart,\n head: curEnd\n }, mode);\n if (linewise) {\n var ranges = cmSel.ranges;\n if (mode == 'block') {\n // Linewise operators in visual block mode extend to end of line\n for (var i = 0; i < ranges.length; i++) {\n ranges[i].head.ch = lineLength(cm, ranges[i].head.line);\n }\n } else if (mode == 'line') {\n ranges[0].head = Pos(ranges[0].head.line + 1, 0);\n }\n }\n } else {\n // Init motion op\n curStart = copyCursor(newAnchor || oldAnchor);\n curEnd = copyCursor(newHead || oldHead);\n if (cursorIsBefore(curEnd, curStart)) {\n var tmp = curStart;\n curStart = curEnd;\n curEnd = tmp;\n }\n linewise = motionArgs.linewise || operatorArgs.linewise;\n if (linewise) {\n // Expand selection to entire line.\n expandSelectionToLine(cm, curStart, curEnd);\n } else if (motionArgs.forward) {\n // Clip to trailing newlines only if the motion goes forward.\n clipToLine(cm, curStart, curEnd);\n }\n mode = 'char';\n var exclusive = !motionArgs.inclusive || linewise;\n cmSel = makeCmSelection(cm, {\n anchor: curStart,\n head: curEnd\n }, mode, exclusive);\n }\n cm.setSelections(cmSel.ranges, cmSel.primary);\n vim.lastMotion = null;\n operatorArgs.repeat = repeat; // For indent in visual mode.\n operatorArgs.registerName = registerName;\n // Keep track of linewise as it affects how paste and change behave.\n operatorArgs.linewise = linewise;\n var operatorMoveTo = operators[operator](\n cm, operatorArgs, cmSel.ranges, oldAnchor, newHead);\n if (vim.visualMode) {\n exitVisualMode(cm, operatorMoveTo != null);\n }\n if (operatorMoveTo) {\n cm.setCursor(operatorMoveTo);\n }\n }\n },\n recordLastEdit: function(vim, inputState, actionCommand) {\n var macroModeState = vimGlobalState.macroModeState;\n if (macroModeState.isPlaying) { return; }\n vim.lastEditInputState = inputState;\n vim.lastEditActionCommand = actionCommand;\n macroModeState.lastInsertModeChanges.changes = [];\n macroModeState.lastInsertModeChanges.expectCursorActivityForChange = false;\n }\n };\n\n /**\n * typedef {Object{line:number,ch:number}} Cursor An object containing the\n * position of the cursor.\n */\n // All of the functions below return Cursor objects.\n var motions = {\n moveToTopLine: function(cm, _head, motionArgs) {\n var line = getUserVisibleLines(cm).top + motionArgs.repeat -1;\n return Pos(line, findFirstNonWhiteSpaceCharacter(cm.getLine(line)));\n },\n moveToMiddleLine: function(cm) {\n var range = getUserVisibleLines(cm);\n var line = Math.floor((range.top + range.bottom) * 0.5);\n return Pos(line, findFirstNonWhiteSpaceCharacter(cm.getLine(line)));\n },\n moveToBottomLine: function(cm, _head, motionArgs) {\n var line = getUserVisibleLines(cm).bottom - motionArgs.repeat +1;\n return Pos(line, findFirstNonWhiteSpaceCharacter(cm.getLine(line)));\n },\n expandToLine: function(_cm, head, motionArgs) {\n // Expands forward to end of line, and then to next line if repeat is\n // >1. Does not handle backward motion!\n var cur = head;\n return Pos(cur.line + motionArgs.repeat - 1, Infinity);\n },\n findNext: function(cm, _head, motionArgs) {\n var state = getSearchState(cm);\n var query = state.getQuery();\n if (!query) {\n return;\n }\n var prev = !motionArgs.forward;\n // If search is initiated with ? instead of /, negate direction.\n prev = (state.isReversed()) ? !prev : prev;\n highlightSearchMatches(cm, query);\n return findNext(cm, prev/** prev */, query, motionArgs.repeat);\n },\n goToMark: function(cm, _head, motionArgs, vim) {\n var mark = vim.marks[motionArgs.selectedCharacter];\n if (mark) {\n var pos = mark.find();\n return motionArgs.linewise ? { line: pos.line, ch: findFirstNonWhiteSpaceCharacter(cm.getLine(pos.line)) } : pos;\n }\n return null;\n },\n moveToOtherHighlightedEnd: function(cm, _head, motionArgs, vim) {\n if (vim.visualBlock && motionArgs.sameLine) {\n var sel = vim.sel;\n return [\n clipCursorToContent(cm, Pos(sel.anchor.line, sel.head.ch)),\n clipCursorToContent(cm, Pos(sel.head.line, sel.anchor.ch))\n ];\n } else {\n return ([vim.sel.head, vim.sel.anchor]);\n }\n },\n jumpToMark: function(cm, head, motionArgs, vim) {\n var best = head;\n for (var i = 0; i < motionArgs.repeat; i++) {\n var cursor = best;\n for (var key in vim.marks) {\n if (!isLowerCase(key)) {\n continue;\n }\n var mark = vim.marks[key].find();\n var isWrongDirection = (motionArgs.forward) ?\n cursorIsBefore(mark, cursor) : cursorIsBefore(cursor, mark);\n\n if (isWrongDirection) {\n continue;\n }\n if (motionArgs.linewise && (mark.line == cursor.line)) {\n continue;\n }\n\n var equal = cursorEqual(cursor, best);\n var between = (motionArgs.forward) ?\n cursorIsBetween(cursor, mark, best) :\n cursorIsBetween(best, mark, cursor);\n\n if (equal || between) {\n best = mark;\n }\n }\n }\n\n if (motionArgs.linewise) {\n // Vim places the cursor on the first non-whitespace character of\n // the line if there is one, else it places the cursor at the end\n // of the line, regardless of whether a mark was found.\n best = Pos(best.line, findFirstNonWhiteSpaceCharacter(cm.getLine(best.line)));\n }\n return best;\n },\n moveByCharacters: function(_cm, head, motionArgs) {\n var cur = head;\n var repeat = motionArgs.repeat;\n var ch = motionArgs.forward ? cur.ch + repeat : cur.ch - repeat;\n return Pos(cur.line, ch);\n },\n moveByLines: function(cm, head, motionArgs, vim) {\n var cur = head;\n var endCh = cur.ch;\n // Depending what our last motion was, we may want to do different\n // things. If our last motion was moving vertically, we want to\n // preserve the HPos from our last horizontal move. If our last motion\n // was going to the end of a line, moving vertically we should go to\n // the end of the line, etc.\n switch (vim.lastMotion) {\n case this.moveByLines:\n case this.moveByDisplayLines:\n case this.moveByScroll:\n case this.moveToColumn:\n case this.moveToEol:\n endCh = vim.lastHPos;\n break;\n default:\n vim.lastHPos = endCh;\n }\n var repeat = motionArgs.repeat+(motionArgs.repeatOffset||0);\n var line = motionArgs.forward ? cur.line + repeat : cur.line - repeat;\n var first = cm.firstLine();\n var last = cm.lastLine();\n // Vim go to line begin or line end when cursor at first/last line and\n // move to previous/next line is triggered.\n if (line < first && cur.line == first){\n return this.moveToStartOfLine(cm, head, motionArgs, vim);\n }else if (line > last && cur.line == last){\n return this.moveToEol(cm, head, motionArgs, vim);\n }\n if (motionArgs.toFirstChar){\n endCh=findFirstNonWhiteSpaceCharacter(cm.getLine(line));\n vim.lastHPos = endCh;\n }\n vim.lastHSPos = cm.charCoords(Pos(line, endCh),'div').left;\n return Pos(line, endCh);\n },\n moveByDisplayLines: function(cm, head, motionArgs, vim) {\n var cur = head;\n switch (vim.lastMotion) {\n case this.moveByDisplayLines:\n case this.moveByScroll:\n case this.moveByLines:\n case this.moveToColumn:\n case this.moveToEol:\n break;\n default:\n vim.lastHSPos = cm.charCoords(cur,'div').left;\n }\n var repeat = motionArgs.repeat;\n var res=cm.findPosV(cur,(motionArgs.forward ? repeat : -repeat),'line',vim.lastHSPos);\n if (res.hitSide) {\n if (motionArgs.forward) {\n var lastCharCoords = cm.charCoords(res, 'div');\n var goalCoords = { top: lastCharCoords.top + 8, left: vim.lastHSPos };\n var res = cm.coordsChar(goalCoords, 'div');\n } else {\n var resCoords = cm.charCoords(Pos(cm.firstLine(), 0), 'div');\n resCoords.left = vim.lastHSPos;\n res = cm.coordsChar(resCoords, 'div');\n }\n }\n vim.lastHPos = res.ch;\n return res;\n },\n moveByPage: function(cm, head, motionArgs) {\n // CodeMirror only exposes functions that move the cursor page down, so\n // doing this bad hack to move the cursor and move it back. evalInput\n // will move the cursor to where it should be in the end.\n var curStart = head;\n var repeat = motionArgs.repeat;\n return cm.findPosV(curStart, (motionArgs.forward ? repeat : -repeat), 'page');\n },\n moveByParagraph: function(cm, head, motionArgs) {\n var dir = motionArgs.forward ? 1 : -1;\n return findParagraph(cm, head, motionArgs.repeat, dir);\n },\n moveByScroll: function(cm, head, motionArgs, vim) {\n var scrollbox = cm.getScrollInfo();\n var curEnd = null;\n var repeat = motionArgs.repeat;\n if (!repeat) {\n repeat = scrollbox.clientHeight / (2 * cm.defaultTextHeight());\n }\n var orig = cm.charCoords(head, 'local');\n motionArgs.repeat = repeat;\n var curEnd = motions.moveByDisplayLines(cm, head, motionArgs, vim);\n if (!curEnd) {\n return null;\n }\n var dest = cm.charCoords(curEnd, 'local');\n cm.scrollTo(null, scrollbox.top + dest.top - orig.top);\n return curEnd;\n },\n moveByWords: function(cm, head, motionArgs) {\n return moveToWord(cm, head, motionArgs.repeat, !!motionArgs.forward,\n !!motionArgs.wordEnd, !!motionArgs.bigWord);\n },\n moveTillCharacter: function(cm, _head, motionArgs) {\n var repeat = motionArgs.repeat;\n var curEnd = moveToCharacter(cm, repeat, motionArgs.forward,\n motionArgs.selectedCharacter);\n var increment = motionArgs.forward ? -1 : 1;\n recordLastCharacterSearch(increment, motionArgs);\n if (!curEnd) return null;\n curEnd.ch += increment;\n return curEnd;\n },\n moveToCharacter: function(cm, head, motionArgs) {\n var repeat = motionArgs.repeat;\n recordLastCharacterSearch(0, motionArgs);\n return moveToCharacter(cm, repeat, motionArgs.forward,\n motionArgs.selectedCharacter) || head;\n },\n moveToSymbol: function(cm, head, motionArgs) {\n var repeat = motionArgs.repeat;\n return findSymbol(cm, repeat, motionArgs.forward,\n motionArgs.selectedCharacter) || head;\n },\n moveToColumn: function(cm, head, motionArgs, vim) {\n var repeat = motionArgs.repeat;\n // repeat is equivalent to which column we want to move to!\n vim.lastHPos = repeat - 1;\n vim.lastHSPos = cm.charCoords(head,'div').left;\n return moveToColumn(cm, repeat);\n },\n moveToEol: function(cm, head, motionArgs, vim) {\n var cur = head;\n vim.lastHPos = Infinity;\n var retval= Pos(cur.line + motionArgs.repeat - 1, Infinity);\n var end=cm.clipPos(retval);\n end.ch--;\n vim.lastHSPos = cm.charCoords(end,'div').left;\n return retval;\n },\n moveToFirstNonWhiteSpaceCharacter: function(cm, head) {\n // Go to the start of the line where the text begins, or the end for\n // whitespace-only lines\n var cursor = head;\n return Pos(cursor.line,\n findFirstNonWhiteSpaceCharacter(cm.getLine(cursor.line)));\n },\n moveToMatchedSymbol: function(cm, head) {\n var cursor = head;\n var line = cursor.line;\n var ch = cursor.ch;\n var lineText = cm.getLine(line);\n var symbol;\n do {\n symbol = lineText.charAt(ch++);\n if (symbol && isMatchableSymbol(symbol)) {\n var style = cm.getTokenTypeAt(Pos(line, ch));\n if (style !== \"string\" && style !== \"comment\") {\n break;\n }\n }\n } while (symbol);\n if (symbol) {\n var matched = cm.findMatchingBracket(Pos(line, ch));\n return matched.to;\n } else {\n return cursor;\n }\n },\n moveToStartOfLine: function(_cm, head) {\n return Pos(head.line, 0);\n },\n moveToLineOrEdgeOfDocument: function(cm, _head, motionArgs) {\n var lineNum = motionArgs.forward ? cm.lastLine() : cm.firstLine();\n if (motionArgs.repeatIsExplicit) {\n lineNum = motionArgs.repeat - cm.getOption('firstLineNumber');\n }\n return Pos(lineNum,\n findFirstNonWhiteSpaceCharacter(cm.getLine(lineNum)));\n },\n textObjectManipulation: function(cm, head, motionArgs, vim) {\n // TODO: lots of possible exceptions that can be thrown here. Try da(\n // outside of a () block.\n\n // TODO: adding <> >< to this map doesn't work, presumably because\n // they're operators\n var mirroredPairs = {'(': ')', ')': '(',\n '{': '}', '}': '{',\n '[': ']', ']': '['};\n var selfPaired = {'\\'': true, '\"': true};\n\n var character = motionArgs.selectedCharacter;\n // 'b' refers to '()' block.\n // 'B' refers to '{}' block.\n if (character == 'b') {\n character = '(';\n } else if (character == 'B') {\n character = '{';\n }\n\n // Inclusive is the difference between a and i\n // TODO: Instead of using the additional text object map to perform text\n // object operations, merge the map into the defaultKeyMap and use\n // motionArgs to define behavior. Define separate entries for 'aw',\n // 'iw', 'a[', 'i[', etc.\n var inclusive = !motionArgs.textObjectInner;\n\n var tmp;\n if (mirroredPairs[character]) {\n tmp = selectCompanionObject(cm, head, character, inclusive);\n } else if (selfPaired[character]) {\n tmp = findBeginningAndEnd(cm, head, character, inclusive);\n } else if (character === 'W') {\n tmp = expandWordUnderCursor(cm, inclusive, true /** forward */,\n true /** bigWord */);\n } else if (character === 'w') {\n tmp = expandWordUnderCursor(cm, inclusive, true /** forward */,\n false /** bigWord */);\n } else if (character === 'p') {\n tmp = findParagraph(cm, head, motionArgs.repeat, 0, inclusive);\n motionArgs.linewise = true;\n if (vim.visualMode) {\n if (!vim.visualLine) { vim.visualLine = true; }\n } else {\n var operatorArgs = vim.inputState.operatorArgs;\n if (operatorArgs) { operatorArgs.linewise = true; }\n tmp.end.line--;\n }\n } else {\n // No text object defined for this, don't move.\n return null;\n }\n\n if (!cm.state.vim.visualMode) {\n return [tmp.start, tmp.end];\n } else {\n return expandSelection(cm, tmp.start, tmp.end);\n }\n },\n\n repeatLastCharacterSearch: function(cm, head, motionArgs) {\n var lastSearch = vimGlobalState.lastChararacterSearch;\n var repeat = motionArgs.repeat;\n var forward = motionArgs.forward === lastSearch.forward;\n var increment = (lastSearch.increment ? 1 : 0) * (forward ? -1 : 1);\n cm.moveH(-increment, 'char');\n motionArgs.inclusive = forward ? true : false;\n var curEnd = moveToCharacter(cm, repeat, forward, lastSearch.selectedCharacter);\n if (!curEnd) {\n cm.moveH(increment, 'char');\n return head;\n }\n curEnd.ch += increment;\n return curEnd;\n }\n };\n\n function defineMotion(name, fn) {\n motions[name] = fn;\n }\n\n function fillArray(val, times) {\n var arr = [];\n for (var i = 0; i < times; i++) {\n arr.push(val);\n }\n return arr;\n }\n /**\n * An operator acts on a text selection. It receives the list of selections\n * as input. The corresponding CodeMirror selection is guaranteed to\n * match the input selection.\n */\n var operators = {\n change: function(cm, args, ranges) {\n var finalHead, text;\n var vim = cm.state.vim;\n vimGlobalState.macroModeState.lastInsertModeChanges.inVisualBlock = vim.visualBlock;\n if (!vim.visualMode) {\n var anchor = ranges[0].anchor,\n head = ranges[0].head;\n text = cm.getRange(anchor, head);\n var lastState = vim.lastEditInputState || {};\n if (lastState.motion == \"moveByWords\" && !isWhiteSpaceString(text)) {\n // Exclude trailing whitespace if the range is not all whitespace.\n var match = (/\\s+$/).exec(text);\n if (match && lastState.motionArgs && lastState.motionArgs.forward) {\n head = offsetCursor(head, 0, - match[0].length);\n text = text.slice(0, - match[0].length);\n }\n }\n var prevLineEnd = new Pos(anchor.line - 1, Number.MAX_VALUE);\n var wasLastLine = cm.firstLine() == cm.lastLine();\n if (head.line > cm.lastLine() && args.linewise && !wasLastLine) {\n cm.replaceRange('', prevLineEnd, head);\n } else {\n cm.replaceRange('', anchor, head);\n }\n if (args.linewise) {\n // Push the next line back down, if there is a next line.\n if (!wasLastLine) {\n cm.setCursor(prevLineEnd);\n CodeMirror.commands.newlineAndIndent(cm);\n }\n // make sure cursor ends up at the end of the line.\n anchor.ch = Number.MAX_VALUE;\n }\n finalHead = anchor;\n } else {\n text = cm.getSelection();\n var replacement = fillArray('', ranges.length);\n cm.replaceSelections(replacement);\n finalHead = cursorMin(ranges[0].head, ranges[0].anchor);\n }\n vimGlobalState.registerController.pushText(\n args.registerName, 'change', text,\n args.linewise, ranges.length > 1);\n actions.enterInsertMode(cm, {head: finalHead}, cm.state.vim);\n },\n // delete is a javascript keyword.\n 'delete': function(cm, args, ranges) {\n var finalHead, text;\n var vim = cm.state.vim;\n if (!vim.visualBlock) {\n var anchor = ranges[0].anchor,\n head = ranges[0].head;\n if (args.linewise &&\n head.line != cm.firstLine() &&\n anchor.line == cm.lastLine() &&\n anchor.line == head.line - 1) {\n // Special case for dd on last line (and first line).\n if (anchor.line == cm.firstLine()) {\n anchor.ch = 0;\n } else {\n anchor = Pos(anchor.line - 1, lineLength(cm, anchor.line - 1));\n }\n }\n text = cm.getRange(anchor, head);\n cm.replaceRange('', anchor, head);\n finalHead = anchor;\n if (args.linewise) {\n finalHead = motions.moveToFirstNonWhiteSpaceCharacter(cm, anchor);\n }\n } else {\n text = cm.getSelection();\n var replacement = fillArray('', ranges.length);\n cm.replaceSelections(replacement);\n finalHead = ranges[0].anchor;\n }\n vimGlobalState.registerController.pushText(\n args.registerName, 'delete', text,\n args.linewise, vim.visualBlock);\n return clipCursorToContent(cm, finalHead);\n },\n indent: function(cm, args, ranges) {\n var vim = cm.state.vim;\n var startLine = ranges[0].anchor.line;\n var endLine = vim.visualBlock ?\n ranges[ranges.length - 1].anchor.line :\n ranges[0].head.line;\n // In visual mode, n> shifts the selection right n times, instead of\n // shifting n lines right once.\n var repeat = (vim.visualMode) ? args.repeat : 1;\n if (args.linewise) {\n // The only way to delete a newline is to delete until the start of\n // the next line, so in linewise mode evalInput will include the next\n // line. We don't want this in indent, so we go back a line.\n endLine--;\n }\n for (var i = startLine; i <= endLine; i++) {\n for (var j = 0; j < repeat; j++) {\n cm.indentLine(i, args.indentRight);\n }\n }\n return motions.moveToFirstNonWhiteSpaceCharacter(cm, ranges[0].anchor);\n },\n changeCase: function(cm, args, ranges, oldAnchor, newHead) {\n var selections = cm.getSelections();\n var swapped = [];\n var toLower = args.toLower;\n for (var j = 0; j < selections.length; j++) {\n var toSwap = selections[j];\n var text = '';\n if (toLower === true) {\n text = toSwap.toLowerCase();\n } else if (toLower === false) {\n text = toSwap.toUpperCase();\n } else {\n for (var i = 0; i < toSwap.length; i++) {\n var character = toSwap.charAt(i);\n text += isUpperCase(character) ? character.toLowerCase() :\n character.toUpperCase();\n }\n }\n swapped.push(text);\n }\n cm.replaceSelections(swapped);\n if (args.shouldMoveCursor){\n return newHead;\n } else if (!cm.state.vim.visualMode && args.linewise && ranges[0].anchor.line + 1 == ranges[0].head.line) {\n return motions.moveToFirstNonWhiteSpaceCharacter(cm, oldAnchor);\n } else if (args.linewise){\n return oldAnchor;\n } else {\n return cursorMin(ranges[0].anchor, ranges[0].head);\n }\n },\n yank: function(cm, args, ranges, oldAnchor) {\n var vim = cm.state.vim;\n var text = cm.getSelection();\n var endPos = vim.visualMode\n ? cursorMin(vim.sel.anchor, vim.sel.head, ranges[0].head, ranges[0].anchor)\n : oldAnchor;\n vimGlobalState.registerController.pushText(\n args.registerName, 'yank',\n text, args.linewise, vim.visualBlock);\n return endPos;\n }\n };\n\n function defineOperator(name, fn) {\n operators[name] = fn;\n }\n\n var actions = {\n jumpListWalk: function(cm, actionArgs, vim) {\n if (vim.visualMode) {\n return;\n }\n var repeat = actionArgs.repeat;\n var forward = actionArgs.forward;\n var jumpList = vimGlobalState.jumpList;\n\n var mark = jumpList.move(cm, forward ? repeat : -repeat);\n var markPos = mark ? mark.find() : undefined;\n markPos = markPos ? markPos : cm.getCursor();\n cm.setCursor(markPos);\n },\n scroll: function(cm, actionArgs, vim) {\n if (vim.visualMode) {\n return;\n }\n var repeat = actionArgs.repeat || 1;\n var lineHeight = cm.defaultTextHeight();\n var top = cm.getScrollInfo().top;\n var delta = lineHeight * repeat;\n var newPos = actionArgs.forward ? top + delta : top - delta;\n var cursor = copyCursor(cm.getCursor());\n var cursorCoords = cm.charCoords(cursor, 'local');\n if (actionArgs.forward) {\n if (newPos > cursorCoords.top) {\n cursor.line += (newPos - cursorCoords.top) / lineHeight;\n cursor.line = Math.ceil(cursor.line);\n cm.setCursor(cursor);\n cursorCoords = cm.charCoords(cursor, 'local');\n cm.scrollTo(null, cursorCoords.top);\n } else {\n // Cursor stays within bounds. Just reposition the scroll window.\n cm.scrollTo(null, newPos);\n }\n } else {\n var newBottom = newPos + cm.getScrollInfo().clientHeight;\n if (newBottom < cursorCoords.bottom) {\n cursor.line -= (cursorCoords.bottom - newBottom) / lineHeight;\n cursor.line = Math.floor(cursor.line);\n cm.setCursor(cursor);\n cursorCoords = cm.charCoords(cursor, 'local');\n cm.scrollTo(\n null, cursorCoords.bottom - cm.getScrollInfo().clientHeight);\n } else {\n // Cursor stays within bounds. Just reposition the scroll window.\n cm.scrollTo(null, newPos);\n }\n }\n },\n scrollToCursor: function(cm, actionArgs) {\n var lineNum = cm.getCursor().line;\n var charCoords = cm.charCoords(Pos(lineNum, 0), 'local');\n var height = cm.getScrollInfo().clientHeight;\n var y = charCoords.top;\n var lineHeight = charCoords.bottom - y;\n switch (actionArgs.position) {\n case 'center': y = y - (height / 2) + lineHeight;\n break;\n case 'bottom': y = y - height + lineHeight;\n break;\n }\n cm.scrollTo(null, y);\n },\n replayMacro: function(cm, actionArgs, vim) {\n var registerName = actionArgs.selectedCharacter;\n var repeat = actionArgs.repeat;\n var macroModeState = vimGlobalState.macroModeState;\n if (registerName == '@') {\n registerName = macroModeState.latestRegister;\n }\n while(repeat--){\n executeMacroRegister(cm, vim, macroModeState, registerName);\n }\n },\n enterMacroRecordMode: function(cm, actionArgs) {\n var macroModeState = vimGlobalState.macroModeState;\n var registerName = actionArgs.selectedCharacter;\n macroModeState.enterMacroRecordMode(cm, registerName);\n },\n enterInsertMode: function(cm, actionArgs, vim) {\n if (cm.getOption('readOnly')) { return; }\n vim.insertMode = true;\n vim.insertModeRepeat = actionArgs && actionArgs.repeat || 1;\n var insertAt = (actionArgs) ? actionArgs.insertAt : null;\n var sel = vim.sel;\n var head = actionArgs.head || cm.getCursor('head');\n var height = cm.listSelections().length;\n if (insertAt == 'eol') {\n head = Pos(head.line, lineLength(cm, head.line));\n } else if (insertAt == 'charAfter') {\n head = offsetCursor(head, 0, 1);\n } else if (insertAt == 'firstNonBlank') {\n head = motions.moveToFirstNonWhiteSpaceCharacter(cm, head);\n } else if (insertAt == 'startOfSelectedArea') {\n if (!vim.visualBlock) {\n if (sel.head.line < sel.anchor.line) {\n head = sel.head;\n } else {\n head = Pos(sel.anchor.line, 0);\n }\n } else {\n head = Pos(\n Math.min(sel.head.line, sel.anchor.line),\n Math.min(sel.head.ch, sel.anchor.ch));\n height = Math.abs(sel.head.line - sel.anchor.line) + 1;\n }\n } else if (insertAt == 'endOfSelectedArea') {\n if (!vim.visualBlock) {\n if (sel.head.line >= sel.anchor.line) {\n head = offsetCursor(sel.head, 0, 1);\n } else {\n head = Pos(sel.anchor.line, 0);\n }\n } else {\n head = Pos(\n Math.min(sel.head.line, sel.anchor.line),\n Math.max(sel.head.ch + 1, sel.anchor.ch));\n height = Math.abs(sel.head.line - sel.anchor.line) + 1;\n }\n } else if (insertAt == 'inplace') {\n if (vim.visualMode){\n return;\n }\n }\n cm.setOption('keyMap', 'vim-insert');\n cm.setOption('disableInput', false);\n if (actionArgs && actionArgs.replace) {\n // Handle Replace-mode as a special case of insert mode.\n cm.toggleOverwrite(true);\n cm.setOption('keyMap', 'vim-replace');\n CodeMirror.signal(cm, \"vim-mode-change\", {mode: \"replace\"});\n } else {\n cm.setOption('keyMap', 'vim-insert');\n CodeMirror.signal(cm, \"vim-mode-change\", {mode: \"insert\"});\n }\n if (!vimGlobalState.macroModeState.isPlaying) {\n // Only record if not replaying.\n cm.on('change', onChange);\n CodeMirror.on(cm.getInputField(), 'keydown', onKeyEventTargetKeyDown);\n }\n if (vim.visualMode) {\n exitVisualMode(cm);\n }\n selectForInsert(cm, head, height);\n },\n toggleVisualMode: function(cm, actionArgs, vim) {\n var repeat = actionArgs.repeat;\n var anchor = cm.getCursor();\n var head;\n // TODO: The repeat should actually select number of characters/lines\n // equal to the repeat times the size of the previous visual\n // operation.\n if (!vim.visualMode) {\n // Entering visual mode\n vim.visualMode = true;\n vim.visualLine = !!actionArgs.linewise;\n vim.visualBlock = !!actionArgs.blockwise;\n head = clipCursorToContent(\n cm, Pos(anchor.line, anchor.ch + repeat - 1),\n true /** includeLineBreak */);\n vim.sel = {\n anchor: anchor,\n head: head\n };\n CodeMirror.signal(cm, \"vim-mode-change\", {mode: \"visual\", subMode: vim.visualLine ? \"linewise\" : vim.visualBlock ? \"blockwise\" : \"\"});\n updateCmSelection(cm);\n updateMark(cm, vim, '<', cursorMin(anchor, head));\n updateMark(cm, vim, '>', cursorMax(anchor, head));\n } else if (vim.visualLine ^ actionArgs.linewise ||\n vim.visualBlock ^ actionArgs.blockwise) {\n // Toggling between modes\n vim.visualLine = !!actionArgs.linewise;\n vim.visualBlock = !!actionArgs.blockwise;\n CodeMirror.signal(cm, \"vim-mode-change\", {mode: \"visual\", subMode: vim.visualLine ? \"linewise\" : vim.visualBlock ? \"blockwise\" : \"\"});\n updateCmSelection(cm);\n } else {\n exitVisualMode(cm);\n }\n },\n reselectLastSelection: function(cm, _actionArgs, vim) {\n var lastSelection = vim.lastSelection;\n if (vim.visualMode) {\n updateLastSelection(cm, vim);\n }\n if (lastSelection) {\n var anchor = lastSelection.anchorMark.find();\n var head = lastSelection.headMark.find();\n if (!anchor || !head) {\n // If the marks have been destroyed due to edits, do nothing.\n return;\n }\n vim.sel = {\n anchor: anchor,\n head: head\n };\n vim.visualMode = true;\n vim.visualLine = lastSelection.visualLine;\n vim.visualBlock = lastSelection.visualBlock;\n updateCmSelection(cm);\n updateMark(cm, vim, '<', cursorMin(anchor, head));\n updateMark(cm, vim, '>', cursorMax(anchor, head));\n CodeMirror.signal(cm, 'vim-mode-change', {\n mode: 'visual',\n subMode: vim.visualLine ? 'linewise' :\n vim.visualBlock ? 'blockwise' : ''});\n }\n },\n joinLines: function(cm, actionArgs, vim) {\n var curStart, curEnd;\n if (vim.visualMode) {\n curStart = cm.getCursor('anchor');\n curEnd = cm.getCursor('head');\n if (cursorIsBefore(curEnd, curStart)) {\n var tmp = curEnd;\n curEnd = curStart;\n curStart = tmp;\n }\n curEnd.ch = lineLength(cm, curEnd.line) - 1;\n } else {\n // Repeat is the number of lines to join. Minimum 2 lines.\n var repeat = Math.max(actionArgs.repeat, 2);\n curStart = cm.getCursor();\n curEnd = clipCursorToContent(cm, Pos(curStart.line + repeat - 1,\n Infinity));\n }\n var finalCh = 0;\n for (var i = curStart.line; i < curEnd.line; i++) {\n finalCh = lineLength(cm, curStart.line);\n var tmp = Pos(curStart.line + 1,\n lineLength(cm, curStart.line + 1));\n var text = cm.getRange(curStart, tmp);\n text = text.replace(/\\n\\s*/g, ' ');\n cm.replaceRange(text, curStart, tmp);\n }\n var curFinalPos = Pos(curStart.line, finalCh);\n if (vim.visualMode) {\n exitVisualMode(cm, false);\n }\n cm.setCursor(curFinalPos);\n },\n newLineAndEnterInsertMode: function(cm, actionArgs, vim) {\n vim.insertMode = true;\n var insertAt = copyCursor(cm.getCursor());\n if (insertAt.line === cm.firstLine() && !actionArgs.after) {\n // Special case for inserting newline before start of document.\n cm.replaceRange('\\n', Pos(cm.firstLine(), 0));\n cm.setCursor(cm.firstLine(), 0);\n } else {\n insertAt.line = (actionArgs.after) ? insertAt.line :\n insertAt.line - 1;\n insertAt.ch = lineLength(cm, insertAt.line);\n cm.setCursor(insertAt);\n var newlineFn = CodeMirror.commands.newlineAndIndentContinueComment ||\n CodeMirror.commands.newlineAndIndent;\n newlineFn(cm);\n }\n this.enterInsertMode(cm, { repeat: actionArgs.repeat }, vim);\n },\n paste: function(cm, actionArgs, vim) {\n var cur = copyCursor(cm.getCursor());\n var register = vimGlobalState.registerController.getRegister(\n actionArgs.registerName);\n var text = register.toString();\n if (!text) {\n return;\n }\n if (actionArgs.matchIndent) {\n var tabSize = cm.getOption(\"tabSize\");\n // length that considers tabs and tabSize\n var whitespaceLength = function(str) {\n var tabs = (str.split(\"\\t\").length - 1);\n var spaces = (str.split(\" \").length - 1);\n return tabs * tabSize + spaces * 1;\n };\n var currentLine = cm.getLine(cm.getCursor().line);\n var indent = whitespaceLength(currentLine.match(/^\\s*/)[0]);\n // chomp last newline b/c don't want it to match /^\\s*/gm\n var chompedText = text.replace(/\\n$/, '');\n var wasChomped = text !== chompedText;\n var firstIndent = whitespaceLength(text.match(/^\\s*/)[0]);\n var text = chompedText.replace(/^\\s*/gm, function(wspace) {\n var newIndent = indent + (whitespaceLength(wspace) - firstIndent);\n if (newIndent < 0) {\n return \"\";\n }\n else if (cm.getOption(\"indentWithTabs\")) {\n var quotient = Math.floor(newIndent / tabSize);\n return Array(quotient + 1).join('\\t');\n }\n else {\n return Array(newIndent + 1).join(' ');\n }\n });\n text += wasChomped ? \"\\n\" : \"\";\n }\n if (actionArgs.repeat > 1) {\n var text = Array(actionArgs.repeat + 1).join(text);\n }\n var linewise = register.linewise;\n var blockwise = register.blockwise;\n if (linewise) {\n if(vim.visualMode) {\n text = vim.visualLine ? text.slice(0, -1) : '\\n' + text.slice(0, text.length - 1) + '\\n';\n } else if (actionArgs.after) {\n // Move the newline at the end to the start instead, and paste just\n // before the newline character of the line we are on right now.\n text = '\\n' + text.slice(0, text.length - 1);\n cur.ch = lineLength(cm, cur.line);\n } else {\n cur.ch = 0;\n }\n } else {\n if (blockwise) {\n text = text.split('\\n');\n for (var i = 0; i < text.length; i++) {\n text[i] = (text[i] == '') ? ' ' : text[i];\n }\n }\n cur.ch += actionArgs.after ? 1 : 0;\n }\n var curPosFinal;\n var idx;\n if (vim.visualMode) {\n // save the pasted text for reselection if the need arises\n vim.lastPastedText = text;\n var lastSelectionCurEnd;\n var selectedArea = getSelectedAreaRange(cm, vim);\n var selectionStart = selectedArea[0];\n var selectionEnd = selectedArea[1];\n var selectedText = cm.getSelection();\n var selections = cm.listSelections();\n var emptyStrings = new Array(selections.length).join('1').split('1');\n // save the curEnd marker before it get cleared due to cm.replaceRange.\n if (vim.lastSelection) {\n lastSelectionCurEnd = vim.lastSelection.headMark.find();\n }\n // push the previously selected text to unnamed register\n vimGlobalState.registerController.unnamedRegister.setText(selectedText);\n if (blockwise) {\n // first delete the selected text\n cm.replaceSelections(emptyStrings);\n // Set new selections as per the block length of the yanked text\n selectionEnd = Pos(selectionStart.line + text.length-1, selectionStart.ch);\n cm.setCursor(selectionStart);\n selectBlock(cm, selectionEnd);\n cm.replaceSelections(text);\n curPosFinal = selectionStart;\n } else if (vim.visualBlock) {\n cm.replaceSelections(emptyStrings);\n cm.setCursor(selectionStart);\n cm.replaceRange(text, selectionStart, selectionStart);\n curPosFinal = selectionStart;\n } else {\n cm.replaceRange(text, selectionStart, selectionEnd);\n curPosFinal = cm.posFromIndex(cm.indexFromPos(selectionStart) + text.length - 1);\n }\n // restore the the curEnd marker\n if(lastSelectionCurEnd) {\n vim.lastSelection.headMark = cm.setBookmark(lastSelectionCurEnd);\n }\n if (linewise) {\n curPosFinal.ch=0;\n }\n } else {\n if (blockwise) {\n cm.setCursor(cur);\n for (var i = 0; i < text.length; i++) {\n var line = cur.line+i;\n if (line > cm.lastLine()) {\n cm.replaceRange('\\n', Pos(line, 0));\n }\n var lastCh = lineLength(cm, line);\n if (lastCh < cur.ch) {\n extendLineToColumn(cm, line, cur.ch);\n }\n }\n cm.setCursor(cur);\n selectBlock(cm, Pos(cur.line + text.length-1, cur.ch));\n cm.replaceSelections(text);\n curPosFinal = cur;\n } else {\n cm.replaceRange(text, cur);\n // Now fine tune the cursor to where we want it.\n if (linewise && actionArgs.after) {\n curPosFinal = Pos(\n cur.line + 1,\n findFirstNonWhiteSpaceCharacter(cm.getLine(cur.line + 1)));\n } else if (linewise && !actionArgs.after) {\n curPosFinal = Pos(\n cur.line,\n findFirstNonWhiteSpaceCharacter(cm.getLine(cur.line)));\n } else if (!linewise && actionArgs.after) {\n idx = cm.indexFromPos(cur);\n curPosFinal = cm.posFromIndex(idx + text.length - 1);\n } else {\n idx = cm.indexFromPos(cur);\n curPosFinal = cm.posFromIndex(idx + text.length);\n }\n }\n }\n if (vim.visualMode) {\n exitVisualMode(cm, false);\n }\n cm.setCursor(curPosFinal);\n },\n undo: function(cm, actionArgs) {\n cm.operation(function() {\n repeatFn(cm, CodeMirror.commands.undo, actionArgs.repeat)();\n cm.setCursor(cm.getCursor('anchor'));\n });\n },\n redo: function(cm, actionArgs) {\n repeatFn(cm, CodeMirror.commands.redo, actionArgs.repeat)();\n },\n setRegister: function(_cm, actionArgs, vim) {\n vim.inputState.registerName = actionArgs.selectedCharacter;\n },\n setMark: function(cm, actionArgs, vim) {\n var markName = actionArgs.selectedCharacter;\n updateMark(cm, vim, markName, cm.getCursor());\n },\n replace: function(cm, actionArgs, vim) {\n var replaceWith = actionArgs.selectedCharacter;\n var curStart = cm.getCursor();\n var replaceTo;\n var curEnd;\n var selections = cm.listSelections();\n if (vim.visualMode) {\n curStart = cm.getCursor('start');\n curEnd = cm.getCursor('end');\n } else {\n var line = cm.getLine(curStart.line);\n replaceTo = curStart.ch + actionArgs.repeat;\n if (replaceTo > line.length) {\n replaceTo=line.length;\n }\n curEnd = Pos(curStart.line, replaceTo);\n }\n if (replaceWith=='\\n') {\n if (!vim.visualMode) cm.replaceRange('', curStart, curEnd);\n // special case, where vim help says to replace by just one line-break\n (CodeMirror.commands.newlineAndIndentContinueComment || CodeMirror.commands.newlineAndIndent)(cm);\n } else {\n var replaceWithStr = cm.getRange(curStart, curEnd);\n //replace all characters in range by selected, but keep linebreaks\n replaceWithStr = replaceWithStr.replace(/[^\\n]/g, replaceWith);\n if (vim.visualBlock) {\n // Tabs are split in visua block before replacing\n var spaces = new Array(cm.getOption(\"tabSize\")+1).join(' ');\n replaceWithStr = cm.getSelection();\n replaceWithStr = replaceWithStr.replace(/\\t/g, spaces).replace(/[^\\n]/g, replaceWith).split('\\n');\n cm.replaceSelections(replaceWithStr);\n } else {\n cm.replaceRange(replaceWithStr, curStart, curEnd);\n }\n if (vim.visualMode) {\n curStart = cursorIsBefore(selections[0].anchor, selections[0].head) ?\n selections[0].anchor : selections[0].head;\n cm.setCursor(curStart);\n exitVisualMode(cm, false);\n } else {\n cm.setCursor(offsetCursor(curEnd, 0, -1));\n }\n }\n },\n incrementNumberToken: function(cm, actionArgs) {\n var cur = cm.getCursor();\n var lineStr = cm.getLine(cur.line);\n var re = /-?\\d+/g;\n var match;\n var start;\n var end;\n var numberStr;\n var token;\n while ((match = re.exec(lineStr)) !== null) {\n token = match[0];\n start = match.index;\n end = start + token.length;\n if (cur.ch < end)break;\n }\n if (!actionArgs.backtrack && (end <= cur.ch))return;\n if (token) {\n var increment = actionArgs.increase ? 1 : -1;\n var number = parseInt(token) + (increment * actionArgs.repeat);\n var from = Pos(cur.line, start);\n var to = Pos(cur.line, end);\n numberStr = number.toString();\n cm.replaceRange(numberStr, from, to);\n } else {\n return;\n }\n cm.setCursor(Pos(cur.line, start + numberStr.length - 1));\n },\n repeatLastEdit: function(cm, actionArgs, vim) {\n var lastEditInputState = vim.lastEditInputState;\n if (!lastEditInputState) { return; }\n var repeat = actionArgs.repeat;\n if (repeat && actionArgs.repeatIsExplicit) {\n vim.lastEditInputState.repeatOverride = repeat;\n } else {\n repeat = vim.lastEditInputState.repeatOverride || repeat;\n }\n repeatLastEdit(cm, vim, repeat, false /** repeatForInsert */);\n },\n exitInsertMode: exitInsertMode\n };\n\n function defineAction(name, fn) {\n actions[name] = fn;\n }\n\n /*\n * Below are miscellaneous utility functions used by vim.js\n */\n\n /**\n * Clips cursor to ensure that line is within the buffer's range\n * If includeLineBreak is true, then allow cur.ch == lineLength.\n */\n function clipCursorToContent(cm, cur, includeLineBreak) {\n var line = Math.min(Math.max(cm.firstLine(), cur.line), cm.lastLine() );\n var maxCh = lineLength(cm, line) - 1;\n maxCh = (includeLineBreak) ? maxCh + 1 : maxCh;\n var ch = Math.min(Math.max(0, cur.ch), maxCh);\n return Pos(line, ch);\n }\n function copyArgs(args) {\n var ret = {};\n for (var prop in args) {\n if (args.hasOwnProperty(prop)) {\n ret[prop] = args[prop];\n }\n }\n return ret;\n }\n function offsetCursor(cur, offsetLine, offsetCh) {\n if (typeof offsetLine === 'object') {\n offsetCh = offsetLine.ch;\n offsetLine = offsetLine.line;\n }\n return Pos(cur.line + offsetLine, cur.ch + offsetCh);\n }\n function getOffset(anchor, head) {\n return {\n line: head.line - anchor.line,\n ch: head.line - anchor.line\n };\n }\n function commandMatches(keys, keyMap, context, inputState) {\n // Partial matches are not applied. They inform the key handler\n // that the current key sequence is a subsequence of a valid key\n // sequence, so that the key buffer is not cleared.\n var match, partial = [], full = [];\n for (var i = 0; i < keyMap.length; i++) {\n var command = keyMap[i];\n if (context == 'insert' && command.context != 'insert' ||\n command.context && command.context != context ||\n inputState.operator && command.type == 'action' ||\n !(match = commandMatch(keys, command.keys))) { continue; }\n if (match == 'partial') { partial.push(command); }\n if (match == 'full') { full.push(command); }\n }\n return {\n partial: partial.length && partial,\n full: full.length && full\n };\n }\n function commandMatch(pressed, mapped) {\n if (mapped.slice(-11) == '<character>') {\n // Last character matches anything.\n var prefixLen = mapped.length - 11;\n var pressedPrefix = pressed.slice(0, prefixLen);\n var mappedPrefix = mapped.slice(0, prefixLen);\n return pressedPrefix == mappedPrefix && pressed.length > prefixLen ? 'full' :\n mappedPrefix.indexOf(pressedPrefix) == 0 ? 'partial' : false;\n } else {\n return pressed == mapped ? 'full' :\n mapped.indexOf(pressed) == 0 ? 'partial' : false;\n }\n }\n function lastChar(keys) {\n var match = /^.*(<[\\w\\-]+>)$/.exec(keys);\n var selectedCharacter = match ? match[1] : keys.slice(-1);\n if (selectedCharacter.length > 1){\n switch(selectedCharacter){\n case '<CR>':\n selectedCharacter='\\n';\n break;\n case '<Space>':\n selectedCharacter=' ';\n break;\n default:\n break;\n }\n }\n return selectedCharacter;\n }\n function repeatFn(cm, fn, repeat) {\n return function() {\n for (var i = 0; i < repeat; i++) {\n fn(cm);\n }\n };\n }\n function copyCursor(cur) {\n return Pos(cur.line, cur.ch);\n }\n function cursorEqual(cur1, cur2) {\n return cur1.ch == cur2.ch && cur1.line == cur2.line;\n }\n function cursorIsBefore(cur1, cur2) {\n if (cur1.line < cur2.line) {\n return true;\n }\n if (cur1.line == cur2.line && cur1.ch < cur2.ch) {\n return true;\n }\n return false;\n }\n function cursorMin(cur1, cur2) {\n if (arguments.length > 2) {\n cur2 = cursorMin.apply(undefined, Array.prototype.slice.call(arguments, 1));\n }\n return cursorIsBefore(cur1, cur2) ? cur1 : cur2;\n }\n function cursorMax(cur1, cur2) {\n if (arguments.length > 2) {\n cur2 = cursorMax.apply(undefined, Array.prototype.slice.call(arguments, 1));\n }\n return cursorIsBefore(cur1, cur2) ? cur2 : cur1;\n }\n function cursorIsBetween(cur1, cur2, cur3) {\n // returns true if cur2 is between cur1 and cur3.\n var cur1before2 = cursorIsBefore(cur1, cur2);\n var cur2before3 = cursorIsBefore(cur2, cur3);\n return cur1before2 && cur2before3;\n }\n function lineLength(cm, lineNum) {\n return cm.getLine(lineNum).length;\n }\n function trim(s) {\n if (s.trim) {\n return s.trim();\n }\n return s.replace(/^\\s+|\\s+$/g, '');\n }\n function escapeRegex(s) {\n return s.replace(/([.?*+$\\[\\]\\/\\\\(){}|\\-])/g, '\\\\$1');\n }\n function extendLineToColumn(cm, lineNum, column) {\n var endCh = lineLength(cm, lineNum);\n var spaces = new Array(column-endCh+1).join(' ');\n cm.setCursor(Pos(lineNum, endCh));\n cm.replaceRange(spaces, cm.getCursor());\n }\n // This functions selects a rectangular block\n // of text with selectionEnd as any of its corner\n // Height of block:\n // Difference in selectionEnd.line and first/last selection.line\n // Width of the block:\n // Distance between selectionEnd.ch and any(first considered here) selection.ch\n function selectBlock(cm, selectionEnd) {\n var selections = [], ranges = cm.listSelections();\n var head = copyCursor(cm.clipPos(selectionEnd));\n var isClipped = !cursorEqual(selectionEnd, head);\n var curHead = cm.getCursor('head');\n var primIndex = getIndex(ranges, curHead);\n var wasClipped = cursorEqual(ranges[primIndex].head, ranges[primIndex].anchor);\n var max = ranges.length - 1;\n var index = max - primIndex > primIndex ? max : 0;\n var base = ranges[index].anchor;\n\n var firstLine = Math.min(base.line, head.line);\n var lastLine = Math.max(base.line, head.line);\n var baseCh = base.ch, headCh = head.ch;\n\n var dir = ranges[index].head.ch - baseCh;\n var newDir = headCh - baseCh;\n if (dir > 0 && newDir <= 0) {\n baseCh++;\n if (!isClipped) { headCh--; }\n } else if (dir < 0 && newDir >= 0) {\n baseCh--;\n if (!wasClipped) { headCh++; }\n } else if (dir < 0 && newDir == -1) {\n baseCh--;\n headCh++;\n }\n for (var line = firstLine; line <= lastLine; line++) {\n var range = {anchor: new Pos(line, baseCh), head: new Pos(line, headCh)};\n selections.push(range);\n }\n primIndex = head.line == lastLine ? selections.length - 1 : 0;\n cm.setSelections(selections);\n selectionEnd.ch = headCh;\n base.ch = baseCh;\n return base;\n }\n function selectForInsert(cm, head, height) {\n var sel = [];\n for (var i = 0; i < height; i++) {\n var lineHead = offsetCursor(head, i, 0);\n sel.push({anchor: lineHead, head: lineHead});\n }\n cm.setSelections(sel, 0);\n }\n // getIndex returns the index of the cursor in the selections.\n function getIndex(ranges, cursor, end) {\n for (var i = 0; i < ranges.length; i++) {\n var atAnchor = end != 'head' && cursorEqual(ranges[i].anchor, cursor);\n var atHead = end != 'anchor' && cursorEqual(ranges[i].head, cursor);\n if (atAnchor || atHead) {\n return i;\n }\n }\n return -1;\n }\n function getSelectedAreaRange(cm, vim) {\n var lastSelection = vim.lastSelection;\n var getCurrentSelectedAreaRange = function() {\n var selections = cm.listSelections();\n var start = selections[0];\n var end = selections[selections.length-1];\n var selectionStart = cursorIsBefore(start.anchor, start.head) ? start.anchor : start.head;\n var selectionEnd = cursorIsBefore(end.anchor, end.head) ? end.head : end.anchor;\n return [selectionStart, selectionEnd];\n };\n var getLastSelectedAreaRange = function() {\n var selectionStart = cm.getCursor();\n var selectionEnd = cm.getCursor();\n var block = lastSelection.visualBlock;\n if (block) {\n var width = block.width;\n var height = block.height;\n selectionEnd = Pos(selectionStart.line + height, selectionStart.ch + width);\n var selections = [];\n // selectBlock creates a 'proper' rectangular block.\n // We do not want that in all cases, so we manually set selections.\n for (var i = selectionStart.line; i < selectionEnd.line; i++) {\n var anchor = Pos(i, selectionStart.ch);\n var head = Pos(i, selectionEnd.ch);\n var range = {anchor: anchor, head: head};\n selections.push(range);\n }\n cm.setSelections(selections);\n } else {\n var start = lastSelection.anchorMark.find();\n var end = lastSelection.headMark.find();\n var line = end.line - start.line;\n var ch = end.ch - start.ch;\n selectionEnd = {line: selectionEnd.line + line, ch: line ? selectionEnd.ch : ch + selectionEnd.ch};\n if (lastSelection.visualLine) {\n selectionStart = Pos(selectionStart.line, 0);\n selectionEnd = Pos(selectionEnd.line, lineLength(cm, selectionEnd.line));\n }\n cm.setSelection(selectionStart, selectionEnd);\n }\n return [selectionStart, selectionEnd];\n };\n if (!vim.visualMode) {\n // In case of replaying the action.\n return getLastSelectedAreaRange();\n } else {\n return getCurrentSelectedAreaRange();\n }\n }\n // Updates the previous selection with the current selection's values. This\n // should only be called in visual mode.\n function updateLastSelection(cm, vim) {\n var anchor = vim.sel.anchor;\n var head = vim.sel.head;\n // To accommodate the effect of lastPastedText in the last selection\n if (vim.lastPastedText) {\n head = cm.posFromIndex(cm.indexFromPos(anchor) + vim.lastPastedText.length);\n vim.lastPastedText = null;\n }\n vim.lastSelection = {'anchorMark': cm.setBookmark(anchor),\n 'headMark': cm.setBookmark(head),\n 'anchor': copyCursor(anchor),\n 'head': copyCursor(head),\n 'visualMode': vim.visualMode,\n 'visualLine': vim.visualLine,\n 'visualBlock': vim.visualBlock};\n }\n function expandSelection(cm, start, end) {\n var sel = cm.state.vim.sel;\n var head = sel.head;\n var anchor = sel.anchor;\n var tmp;\n if (cursorIsBefore(end, start)) {\n tmp = end;\n end = start;\n start = tmp;\n }\n if (cursorIsBefore(head, anchor)) {\n head = cursorMin(start, head);\n anchor = cursorMax(anchor, end);\n } else {\n anchor = cursorMin(start, anchor);\n head = cursorMax(head, end);\n head = offsetCursor(head, 0, -1);\n if (head.ch == -1 && head.line != cm.firstLine()) {\n head = Pos(head.line - 1, lineLength(cm, head.line - 1));\n }\n }\n return [anchor, head];\n }\n /**\n * Updates the CodeMirror selection to match the provided vim selection.\n * If no arguments are given, it uses the current vim selection state.\n */\n function updateCmSelection(cm, sel, mode) {\n var vim = cm.state.vim;\n sel = sel || vim.sel;\n var mode = mode ||\n vim.visualLine ? 'line' : vim.visualBlock ? 'block' : 'char';\n var cmSel = makeCmSelection(cm, sel, mode);\n cm.setSelections(cmSel.ranges, cmSel.primary);\n updateFakeCursor(cm);\n }\n function makeCmSelection(cm, sel, mode, exclusive) {\n var head = copyCursor(sel.head);\n var anchor = copyCursor(sel.anchor);\n if (mode == 'char') {\n var headOffset = !exclusive && !cursorIsBefore(sel.head, sel.anchor) ? 1 : 0;\n var anchorOffset = cursorIsBefore(sel.head, sel.anchor) ? 1 : 0;\n head = offsetCursor(sel.head, 0, headOffset);\n anchor = offsetCursor(sel.anchor, 0, anchorOffset);\n return {\n ranges: [{anchor: anchor, head: head}],\n primary: 0\n };\n } else if (mode == 'line') {\n if (!cursorIsBefore(sel.head, sel.anchor)) {\n anchor.ch = 0;\n\n var lastLine = cm.lastLine();\n if (head.line > lastLine) {\n head.line = lastLine;\n }\n head.ch = lineLength(cm, head.line);\n } else {\n head.ch = 0;\n anchor.ch = lineLength(cm, anchor.line);\n }\n return {\n ranges: [{anchor: anchor, head: head}],\n primary: 0\n };\n } else if (mode == 'block') {\n var top = Math.min(anchor.line, head.line),\n left = Math.min(anchor.ch, head.ch),\n bottom = Math.max(anchor.line, head.line),\n right = Math.max(anchor.ch, head.ch) + 1;\n var height = bottom - top + 1;\n var primary = head.line == top ? 0 : height - 1;\n var ranges = [];\n for (var i = 0; i < height; i++) {\n ranges.push({\n anchor: Pos(top + i, left),\n head: Pos(top + i, right)\n });\n }\n return {\n ranges: ranges,\n primary: primary\n };\n }\n }\n function getHead(cm) {\n var cur = cm.getCursor('head');\n if (cm.getSelection().length == 1) {\n // Small corner case when only 1 character is selected. The \"real\"\n // head is the left of head and anchor.\n cur = cursorMin(cur, cm.getCursor('anchor'));\n }\n return cur;\n }\n\n /**\n * If moveHead is set to false, the CodeMirror selection will not be\n * touched. The caller assumes the responsibility of putting the cursor\n * in the right place.\n */\n function exitVisualMode(cm, moveHead) {\n var vim = cm.state.vim;\n if (moveHead !== false) {\n cm.setCursor(clipCursorToContent(cm, vim.sel.head));\n }\n updateLastSelection(cm, vim);\n vim.visualMode = false;\n vim.visualLine = false;\n vim.visualBlock = false;\n CodeMirror.signal(cm, \"vim-mode-change\", {mode: \"normal\"});\n if (vim.fakeCursor) {\n vim.fakeCursor.clear();\n }\n }\n\n // Remove any trailing newlines from the selection. For\n // example, with the caret at the start of the last word on the line,\n // 'dw' should word, but not the newline, while 'w' should advance the\n // caret to the first character of the next line.\n function clipToLine(cm, curStart, curEnd) {\n var selection = cm.getRange(curStart, curEnd);\n // Only clip if the selection ends with trailing newline + whitespace\n if (/\\n\\s*$/.test(selection)) {\n var lines = selection.split('\\n');\n // We know this is all whitepsace.\n lines.pop();\n\n // Cases:\n // 1. Last word is an empty line - do not clip the trailing '\\n'\n // 2. Last word is not an empty line - clip the trailing '\\n'\n var line;\n // Find the line containing the last word, and clip all whitespace up\n // to it.\n for (var line = lines.pop(); lines.length > 0 && line && isWhiteSpaceString(line); line = lines.pop()) {\n curEnd.line--;\n curEnd.ch = 0;\n }\n // If the last word is not an empty line, clip an additional newline\n if (line) {\n curEnd.line--;\n curEnd.ch = lineLength(cm, curEnd.line);\n } else {\n curEnd.ch = 0;\n }\n }\n }\n\n // Expand the selection to line ends.\n function expandSelectionToLine(_cm, curStart, curEnd) {\n curStart.ch = 0;\n curEnd.ch = 0;\n curEnd.line++;\n }\n\n function findFirstNonWhiteSpaceCharacter(text) {\n if (!text) {\n return 0;\n }\n var firstNonWS = text.search(/\\S/);\n return firstNonWS == -1 ? text.length : firstNonWS;\n }\n\n function expandWordUnderCursor(cm, inclusive, _forward, bigWord, noSymbol) {\n var cur = getHead(cm);\n var line = cm.getLine(cur.line);\n var idx = cur.ch;\n\n // Seek to first word or non-whitespace character, depending on if\n // noSymbol is true.\n var test = noSymbol ? wordCharTest[0] : bigWordCharTest [0];\n while (!test(line.charAt(idx))) {\n idx++;\n if (idx >= line.length) { return null; }\n }\n\n if (bigWord) {\n test = bigWordCharTest[0];\n } else {\n test = wordCharTest[0];\n if (!test(line.charAt(idx))) {\n test = wordCharTest[1];\n }\n }\n\n var end = idx, start = idx;\n while (test(line.charAt(end)) && end < line.length) { end++; }\n while (test(line.charAt(start)) && start >= 0) { start--; }\n start++;\n\n if (inclusive) {\n // If present, include all whitespace after word.\n // Otherwise, include all whitespace before word, except indentation.\n var wordEnd = end;\n while (/\\s/.test(line.charAt(end)) && end < line.length) { end++; }\n if (wordEnd == end) {\n var wordStart = start;\n while (/\\s/.test(line.charAt(start - 1)) && start > 0) { start--; }\n if (!start) { start = wordStart; }\n }\n }\n return { start: Pos(cur.line, start), end: Pos(cur.line, end) };\n }\n\n function recordJumpPosition(cm, oldCur, newCur) {\n if (!cursorEqual(oldCur, newCur)) {\n vimGlobalState.jumpList.add(cm, oldCur, newCur);\n }\n }\n\n function recordLastCharacterSearch(increment, args) {\n vimGlobalState.lastChararacterSearch.increment = increment;\n vimGlobalState.lastChararacterSearch.forward = args.forward;\n vimGlobalState.lastChararacterSearch.selectedCharacter = args.selectedCharacter;\n }\n\n var symbolToMode = {\n '(': 'bracket', ')': 'bracket', '{': 'bracket', '}': 'bracket',\n '[': 'section', ']': 'section',\n '*': 'comment', '/': 'comment',\n 'm': 'method', 'M': 'method',\n '#': 'preprocess'\n };\n var findSymbolModes = {\n bracket: {\n isComplete: function(state) {\n if (state.nextCh === state.symb) {\n state.depth++;\n if (state.depth >= 1)return true;\n } else if (state.nextCh === state.reverseSymb) {\n state.depth--;\n }\n return false;\n }\n },\n section: {\n init: function(state) {\n state.curMoveThrough = true;\n state.symb = (state.forward ? ']' : '[') === state.symb ? '{' : '}';\n },\n isComplete: function(state) {\n return state.index === 0 && state.nextCh === state.symb;\n }\n },\n comment: {\n isComplete: function(state) {\n var found = state.lastCh === '*' && state.nextCh === '/';\n state.lastCh = state.nextCh;\n return found;\n }\n },\n // TODO: The original Vim implementation only operates on level 1 and 2.\n // The current implementation doesn't check for code block level and\n // therefore it operates on any levels.\n method: {\n init: function(state) {\n state.symb = (state.symb === 'm' ? '{' : '}');\n state.reverseSymb = state.symb === '{' ? '}' : '{';\n },\n isComplete: function(state) {\n if (state.nextCh === state.symb)return true;\n return false;\n }\n },\n preprocess: {\n init: function(state) {\n state.index = 0;\n },\n isComplete: function(state) {\n if (state.nextCh === '#') {\n var token = state.lineText.match(/#(\\w+)/)[1];\n if (token === 'endif') {\n if (state.forward && state.depth === 0) {\n return true;\n }\n state.depth++;\n } else if (token === 'if') {\n if (!state.forward && state.depth === 0) {\n return true;\n }\n state.depth--;\n }\n if (token === 'else' && state.depth === 0)return true;\n }\n return false;\n }\n }\n };\n function findSymbol(cm, repeat, forward, symb) {\n var cur = copyCursor(cm.getCursor());\n var increment = forward ? 1 : -1;\n var endLine = forward ? cm.lineCount() : -1;\n var curCh = cur.ch;\n var line = cur.line;\n var lineText = cm.getLine(line);\n var state = {\n lineText: lineText,\n nextCh: lineText.charAt(curCh),\n lastCh: null,\n index: curCh,\n symb: symb,\n reverseSymb: (forward ? { ')': '(', '}': '{' } : { '(': ')', '{': '}' })[symb],\n forward: forward,\n depth: 0,\n curMoveThrough: false\n };\n var mode = symbolToMode[symb];\n if (!mode)return cur;\n var init = findSymbolModes[mode].init;\n var isComplete = findSymbolModes[mode].isComplete;\n if (init) { init(state); }\n while (line !== endLine && repeat) {\n state.index += increment;\n state.nextCh = state.lineText.charAt(state.index);\n if (!state.nextCh) {\n line += increment;\n state.lineText = cm.getLine(line) || '';\n if (increment > 0) {\n state.index = 0;\n } else {\n var lineLen = state.lineText.length;\n state.index = (lineLen > 0) ? (lineLen-1) : 0;\n }\n state.nextCh = state.lineText.charAt(state.index);\n }\n if (isComplete(state)) {\n cur.line = line;\n cur.ch = state.index;\n repeat--;\n }\n }\n if (state.nextCh || state.curMoveThrough) {\n return Pos(line, state.index);\n }\n return cur;\n }\n\n /**\n * Returns the boundaries of the next word. If the cursor in the middle of\n * the word, then returns the boundaries of the current word, starting at\n * the cursor. If the cursor is at the start/end of a word, and we are going\n * forward/backward, respectively, find the boundaries of the next word.\n *\n * @param {CodeMirror} cm CodeMirror object.\n * @param {Cursor} cur The cursor position.\n * @param {boolean} forward True to search forward. False to search\n * backward.\n * @param {boolean} bigWord True if punctuation count as part of the word.\n * False if only [a-zA-Z0-9] characters count as part of the word.\n * @param {boolean} emptyLineIsWord True if empty lines should be treated\n * as words.\n * @return {Object{from:number, to:number, line: number}} The boundaries of\n * the word, or null if there are no more words.\n */\n function findWord(cm, cur, forward, bigWord, emptyLineIsWord) {\n var lineNum = cur.line;\n var pos = cur.ch;\n var line = cm.getLine(lineNum);\n var dir = forward ? 1 : -1;\n var charTests = bigWord ? bigWordCharTest: wordCharTest;\n\n if (emptyLineIsWord && line == '') {\n lineNum += dir;\n line = cm.getLine(lineNum);\n if (!isLine(cm, lineNum)) {\n return null;\n }\n pos = (forward) ? 0 : line.length;\n }\n\n while (true) {\n if (emptyLineIsWord && line == '') {\n return { from: 0, to: 0, line: lineNum };\n }\n var stop = (dir > 0) ? line.length : -1;\n var wordStart = stop, wordEnd = stop;\n // Find bounds of next word.\n while (pos != stop) {\n var foundWord = false;\n for (var i = 0; i < charTests.length && !foundWord; ++i) {\n if (charTests[i](line.charAt(pos))) {\n wordStart = pos;\n // Advance to end of word.\n while (pos != stop && charTests[i](line.charAt(pos))) {\n pos += dir;\n }\n wordEnd = pos;\n foundWord = wordStart != wordEnd;\n if (wordStart == cur.ch && lineNum == cur.line &&\n wordEnd == wordStart + dir) {\n // We started at the end of a word. Find the next one.\n continue;\n } else {\n return {\n from: Math.min(wordStart, wordEnd + 1),\n to: Math.max(wordStart, wordEnd),\n line: lineNum };\n }\n }\n }\n if (!foundWord) {\n pos += dir;\n }\n }\n // Advance to next/prev line.\n lineNum += dir;\n if (!isLine(cm, lineNum)) {\n return null;\n }\n line = cm.getLine(lineNum);\n pos = (dir > 0) ? 0 : line.length;\n }\n // Should never get here.\n throw new Error('The impossible happened.');\n }\n\n /**\n * @param {CodeMirror} cm CodeMirror object.\n * @param {Pos} cur The position to start from.\n * @param {int} repeat Number of words to move past.\n * @param {boolean} forward True to search forward. False to search\n * backward.\n * @param {boolean} wordEnd True to move to end of word. False to move to\n * beginning of word.\n * @param {boolean} bigWord True if punctuation count as part of the word.\n * False if only alphabet characters count as part of the word.\n * @return {Cursor} The position the cursor should move to.\n */\n function moveToWord(cm, cur, repeat, forward, wordEnd, bigWord) {\n var curStart = copyCursor(cur);\n var words = [];\n if (forward && !wordEnd || !forward && wordEnd) {\n repeat++;\n }\n // For 'e', empty lines are not considered words, go figure.\n var emptyLineIsWord = !(forward && wordEnd);\n for (var i = 0; i < repeat; i++) {\n var word = findWord(cm, cur, forward, bigWord, emptyLineIsWord);\n if (!word) {\n var eodCh = lineLength(cm, cm.lastLine());\n words.push(forward\n ? {line: cm.lastLine(), from: eodCh, to: eodCh}\n : {line: 0, from: 0, to: 0});\n break;\n }\n words.push(word);\n cur = Pos(word.line, forward ? (word.to - 1) : word.from);\n }\n var shortCircuit = words.length != repeat;\n var firstWord = words[0];\n var lastWord = words.pop();\n if (forward && !wordEnd) {\n // w\n if (!shortCircuit && (firstWord.from != curStart.ch || firstWord.line != curStart.line)) {\n // We did not start in the middle of a word. Discard the extra word at the end.\n lastWord = words.pop();\n }\n return Pos(lastWord.line, lastWord.from);\n } else if (forward && wordEnd) {\n return Pos(lastWord.line, lastWord.to - 1);\n } else if (!forward && wordEnd) {\n // ge\n if (!shortCircuit && (firstWord.to != curStart.ch || firstWord.line != curStart.line)) {\n // We did not start in the middle of a word. Discard the extra word at the end.\n lastWord = words.pop();\n }\n return Pos(lastWord.line, lastWord.to);\n } else {\n // b\n return Pos(lastWord.line, lastWord.from);\n }\n }\n\n function moveToCharacter(cm, repeat, forward, character) {\n var cur = cm.getCursor();\n var start = cur.ch;\n var idx;\n for (var i = 0; i < repeat; i ++) {\n var line = cm.getLine(cur.line);\n idx = charIdxInLine(start, line, character, forward, true);\n if (idx == -1) {\n return null;\n }\n start = idx;\n }\n return Pos(cm.getCursor().line, idx);\n }\n\n function moveToColumn(cm, repeat) {\n // repeat is always >= 1, so repeat - 1 always corresponds\n // to the column we want to go to.\n var line = cm.getCursor().line;\n return clipCursorToContent(cm, Pos(line, repeat - 1));\n }\n\n function updateMark(cm, vim, markName, pos) {\n if (!inArray(markName, validMarks)) {\n return;\n }\n if (vim.marks[markName]) {\n vim.marks[markName].clear();\n }\n vim.marks[markName] = cm.setBookmark(pos);\n }\n\n function charIdxInLine(start, line, character, forward, includeChar) {\n // Search for char in line.\n // motion_options: {forward, includeChar}\n // If includeChar = true, include it too.\n // If forward = true, search forward, else search backwards.\n // If char is not found on this line, do nothing\n var idx;\n if (forward) {\n idx = line.indexOf(character, start + 1);\n if (idx != -1 && !includeChar) {\n idx -= 1;\n }\n } else {\n idx = line.lastIndexOf(character, start - 1);\n if (idx != -1 && !includeChar) {\n idx += 1;\n }\n }\n return idx;\n }\n\n function findParagraph(cm, head, repeat, dir, inclusive) {\n var line = head.line;\n var min = cm.firstLine();\n var max = cm.lastLine();\n var start, end, i = line;\n function isEmpty(i) { return !cm.getLine(i); }\n function isBoundary(i, dir, any) {\n if (any) { return isEmpty(i) != isEmpty(i + dir); }\n return !isEmpty(i) && isEmpty(i + dir);\n }\n if (dir) {\n while (min <= i && i <= max && repeat > 0) {\n if (isBoundary(i, dir)) { repeat--; }\n i += dir;\n }\n return new Pos(i, 0);\n }\n\n var vim = cm.state.vim;\n if (vim.visualLine && isBoundary(line, 1, true)) {\n var anchor = vim.sel.anchor;\n if (isBoundary(anchor.line, -1, true)) {\n if (!inclusive || anchor.line != line) {\n line += 1;\n }\n }\n }\n var startState = isEmpty(line);\n for (i = line; i <= max && repeat; i++) {\n if (isBoundary(i, 1, true)) {\n if (!inclusive || isEmpty(i) != startState) {\n repeat--;\n }\n }\n }\n end = new Pos(i, 0);\n // select boundary before paragraph for the last one\n if (i > max && !startState) { startState = true; }\n else { inclusive = false; }\n for (i = line; i > min; i--) {\n if (!inclusive || isEmpty(i) == startState || i == line) {\n if (isBoundary(i, -1, true)) { break; }\n }\n }\n start = new Pos(i, 0);\n return { start: start, end: end };\n }\n\n // TODO: perhaps this finagling of start and end positions belonds\n // in codmirror/replaceRange?\n function selectCompanionObject(cm, head, symb, inclusive) {\n var cur = head, start, end;\n\n var bracketRegexp = ({\n '(': /[()]/, ')': /[()]/,\n '[': /[[\\]]/, ']': /[[\\]]/,\n '{': /[{}]/, '}': /[{}]/})[symb];\n var openSym = ({\n '(': '(', ')': '(',\n '[': '[', ']': '[',\n '{': '{', '}': '{'})[symb];\n var curChar = cm.getLine(cur.line).charAt(cur.ch);\n // Due to the behavior of scanForBracket, we need to add an offset if the\n // cursor is on a matching open bracket.\n var offset = curChar === openSym ? 1 : 0;\n\n start = cm.scanForBracket(Pos(cur.line, cur.ch + offset), -1, null, {'bracketRegex': bracketRegexp});\n end = cm.scanForBracket(Pos(cur.line, cur.ch + offset), 1, null, {'bracketRegex': bracketRegexp});\n\n if (!start || !end) {\n return { start: cur, end: cur };\n }\n\n start = start.pos;\n end = end.pos;\n\n if ((start.line == end.line && start.ch > end.ch)\n || (start.line > end.line)) {\n var tmp = start;\n start = end;\n end = tmp;\n }\n\n if (inclusive) {\n end.ch += 1;\n } else {\n start.ch += 1;\n }\n\n return { start: start, end: end };\n }\n\n // Takes in a symbol and a cursor and tries to simulate text objects that\n // have identical opening and closing symbols\n // TODO support across multiple lines\n function findBeginningAndEnd(cm, head, symb, inclusive) {\n var cur = copyCursor(head);\n var line = cm.getLine(cur.line);\n var chars = line.split('');\n var start, end, i, len;\n var firstIndex = chars.indexOf(symb);\n\n // the decision tree is to always look backwards for the beginning first,\n // but if the cursor is in front of the first instance of the symb,\n // then move the cursor forward\n if (cur.ch < firstIndex) {\n cur.ch = firstIndex;\n // Why is this line even here???\n // cm.setCursor(cur.line, firstIndex+1);\n }\n // otherwise if the cursor is currently on the closing symbol\n else if (firstIndex < cur.ch && chars[cur.ch] == symb) {\n end = cur.ch; // assign end to the current cursor\n --cur.ch; // make sure to look backwards\n }\n\n // if we're currently on the symbol, we've got a start\n if (chars[cur.ch] == symb && !end) {\n start = cur.ch + 1; // assign start to ahead of the cursor\n } else {\n // go backwards to find the start\n for (i = cur.ch; i > -1 && !start; i--) {\n if (chars[i] == symb) {\n start = i + 1;\n }\n }\n }\n\n // look forwards for the end symbol\n if (start && !end) {\n for (i = start, len = chars.length; i < len && !end; i++) {\n if (chars[i] == symb) {\n end = i;\n }\n }\n }\n\n // nothing found\n if (!start || !end) {\n return { start: cur, end: cur };\n }\n\n // include the symbols\n if (inclusive) {\n --start; ++end;\n }\n\n return {\n start: Pos(cur.line, start),\n end: Pos(cur.line, end)\n };\n }\n\n // Search functions\n defineOption('pcre', true, 'boolean');\n function SearchState() {}\n SearchState.prototype = {\n getQuery: function() {\n return vimGlobalState.query;\n },\n setQuery: function(query) {\n vimGlobalState.query = query;\n },\n getOverlay: function() {\n return this.searchOverlay;\n },\n setOverlay: function(overlay) {\n this.searchOverlay = overlay;\n },\n isReversed: function() {\n return vimGlobalState.isReversed;\n },\n setReversed: function(reversed) {\n vimGlobalState.isReversed = reversed;\n },\n getScrollbarAnnotate: function() {\n return this.annotate;\n },\n setScrollbarAnnotate: function(annotate) {\n this.annotate = annotate;\n }\n };\n function getSearchState(cm) {\n var vim = cm.state.vim;\n return vim.searchState_ || (vim.searchState_ = new SearchState());\n }\n function dialog(cm, template, shortText, onClose, options) {\n if (cm.openDialog) {\n cm.openDialog(template, onClose, { bottom: true, value: options.value,\n onKeyDown: options.onKeyDown, onKeyUp: options.onKeyUp,\n selectValueOnOpen: false});\n }\n else {\n onClose(prompt(shortText, ''));\n }\n }\n function splitBySlash(argString) {\n var slashes = findUnescapedSlashes(argString) || [];\n if (!slashes.length) return [];\n var tokens = [];\n // in case of strings like foo/bar\n if (slashes[0] !== 0) return;\n for (var i = 0; i < slashes.length; i++) {\n if (typeof slashes[i] == 'number')\n tokens.push(argString.substring(slashes[i] + 1, slashes[i+1]));\n }\n return tokens;\n }\n\n function findUnescapedSlashes(str) {\n var escapeNextChar = false;\n var slashes = [];\n for (var i = 0; i < str.length; i++) {\n var c = str.charAt(i);\n if (!escapeNextChar && c == '/') {\n slashes.push(i);\n }\n escapeNextChar = !escapeNextChar && (c == '\\\\');\n }\n return slashes;\n }\n\n // Translates a search string from ex (vim) syntax into javascript form.\n function translateRegex(str) {\n // When these match, add a '\\' if unescaped or remove one if escaped.\n var specials = '|(){';\n // Remove, but never add, a '\\' for these.\n var unescape = '}';\n var escapeNextChar = false;\n var out = [];\n for (var i = -1; i < str.length; i++) {\n var c = str.charAt(i) || '';\n var n = str.charAt(i+1) || '';\n var specialComesNext = (n && specials.indexOf(n) != -1);\n if (escapeNextChar) {\n if (c !== '\\\\' || !specialComesNext) {\n out.push(c);\n }\n escapeNextChar = false;\n } else {\n if (c === '\\\\') {\n escapeNextChar = true;\n // Treat the unescape list as special for removing, but not adding '\\'.\n if (n && unescape.indexOf(n) != -1) {\n specialComesNext = true;\n }\n // Not passing this test means removing a '\\'.\n if (!specialComesNext || n === '\\\\') {\n out.push(c);\n }\n } else {\n out.push(c);\n if (specialComesNext && n !== '\\\\') {\n out.push('\\\\');\n }\n }\n }\n }\n return out.join('');\n }\n\n // Translates the replace part of a search and replace from ex (vim) syntax into\n // javascript form. Similar to translateRegex, but additionally fixes back references\n // (translates '\\[0..9]' to '$[0..9]') and follows different rules for escaping '$'.\n var charUnescapes = {'\\\\n': '\\n', '\\\\r': '\\r', '\\\\t': '\\t'};\n function translateRegexReplace(str) {\n var escapeNextChar = false;\n var out = [];\n for (var i = -1; i < str.length; i++) {\n var c = str.charAt(i) || '';\n var n = str.charAt(i+1) || '';\n if (charUnescapes[c + n]) {\n out.push(charUnescapes[c+n]);\n i++;\n } else if (escapeNextChar) {\n // At any point in the loop, escapeNextChar is true if the previous\n // character was a '\\' and was not escaped.\n out.push(c);\n escapeNextChar = false;\n } else {\n if (c === '\\\\') {\n escapeNextChar = true;\n if ((isNumber(n) || n === '$')) {\n out.push('$');\n } else if (n !== '/' && n !== '\\\\') {\n out.push('\\\\');\n }\n } else {\n if (c === '$') {\n out.push('$');\n }\n out.push(c);\n if (n === '/') {\n out.push('\\\\');\n }\n }\n }\n }\n return out.join('');\n }\n\n // Unescape \\ and / in the replace part, for PCRE mode.\n var unescapes = {'\\\\/': '/', '\\\\\\\\': '\\\\', '\\\\n': '\\n', '\\\\r': '\\r', '\\\\t': '\\t'};\n function unescapeRegexReplace(str) {\n var stream = new CodeMirror.StringStream(str);\n var output = [];\n while (!stream.eol()) {\n // Search for \\.\n while (stream.peek() && stream.peek() != '\\\\') {\n output.push(stream.next());\n }\n var matched = false;\n for (var matcher in unescapes) {\n if (stream.match(matcher, true)) {\n matched = true;\n output.push(unescapes[matcher]);\n break;\n }\n }\n if (!matched) {\n // Don't change anything\n output.push(stream.next());\n }\n }\n return output.join('');\n }\n\n /**\n * Extract the regular expression from the query and return a Regexp object.\n * Returns null if the query is blank.\n * If ignoreCase is passed in, the Regexp object will have the 'i' flag set.\n * If smartCase is passed in, and the query contains upper case letters,\n * then ignoreCase is overridden, and the 'i' flag will not be set.\n * If the query contains the /i in the flag part of the regular expression,\n * then both ignoreCase and smartCase are ignored, and 'i' will be passed\n * through to the Regex object.\n */\n function parseQuery(query, ignoreCase, smartCase) {\n // First update the last search register\n var lastSearchRegister = vimGlobalState.registerController.getRegister('/');\n lastSearchRegister.setText(query);\n // Check if the query is already a regex.\n if (query instanceof RegExp) { return query; }\n // First try to extract regex + flags from the input. If no flags found,\n // extract just the regex. IE does not accept flags directly defined in\n // the regex string in the form /regex/flags\n var slashes = findUnescapedSlashes(query);\n var regexPart;\n var forceIgnoreCase;\n if (!slashes.length) {\n // Query looks like 'regexp'\n regexPart = query;\n } else {\n // Query looks like 'regexp/...'\n regexPart = query.substring(0, slashes[0]);\n var flagsPart = query.substring(slashes[0]);\n forceIgnoreCase = (flagsPart.indexOf('i') != -1);\n }\n if (!regexPart) {\n return null;\n }\n if (!getOption('pcre')) {\n regexPart = translateRegex(regexPart);\n }\n if (smartCase) {\n ignoreCase = (/^[^A-Z]*$/).test(regexPart);\n }\n var regexp = new RegExp(regexPart,\n (ignoreCase || forceIgnoreCase) ? 'i' : undefined);\n return regexp;\n }\n function showConfirm(cm, text) {\n if (cm.openNotification) {\n cm.openNotification('<span style=\"color: red\">' + text + '</span>',\n {bottom: true, duration: 5000});\n } else {\n alert(text);\n }\n }\n function makePrompt(prefix, desc) {\n var raw = '';\n if (prefix) {\n raw += '<span style=\"font-family: monospace\">' + prefix + '</span>';\n }\n raw += '<input type=\"text\"/> ' +\n '<span style=\"color: #888\">';\n if (desc) {\n raw += '<span style=\"color: #888\">';\n raw += desc;\n raw += '</span>';\n }\n return raw;\n }\n var searchPromptDesc = '(Javascript regexp)';\n function showPrompt(cm, options) {\n var shortText = (options.prefix || '') + ' ' + (options.desc || '');\n var prompt = makePrompt(options.prefix, options.desc);\n dialog(cm, prompt, shortText, options.onClose, options);\n }\n function regexEqual(r1, r2) {\n if (r1 instanceof RegExp && r2 instanceof RegExp) {\n var props = ['global', 'multiline', 'ignoreCase', 'source'];\n for (var i = 0; i < props.length; i++) {\n var prop = props[i];\n if (r1[prop] !== r2[prop]) {\n return false;\n }\n }\n return true;\n }\n return false;\n }\n // Returns true if the query is valid.\n function updateSearchQuery(cm, rawQuery, ignoreCase, smartCase) {\n if (!rawQuery) {\n return;\n }\n var state = getSearchState(cm);\n var query = parseQuery(rawQuery, !!ignoreCase, !!smartCase);\n if (!query) {\n return;\n }\n highlightSearchMatches(cm, query);\n if (regexEqual(query, state.getQuery())) {\n return query;\n }\n state.setQuery(query);\n return query;\n }\n function searchOverlay(query) {\n if (query.source.charAt(0) == '^') {\n var matchSol = true;\n }\n return {\n token: function(stream) {\n if (matchSol && !stream.sol()) {\n stream.skipToEnd();\n return;\n }\n var match = stream.match(query, false);\n if (match) {\n if (match[0].length == 0) {\n // Matched empty string, skip to next.\n stream.next();\n return 'searching';\n }\n if (!stream.sol()) {\n // Backtrack 1 to match \\b\n stream.backUp(1);\n if (!query.exec(stream.next() + match[0])) {\n stream.next();\n return null;\n }\n }\n stream.match(query);\n return 'searching';\n }\n while (!stream.eol()) {\n stream.next();\n if (stream.match(query, false)) break;\n }\n },\n query: query\n };\n }\n function highlightSearchMatches(cm, query) {\n var searchState = getSearchState(cm);\n var overlay = searchState.getOverlay();\n if (!overlay || query != overlay.query) {\n if (overlay) {\n cm.removeOverlay(overlay);\n }\n overlay = searchOverlay(query);\n cm.addOverlay(overlay);\n if (cm.showMatchesOnScrollbar) {\n if (searchState.getScrollbarAnnotate()) {\n searchState.getScrollbarAnnotate().clear();\n }\n searchState.setScrollbarAnnotate(cm.showMatchesOnScrollbar(query));\n }\n searchState.setOverlay(overlay);\n }\n }\n function findNext(cm, prev, query, repeat) {\n if (repeat === undefined) { repeat = 1; }\n return cm.operation(function() {\n var pos = cm.getCursor();\n var cursor = cm.getSearchCursor(query, pos);\n for (var i = 0; i < repeat; i++) {\n var found = cursor.find(prev);\n if (i == 0 && found && cursorEqual(cursor.from(), pos)) { found = cursor.find(prev); }\n if (!found) {\n // SearchCursor may have returned null because it hit EOF, wrap\n // around and try again.\n cursor = cm.getSearchCursor(query,\n (prev) ? Pos(cm.lastLine()) : Pos(cm.firstLine(), 0) );\n if (!cursor.find(prev)) {\n return;\n }\n }\n }\n return cursor.from();\n });\n }\n function clearSearchHighlight(cm) {\n var state = getSearchState(cm);\n cm.removeOverlay(getSearchState(cm).getOverlay());\n state.setOverlay(null);\n if (state.getScrollbarAnnotate()) {\n state.getScrollbarAnnotate().clear();\n state.setScrollbarAnnotate(null);\n }\n }\n /**\n * Check if pos is in the specified range, INCLUSIVE.\n * Range can be specified with 1 or 2 arguments.\n * If the first range argument is an array, treat it as an array of line\n * numbers. Match pos against any of the lines.\n * If the first range argument is a number,\n * if there is only 1 range argument, check if pos has the same line\n * number\n * if there are 2 range arguments, then check if pos is in between the two\n * range arguments.\n */\n function isInRange(pos, start, end) {\n if (typeof pos != 'number') {\n // Assume it is a cursor position. Get the line number.\n pos = pos.line;\n }\n if (start instanceof Array) {\n return inArray(pos, start);\n } else {\n if (end) {\n return (pos >= start && pos <= end);\n } else {\n return pos == start;\n }\n }\n }\n function getUserVisibleLines(cm) {\n var scrollInfo = cm.getScrollInfo();\n var occludeToleranceTop = 6;\n var occludeToleranceBottom = 10;\n var from = cm.coordsChar({left:0, top: occludeToleranceTop + scrollInfo.top}, 'local');\n var bottomY = scrollInfo.clientHeight - occludeToleranceBottom + scrollInfo.top;\n var to = cm.coordsChar({left:0, top: bottomY}, 'local');\n return {top: from.line, bottom: to.line};\n }\n\n var ExCommandDispatcher = function() {\n this.buildCommandMap_();\n };\n ExCommandDispatcher.prototype = {\n processCommand: function(cm, input, opt_params) {\n var that = this;\n cm.operation(function () {\n cm.curOp.isVimOp = true;\n that._processCommand(cm, input, opt_params);\n });\n },\n _processCommand: function(cm, input, opt_params) {\n var vim = cm.state.vim;\n var commandHistoryRegister = vimGlobalState.registerController.getRegister(':');\n var previousCommand = commandHistoryRegister.toString();\n if (vim.visualMode) {\n exitVisualMode(cm);\n }\n var inputStream = new CodeMirror.StringStream(input);\n // update \": with the latest command whether valid or invalid\n commandHistoryRegister.setText(input);\n var params = opt_params || {};\n params.input = input;\n try {\n this.parseInput_(cm, inputStream, params);\n } catch(e) {\n showConfirm(cm, e);\n throw e;\n }\n var command;\n var commandName;\n if (!params.commandName) {\n // If only a line range is defined, move to the line.\n if (params.line !== undefined) {\n commandName = 'move';\n }\n } else {\n command = this.matchCommand_(params.commandName);\n if (command) {\n commandName = command.name;\n if (command.excludeFromCommandHistory) {\n commandHistoryRegister.setText(previousCommand);\n }\n this.parseCommandArgs_(inputStream, params, command);\n if (command.type == 'exToKey') {\n // Handle Ex to Key mapping.\n for (var i = 0; i < command.toKeys.length; i++) {\n CodeMirror.Vim.handleKey(cm, command.toKeys[i], 'mapping');\n }\n return;\n } else if (command.type == 'exToEx') {\n // Handle Ex to Ex mapping.\n this.processCommand(cm, command.toInput);\n return;\n }\n }\n }\n if (!commandName) {\n showConfirm(cm, 'Not an editor command \":' + input + '\"');\n return;\n }\n try {\n exCommands[commandName](cm, params);\n // Possibly asynchronous commands (e.g. substitute, which might have a\n // user confirmation), are responsible for calling the callback when\n // done. All others have it taken care of for them here.\n if ((!command || !command.possiblyAsync) && params.callback) {\n params.callback();\n }\n } catch(e) {\n showConfirm(cm, e);\n throw e;\n }\n },\n parseInput_: function(cm, inputStream, result) {\n inputStream.eatWhile(':');\n // Parse range.\n if (inputStream.eat('%')) {\n result.line = cm.firstLine();\n result.lineEnd = cm.lastLine();\n } else {\n result.line = this.parseLineSpec_(cm, inputStream);\n if (result.line !== undefined && inputStream.eat(',')) {\n result.lineEnd = this.parseLineSpec_(cm, inputStream);\n }\n }\n\n // Parse command name.\n var commandMatch = inputStream.match(/^(\\w+)/);\n if (commandMatch) {\n result.commandName = commandMatch[1];\n } else {\n result.commandName = inputStream.match(/.*/)[0];\n }\n\n return result;\n },\n parseLineSpec_: function(cm, inputStream) {\n var numberMatch = inputStream.match(/^(\\d+)/);\n if (numberMatch) {\n return parseInt(numberMatch[1], 10) - 1;\n }\n switch (inputStream.next()) {\n case '.':\n return cm.getCursor().line;\n case '$':\n return cm.lastLine();\n case '\\'':\n var mark = cm.state.vim.marks[inputStream.next()];\n if (mark && mark.find()) {\n return mark.find().line;\n }\n throw new Error('Mark not set');\n default:\n inputStream.backUp(1);\n return undefined;\n }\n },\n parseCommandArgs_: function(inputStream, params, command) {\n if (inputStream.eol()) {\n return;\n }\n params.argString = inputStream.match(/.*/)[0];\n // Parse command-line arguments\n var delim = command.argDelimiter || /\\s+/;\n var args = trim(params.argString).split(delim);\n if (args.length && args[0]) {\n params.args = args;\n }\n },\n matchCommand_: function(commandName) {\n // Return the command in the command map that matches the shortest\n // prefix of the passed in command name. The match is guaranteed to be\n // unambiguous if the defaultExCommandMap's shortNames are set up\n // correctly. (see @code{defaultExCommandMap}).\n for (var i = commandName.length; i > 0; i--) {\n var prefix = commandName.substring(0, i);\n if (this.commandMap_[prefix]) {\n var command = this.commandMap_[prefix];\n if (command.name.indexOf(commandName) === 0) {\n return command;\n }\n }\n }\n return null;\n },\n buildCommandMap_: function() {\n this.commandMap_ = {};\n for (var i = 0; i < defaultExCommandMap.length; i++) {\n var command = defaultExCommandMap[i];\n var key = command.shortName || command.name;\n this.commandMap_[key] = command;\n }\n },\n map: function(lhs, rhs, ctx) {\n if (lhs != ':' && lhs.charAt(0) == ':') {\n if (ctx) { throw Error('Mode not supported for ex mappings'); }\n var commandName = lhs.substring(1);\n if (rhs != ':' && rhs.charAt(0) == ':') {\n // Ex to Ex mapping\n this.commandMap_[commandName] = {\n name: commandName,\n type: 'exToEx',\n toInput: rhs.substring(1),\n user: true\n };\n } else {\n // Ex to key mapping\n this.commandMap_[commandName] = {\n name: commandName,\n type: 'exToKey',\n toKeys: rhs,\n user: true\n };\n }\n } else {\n if (rhs != ':' && rhs.charAt(0) == ':') {\n // Key to Ex mapping.\n var mapping = {\n keys: lhs,\n type: 'keyToEx',\n exArgs: { input: rhs.substring(1) },\n user: true};\n if (ctx) { mapping.context = ctx; }\n defaultKeymap.unshift(mapping);\n } else {\n // Key to key mapping\n var mapping = {\n keys: lhs,\n type: 'keyToKey',\n toKeys: rhs,\n user: true\n };\n if (ctx) { mapping.context = ctx; }\n defaultKeymap.unshift(mapping);\n }\n }\n },\n unmap: function(lhs, ctx) {\n if (lhs != ':' && lhs.charAt(0) == ':') {\n // Ex to Ex or Ex to key mapping\n if (ctx) { throw Error('Mode not supported for ex mappings'); }\n var commandName = lhs.substring(1);\n if (this.commandMap_[commandName] && this.commandMap_[commandName].user) {\n delete this.commandMap_[commandName];\n return;\n }\n } else {\n // Key to Ex or key to key mapping\n var keys = lhs;\n for (var i = 0; i < defaultKeymap.length; i++) {\n if (keys == defaultKeymap[i].keys\n && defaultKeymap[i].context === ctx\n && defaultKeymap[i].user) {\n defaultKeymap.splice(i, 1);\n return;\n }\n }\n }\n throw Error('No such mapping.');\n }\n };\n\n var exCommands = {\n colorscheme: function(cm, params) {\n if (!params.args || params.args.length < 1) {\n showConfirm(cm, cm.getOption('theme'));\n return;\n }\n cm.setOption('theme', params.args[0]);\n },\n map: function(cm, params, ctx) {\n var mapArgs = params.args;\n if (!mapArgs || mapArgs.length < 2) {\n if (cm) {\n showConfirm(cm, 'Invalid mapping: ' + params.input);\n }\n return;\n }\n exCommandDispatcher.map(mapArgs[0], mapArgs[1], ctx);\n },\n imap: function(cm, params) { this.map(cm, params, 'insert'); },\n nmap: function(cm, params) { this.map(cm, params, 'normal'); },\n vmap: function(cm, params) { this.map(cm, params, 'visual'); },\n unmap: function(cm, params, ctx) {\n var mapArgs = params.args;\n if (!mapArgs || mapArgs.length < 1) {\n if (cm) {\n showConfirm(cm, 'No such mapping: ' + params.input);\n }\n return;\n }\n exCommandDispatcher.unmap(mapArgs[0], ctx);\n },\n move: function(cm, params) {\n commandDispatcher.processCommand(cm, cm.state.vim, {\n type: 'motion',\n motion: 'moveToLineOrEdgeOfDocument',\n motionArgs: { forward: false, explicitRepeat: true,\n linewise: true },\n repeatOverride: params.line+1});\n },\n set: function(cm, params) {\n var setArgs = params.args;\n // Options passed through to the setOption/getOption calls. May be passed in by the\n // local/global versions of the set command\n var setCfg = params.setCfg || {};\n if (!setArgs || setArgs.length < 1) {\n if (cm) {\n showConfirm(cm, 'Invalid mapping: ' + params.input);\n }\n return;\n }\n var expr = setArgs[0].split('=');\n var optionName = expr[0];\n var value = expr[1];\n var forceGet = false;\n\n if (optionName.charAt(optionName.length - 1) == '?') {\n // If post-fixed with ?, then the set is actually a get.\n if (value) { throw Error('Trailing characters: ' + params.argString); }\n optionName = optionName.substring(0, optionName.length - 1);\n forceGet = true;\n }\n if (value === undefined && optionName.substring(0, 2) == 'no') {\n // To set boolean options to false, the option name is prefixed with\n // 'no'.\n optionName = optionName.substring(2);\n value = false;\n }\n\n var optionIsBoolean = options[optionName] && options[optionName].type == 'boolean';\n if (optionIsBoolean && value == undefined) {\n // Calling set with a boolean option sets it to true.\n value = true;\n }\n // If no value is provided, then we assume this is a get.\n if (!optionIsBoolean && value === undefined || forceGet) {\n var oldValue = getOption(optionName, cm, setCfg);\n if (oldValue === true || oldValue === false) {\n showConfirm(cm, ' ' + (oldValue ? '' : 'no') + optionName);\n } else {\n showConfirm(cm, ' ' + optionName + '=' + oldValue);\n }\n } else {\n setOption(optionName, value, cm, setCfg);\n }\n },\n setlocal: function (cm, params) {\n // setCfg is passed through to setOption\n params.setCfg = {scope: 'local'};\n this.set(cm, params);\n },\n setglobal: function (cm, params) {\n // setCfg is passed through to setOption\n params.setCfg = {scope: 'global'};\n this.set(cm, params);\n },\n registers: function(cm, params) {\n var regArgs = params.args;\n var registers = vimGlobalState.registerController.registers;\n var regInfo = '----------Registers----------<br><br>';\n if (!regArgs) {\n for (var registerName in registers) {\n var text = registers[registerName].toString();\n if (text.length) {\n regInfo += '\"' + registerName + ' ' + text + '<br>';\n }\n }\n } else {\n var registerName;\n regArgs = regArgs.join('');\n for (var i = 0; i < regArgs.length; i++) {\n registerName = regArgs.charAt(i);\n if (!vimGlobalState.registerController.isValidRegister(registerName)) {\n continue;\n }\n var register = registers[registerName] || new Register();\n regInfo += '\"' + registerName + ' ' + register.toString() + '<br>';\n }\n }\n showConfirm(cm, regInfo);\n },\n sort: function(cm, params) {\n var reverse, ignoreCase, unique, number;\n function parseArgs() {\n if (params.argString) {\n var args = new CodeMirror.StringStream(params.argString);\n if (args.eat('!')) { reverse = true; }\n if (args.eol()) { return; }\n if (!args.eatSpace()) { return 'Invalid arguments'; }\n var opts = args.match(/[a-z]+/);\n if (opts) {\n opts = opts[0];\n ignoreCase = opts.indexOf('i') != -1;\n unique = opts.indexOf('u') != -1;\n var decimal = opts.indexOf('d') != -1 && 1;\n var hex = opts.indexOf('x') != -1 && 1;\n var octal = opts.indexOf('o') != -1 && 1;\n if (decimal + hex + octal > 1) { return 'Invalid arguments'; }\n number = decimal && 'decimal' || hex && 'hex' || octal && 'octal';\n }\n if (args.match(/\\/.*\\//)) { return 'patterns not supported'; }\n }\n }\n var err = parseArgs();\n if (err) {\n showConfirm(cm, err + ': ' + params.argString);\n return;\n }\n var lineStart = params.line || cm.firstLine();\n var lineEnd = params.lineEnd || params.line || cm.lastLine();\n if (lineStart == lineEnd) { return; }\n var curStart = Pos(lineStart, 0);\n var curEnd = Pos(lineEnd, lineLength(cm, lineEnd));\n var text = cm.getRange(curStart, curEnd).split('\\n');\n var numberRegex = (number == 'decimal') ? /(-?)([\\d]+)/ :\n (number == 'hex') ? /(-?)(?:0x)?([0-9a-f]+)/i :\n (number == 'octal') ? /([0-7]+)/ : null;\n var radix = (number == 'decimal') ? 10 : (number == 'hex') ? 16 : (number == 'octal') ? 8 : null;\n var numPart = [], textPart = [];\n if (number) {\n for (var i = 0; i < text.length; i++) {\n if (numberRegex.exec(text[i])) {\n numPart.push(text[i]);\n } else {\n textPart.push(text[i]);\n }\n }\n } else {\n textPart = text;\n }\n function compareFn(a, b) {\n if (reverse) { var tmp; tmp = a; a = b; b = tmp; }\n if (ignoreCase) { a = a.toLowerCase(); b = b.toLowerCase(); }\n var anum = number && numberRegex.exec(a);\n var bnum = number && numberRegex.exec(b);\n if (!anum) { return a < b ? -1 : 1; }\n anum = parseInt((anum[1] + anum[2]).toLowerCase(), radix);\n bnum = parseInt((bnum[1] + bnum[2]).toLowerCase(), radix);\n return anum - bnum;\n }\n numPart.sort(compareFn);\n textPart.sort(compareFn);\n text = (!reverse) ? textPart.concat(numPart) : numPart.concat(textPart);\n if (unique) { // Remove duplicate lines\n var textOld = text;\n var lastLine;\n text = [];\n for (var i = 0; i < textOld.length; i++) {\n if (textOld[i] != lastLine) {\n text.push(textOld[i]);\n }\n lastLine = textOld[i];\n }\n }\n cm.replaceRange(text.join('\\n'), curStart, curEnd);\n },\n global: function(cm, params) {\n // a global command is of the form\n // :[range]g/pattern/[cmd]\n // argString holds the string /pattern/[cmd]\n var argString = params.argString;\n if (!argString) {\n showConfirm(cm, 'Regular Expression missing from global');\n return;\n }\n // range is specified here\n var lineStart = (params.line !== undefined) ? params.line : cm.firstLine();\n var lineEnd = params.lineEnd || params.line || cm.lastLine();\n // get the tokens from argString\n var tokens = splitBySlash(argString);\n var regexPart = argString, cmd;\n if (tokens.length) {\n regexPart = tokens[0];\n cmd = tokens.slice(1, tokens.length).join('/');\n }\n if (regexPart) {\n // If regex part is empty, then use the previous query. Otherwise\n // use the regex part as the new query.\n try {\n updateSearchQuery(cm, regexPart, true /** ignoreCase */,\n true /** smartCase */);\n } catch (e) {\n showConfirm(cm, 'Invalid regex: ' + regexPart);\n return;\n }\n }\n // now that we have the regexPart, search for regex matches in the\n // specified range of lines\n var query = getSearchState(cm).getQuery();\n var matchedLines = [], content = '';\n for (var i = lineStart; i <= lineEnd; i++) {\n var matched = query.test(cm.getLine(i));\n if (matched) {\n matchedLines.push(i+1);\n content+= cm.getLine(i) + '<br>';\n }\n }\n // if there is no [cmd], just display the list of matched lines\n if (!cmd) {\n showConfirm(cm, content);\n return;\n }\n var index = 0;\n var nextCommand = function() {\n if (index < matchedLines.length) {\n var command = matchedLines[index] + cmd;\n exCommandDispatcher.processCommand(cm, command, {\n callback: nextCommand\n });\n }\n index++;\n };\n nextCommand();\n },\n substitute: function(cm, params) {\n if (!cm.getSearchCursor) {\n throw new Error('Search feature not available. Requires searchcursor.js or ' +\n 'any other getSearchCursor implementation.');\n }\n var argString = params.argString;\n var tokens = argString ? splitBySlash(argString) : [];\n var regexPart, replacePart = '', trailing, flagsPart, count;\n var confirm = false; // Whether to confirm each replace.\n var global = false; // True to replace all instances on a line, false to replace only 1.\n if (tokens.length) {\n regexPart = tokens[0];\n replacePart = tokens[1];\n if (replacePart !== undefined) {\n if (getOption('pcre')) {\n replacePart = unescapeRegexReplace(replacePart);\n } else {\n replacePart = translateRegexReplace(replacePart);\n }\n vimGlobalState.lastSubstituteReplacePart = replacePart;\n }\n trailing = tokens[2] ? tokens[2].split(' ') : [];\n } else {\n // either the argString is empty or its of the form ' hello/world'\n // actually splitBySlash returns a list of tokens\n // only if the string starts with a '/'\n if (argString && argString.length) {\n showConfirm(cm, 'Substitutions should be of the form ' +\n ':s/pattern/replace/');\n return;\n }\n }\n // After the 3rd slash, we can have flags followed by a space followed\n // by count.\n if (trailing) {\n flagsPart = trailing[0];\n count = parseInt(trailing[1]);\n if (flagsPart) {\n if (flagsPart.indexOf('c') != -1) {\n confirm = true;\n flagsPart.replace('c', '');\n }\n if (flagsPart.indexOf('g') != -1) {\n global = true;\n flagsPart.replace('g', '');\n }\n regexPart = regexPart + '/' + flagsPart;\n }\n }\n if (regexPart) {\n // If regex part is empty, then use the previous query. Otherwise use\n // the regex part as the new query.\n try {\n updateSearchQuery(cm, regexPart, true /** ignoreCase */,\n true /** smartCase */);\n } catch (e) {\n showConfirm(cm, 'Invalid regex: ' + regexPart);\n return;\n }\n }\n replacePart = replacePart || vimGlobalState.lastSubstituteReplacePart;\n if (replacePart === undefined) {\n showConfirm(cm, 'No previous substitute regular expression');\n return;\n }\n var state = getSearchState(cm);\n var query = state.getQuery();\n var lineStart = (params.line !== undefined) ? params.line : cm.getCursor().line;\n var lineEnd = params.lineEnd || lineStart;\n if (lineStart == cm.firstLine() && lineEnd == cm.lastLine()) {\n lineEnd = Infinity;\n }\n if (count) {\n lineStart = lineEnd;\n lineEnd = lineStart + count - 1;\n }\n var startPos = clipCursorToContent(cm, Pos(lineStart, 0));\n var cursor = cm.getSearchCursor(query, startPos);\n doReplace(cm, confirm, global, lineStart, lineEnd, cursor, query, replacePart, params.callback);\n },\n redo: CodeMirror.commands.redo,\n undo: CodeMirror.commands.undo,\n write: function(cm) {\n if (CodeMirror.commands.save) {\n // If a save command is defined, call it.\n CodeMirror.commands.save(cm);\n } else {\n // Saves to text area if no save command is defined.\n cm.save();\n }\n },\n nohlsearch: function(cm) {\n clearSearchHighlight(cm);\n },\n delmarks: function(cm, params) {\n if (!params.argString || !trim(params.argString)) {\n showConfirm(cm, 'Argument required');\n return;\n }\n\n var state = cm.state.vim;\n var stream = new CodeMirror.StringStream(trim(params.argString));\n while (!stream.eol()) {\n stream.eatSpace();\n\n // Record the streams position at the beginning of the loop for use\n // in error messages.\n var count = stream.pos;\n\n if (!stream.match(/[a-zA-Z]/, false)) {\n showConfirm(cm, 'Invalid argument: ' + params.argString.substring(count));\n return;\n }\n\n var sym = stream.next();\n // Check if this symbol is part of a range\n if (stream.match('-', true)) {\n // This symbol is part of a range.\n\n // The range must terminate at an alphabetic character.\n if (!stream.match(/[a-zA-Z]/, false)) {\n showConfirm(cm, 'Invalid argument: ' + params.argString.substring(count));\n return;\n }\n\n var startMark = sym;\n var finishMark = stream.next();\n // The range must terminate at an alphabetic character which\n // shares the same case as the start of the range.\n if (isLowerCase(startMark) && isLowerCase(finishMark) ||\n isUpperCase(startMark) && isUpperCase(finishMark)) {\n var start = startMark.charCodeAt(0);\n var finish = finishMark.charCodeAt(0);\n if (start >= finish) {\n showConfirm(cm, 'Invalid argument: ' + params.argString.substring(count));\n return;\n }\n\n // Because marks are always ASCII values, and we have\n // determined that they are the same case, we can use\n // their char codes to iterate through the defined range.\n for (var j = 0; j <= finish - start; j++) {\n var mark = String.fromCharCode(start + j);\n delete state.marks[mark];\n }\n } else {\n showConfirm(cm, 'Invalid argument: ' + startMark + '-');\n return;\n }\n } else {\n // This symbol is a valid mark, and is not part of a range.\n delete state.marks[sym];\n }\n }\n }\n };\n\n var exCommandDispatcher = new ExCommandDispatcher();\n\n /**\n * @param {CodeMirror} cm CodeMirror instance we are in.\n * @param {boolean} confirm Whether to confirm each replace.\n * @param {Cursor} lineStart Line to start replacing from.\n * @param {Cursor} lineEnd Line to stop replacing at.\n * @param {RegExp} query Query for performing matches with.\n * @param {string} replaceWith Text to replace matches with. May contain $1,\n * $2, etc for replacing captured groups using Javascript replace.\n * @param {function()} callback A callback for when the replace is done.\n */\n function doReplace(cm, confirm, global, lineStart, lineEnd, searchCursor, query,\n replaceWith, callback) {\n // Set up all the functions.\n cm.state.vim.exMode = true;\n var done = false;\n var lastPos = searchCursor.from();\n function replaceAll() {\n cm.operation(function() {\n while (!done) {\n replace();\n next();\n }\n stop();\n });\n }\n function replace() {\n var text = cm.getRange(searchCursor.from(), searchCursor.to());\n var newText = text.replace(query, replaceWith);\n searchCursor.replace(newText);\n }\n function next() {\n // The below only loops to skip over multiple occurrences on the same\n // line when 'global' is not true.\n while(searchCursor.findNext() &&\n isInRange(searchCursor.from(), lineStart, lineEnd)) {\n if (!global && lastPos && searchCursor.from().line == lastPos.line) {\n continue;\n }\n cm.scrollIntoView(searchCursor.from(), 30);\n cm.setSelection(searchCursor.from(), searchCursor.to());\n lastPos = searchCursor.from();\n done = false;\n return;\n }\n done = true;\n }\n function stop(close) {\n if (close) { close(); }\n cm.focus();\n if (lastPos) {\n cm.setCursor(lastPos);\n var vim = cm.state.vim;\n vim.exMode = false;\n vim.lastHPos = vim.lastHSPos = lastPos.ch;\n }\n if (callback) { callback(); }\n }\n function onPromptKeyDown(e, _value, close) {\n // Swallow all keys.\n CodeMirror.e_stop(e);\n var keyName = CodeMirror.keyName(e);\n switch (keyName) {\n case 'Y':\n replace(); next(); break;\n case 'N':\n next(); break;\n case 'A':\n // replaceAll contains a call to close of its own. We don't want it\n // to fire too early or multiple times.\n var savedCallback = callback;\n callback = undefined;\n cm.operation(replaceAll);\n callback = savedCallback;\n break;\n case 'L':\n replace();\n // fall through and exit.\n case 'Q':\n case 'Esc':\n case 'Ctrl-C':\n case 'Ctrl-[':\n stop(close);\n break;\n }\n if (done) { stop(close); }\n return true;\n }\n\n // Actually do replace.\n next();\n if (done) {\n showConfirm(cm, 'No matches for ' + query.source);\n return;\n }\n if (!confirm) {\n replaceAll();\n if (callback) { callback(); };\n return;\n }\n showPrompt(cm, {\n prefix: 'replace with <strong>' + replaceWith + '</strong> (y/n/a/q/l)',\n onKeyDown: onPromptKeyDown\n });\n }\n\n CodeMirror.keyMap.vim = {\n attach: attachVimMap,\n detach: detachVimMap,\n call: cmKey\n };\n\n function exitInsertMode(cm) {\n var vim = cm.state.vim;\n var macroModeState = vimGlobalState.macroModeState;\n var insertModeChangeRegister = vimGlobalState.registerController.getRegister('.');\n var isPlaying = macroModeState.isPlaying;\n var lastChange = macroModeState.lastInsertModeChanges;\n // In case of visual block, the insertModeChanges are not saved as a\n // single word, so we convert them to a single word\n // so as to update the \". register as expected in real vim.\n var text = [];\n if (!isPlaying) {\n var selLength = lastChange.inVisualBlock ? vim.lastSelection.visualBlock.height : 1;\n var changes = lastChange.changes;\n var text = [];\n var i = 0;\n // In case of multiple selections in blockwise visual,\n // the inserted text, for example: 'f<Backspace>oo', is stored as\n // 'f', 'f', InsertModeKey 'o', 'o', 'o', 'o'. (if you have a block with 2 lines).\n // We push the contents of the changes array as per the following:\n // 1. In case of InsertModeKey, just increment by 1.\n // 2. In case of a character, jump by selLength (2 in the example).\n while (i < changes.length) {\n // This loop will convert 'ff<bs>oooo' to 'f<bs>oo'.\n text.push(changes[i]);\n if (changes[i] instanceof InsertModeKey) {\n i++;\n } else {\n i+= selLength;\n }\n }\n lastChange.changes = text;\n cm.off('change', onChange);\n CodeMirror.off(cm.getInputField(), 'keydown', onKeyEventTargetKeyDown);\n }\n if (!isPlaying && vim.insertModeRepeat > 1) {\n // Perform insert mode repeat for commands like 3,a and 3,o.\n repeatLastEdit(cm, vim, vim.insertModeRepeat - 1,\n true /** repeatForInsert */);\n vim.lastEditInputState.repeatOverride = vim.insertModeRepeat;\n }\n delete vim.insertModeRepeat;\n vim.insertMode = false;\n cm.setCursor(cm.getCursor().line, cm.getCursor().ch-1);\n cm.setOption('keyMap', 'vim');\n cm.setOption('disableInput', true);\n cm.toggleOverwrite(false); // exit replace mode if we were in it.\n // update the \". register before exiting insert mode\n insertModeChangeRegister.setText(lastChange.changes.join(''));\n CodeMirror.signal(cm, \"vim-mode-change\", {mode: \"normal\"});\n if (macroModeState.isRecording) {\n logInsertModeChange(macroModeState);\n }\n }\n\n function _mapCommand(command) {\n defaultKeymap.unshift(command);\n }\n\n function mapCommand(keys, type, name, args, extra) {\n var command = {keys: keys, type: type};\n command[type] = name;\n command[type + \"Args\"] = args;\n for (var key in extra)\n command[key] = extra[key];\n _mapCommand(command);\n }\n\n // The timeout in milliseconds for the two-character ESC keymap should be\n // adjusted according to your typing speed to prevent false positives.\n defineOption('insertModeEscKeysTimeout', 200, 'number');\n\n CodeMirror.keyMap['vim-insert'] = {\n // TODO: override navigation keys so that Esc will cancel automatic\n // indentation from o, O, i_<CR>\n 'Ctrl-N': 'autocomplete',\n 'Ctrl-P': 'autocomplete',\n 'Enter': function(cm) {\n var fn = CodeMirror.commands.newlineAndIndentContinueComment ||\n CodeMirror.commands.newlineAndIndent;\n fn(cm);\n },\n fallthrough: ['default'],\n attach: attachVimMap,\n detach: detachVimMap,\n call: cmKey\n };\n\n CodeMirror.keyMap['vim-replace'] = {\n 'Backspace': 'goCharLeft',\n fallthrough: ['vim-insert'],\n attach: attachVimMap,\n detach: detachVimMap,\n call: cmKey\n };\n\n function executeMacroRegister(cm, vim, macroModeState, registerName) {\n var register = vimGlobalState.registerController.getRegister(registerName);\n if (registerName == ':') {\n // Read-only register containing last Ex command.\n if (register.keyBuffer[0]) {\n exCommandDispatcher.processCommand(cm, register.keyBuffer[0]);\n }\n macroModeState.isPlaying = false;\n return;\n }\n var keyBuffer = register.keyBuffer;\n var imc = 0;\n macroModeState.isPlaying = true;\n macroModeState.replaySearchQueries = register.searchQueries.slice(0);\n for (var i = 0; i < keyBuffer.length; i++) {\n var text = keyBuffer[i];\n var match, key;\n while (text) {\n // Pull off one command key, which is either a single character\n // or a special sequence wrapped in '<' and '>', e.g. '<Space>'.\n match = (/<\\w+-.+?>|<\\w+>|./).exec(text);\n key = match[0];\n text = text.substring(match.index + key.length);\n CodeMirror.Vim.handleKey(cm, key, 'macro');\n if (vim.insertMode) {\n var changes = register.insertModeChanges[imc++].changes;\n vimGlobalState.macroModeState.lastInsertModeChanges.changes =\n changes;\n repeatInsertModeChanges(cm, changes, 1);\n exitInsertMode(cm);\n }\n }\n };\n macroModeState.isPlaying = false;\n }\n\n function logKey(macroModeState, key) {\n if (macroModeState.isPlaying) { return; }\n var registerName = macroModeState.latestRegister;\n var register = vimGlobalState.registerController.getRegister(registerName);\n if (register) {\n register.pushText(key);\n }\n }\n\n function logInsertModeChange(macroModeState) {\n if (macroModeState.isPlaying) { return; }\n var registerName = macroModeState.latestRegister;\n var register = vimGlobalState.registerController.getRegister(registerName);\n if (register && register.pushInsertModeChanges) {\n register.pushInsertModeChanges(macroModeState.lastInsertModeChanges);\n }\n }\n\n function logSearchQuery(macroModeState, query) {\n if (macroModeState.isPlaying) { return; }\n var registerName = macroModeState.latestRegister;\n var register = vimGlobalState.registerController.getRegister(registerName);\n if (register && register.pushSearchQuery) {\n register.pushSearchQuery(query);\n }\n }\n\n /**\n * Listens for changes made in insert mode.\n * Should only be active in insert mode.\n */\n function onChange(_cm, changeObj) {\n var macroModeState = vimGlobalState.macroModeState;\n var lastChange = macroModeState.lastInsertModeChanges;\n if (!macroModeState.isPlaying) {\n while(changeObj) {\n lastChange.expectCursorActivityForChange = true;\n if (changeObj.origin == '+input' || changeObj.origin == 'paste'\n || changeObj.origin === undefined /* only in testing */) {\n var text = changeObj.text.join('\\n');\n lastChange.changes.push(text);\n }\n // Change objects may be chained with next.\n changeObj = changeObj.next;\n }\n }\n }\n\n /**\n * Listens for any kind of cursor activity on CodeMirror.\n */\n function onCursorActivity(cm) {\n var vim = cm.state.vim;\n if (vim.insertMode) {\n // Tracking cursor activity in insert mode (for macro support).\n var macroModeState = vimGlobalState.macroModeState;\n if (macroModeState.isPlaying) { return; }\n var lastChange = macroModeState.lastInsertModeChanges;\n if (lastChange.expectCursorActivityForChange) {\n lastChange.expectCursorActivityForChange = false;\n } else {\n // Cursor moved outside the context of an edit. Reset the change.\n lastChange.changes = [];\n }\n } else if (!cm.curOp.isVimOp) {\n handleExternalSelection(cm, vim);\n }\n if (vim.visualMode) {\n updateFakeCursor(cm);\n }\n }\n function updateFakeCursor(cm) {\n var vim = cm.state.vim;\n var from = clipCursorToContent(cm, copyCursor(vim.sel.head));\n var to = offsetCursor(from, 0, 1);\n if (vim.fakeCursor) {\n vim.fakeCursor.clear();\n }\n vim.fakeCursor = cm.markText(from, to, {className: 'cm-animate-fat-cursor'});\n }\n function handleExternalSelection(cm, vim) {\n var anchor = cm.getCursor('anchor');\n var head = cm.getCursor('head');\n // Enter or exit visual mode to match mouse selection.\n if (vim.visualMode && !cm.somethingSelected()) {\n exitVisualMode(cm, false);\n } else if (!vim.visualMode && !vim.insertMode && cm.somethingSelected()) {\n vim.visualMode = true;\n vim.visualLine = false;\n CodeMirror.signal(cm, \"vim-mode-change\", {mode: \"visual\"});\n }\n if (vim.visualMode) {\n // Bind CodeMirror selection model to vim selection model.\n // Mouse selections are considered visual characterwise.\n var headOffset = !cursorIsBefore(head, anchor) ? -1 : 0;\n var anchorOffset = cursorIsBefore(head, anchor) ? -1 : 0;\n head = offsetCursor(head, 0, headOffset);\n anchor = offsetCursor(anchor, 0, anchorOffset);\n vim.sel = {\n anchor: anchor,\n head: head\n };\n updateMark(cm, vim, '<', cursorMin(head, anchor));\n updateMark(cm, vim, '>', cursorMax(head, anchor));\n } else if (!vim.insertMode) {\n // Reset lastHPos if selection was modified by something outside of vim mode e.g. by mouse.\n vim.lastHPos = cm.getCursor().ch;\n }\n }\n\n /** Wrapper for special keys pressed in insert mode */\n function InsertModeKey(keyName) {\n this.keyName = keyName;\n }\n\n /**\n * Handles raw key down events from the text area.\n * - Should only be active in insert mode.\n * - For recording deletes in insert mode.\n */\n function onKeyEventTargetKeyDown(e) {\n var macroModeState = vimGlobalState.macroModeState;\n var lastChange = macroModeState.lastInsertModeChanges;\n var keyName = CodeMirror.keyName(e);\n if (!keyName) { return; }\n function onKeyFound() {\n lastChange.changes.push(new InsertModeKey(keyName));\n return true;\n }\n if (keyName.indexOf('Delete') != -1 || keyName.indexOf('Backspace') != -1) {\n CodeMirror.lookupKey(keyName, 'vim-insert', onKeyFound);\n }\n }\n\n /**\n * Repeats the last edit, which includes exactly 1 command and at most 1\n * insert. Operator and motion commands are read from lastEditInputState,\n * while action commands are read from lastEditActionCommand.\n *\n * If repeatForInsert is true, then the function was called by\n * exitInsertMode to repeat the insert mode changes the user just made. The\n * corresponding enterInsertMode call was made with a count.\n */\n function repeatLastEdit(cm, vim, repeat, repeatForInsert) {\n var macroModeState = vimGlobalState.macroModeState;\n macroModeState.isPlaying = true;\n var isAction = !!vim.lastEditActionCommand;\n var cachedInputState = vim.inputState;\n function repeatCommand() {\n if (isAction) {\n commandDispatcher.processAction(cm, vim, vim.lastEditActionCommand);\n } else {\n commandDispatcher.evalInput(cm, vim);\n }\n }\n function repeatInsert(repeat) {\n if (macroModeState.lastInsertModeChanges.changes.length > 0) {\n // For some reason, repeat cw in desktop VIM does not repeat\n // insert mode changes. Will conform to that behavior.\n repeat = !vim.lastEditActionCommand ? 1 : repeat;\n var changeObject = macroModeState.lastInsertModeChanges;\n repeatInsertModeChanges(cm, changeObject.changes, repeat);\n }\n }\n vim.inputState = vim.lastEditInputState;\n if (isAction && vim.lastEditActionCommand.interlaceInsertRepeat) {\n // o and O repeat have to be interlaced with insert repeats so that the\n // insertions appear on separate lines instead of the last line.\n for (var i = 0; i < repeat; i++) {\n repeatCommand();\n repeatInsert(1);\n }\n } else {\n if (!repeatForInsert) {\n // Hack to get the cursor to end up at the right place. If I is\n // repeated in insert mode repeat, cursor will be 1 insert\n // change set left of where it should be.\n repeatCommand();\n }\n repeatInsert(repeat);\n }\n vim.inputState = cachedInputState;\n if (vim.insertMode && !repeatForInsert) {\n // Don't exit insert mode twice. If repeatForInsert is set, then we\n // were called by an exitInsertMode call lower on the stack.\n exitInsertMode(cm);\n }\n macroModeState.isPlaying = false;\n };\n\n function repeatInsertModeChanges(cm, changes, repeat) {\n function keyHandler(binding) {\n if (typeof binding == 'string') {\n CodeMirror.commands[binding](cm);\n } else {\n binding(cm);\n }\n return true;\n }\n var head = cm.getCursor('head');\n var inVisualBlock = vimGlobalState.macroModeState.lastInsertModeChanges.inVisualBlock;\n if (inVisualBlock) {\n // Set up block selection again for repeating the changes.\n var vim = cm.state.vim;\n var lastSel = vim.lastSelection;\n var offset = getOffset(lastSel.anchor, lastSel.head);\n selectForInsert(cm, head, offset.line + 1);\n repeat = cm.listSelections().length;\n cm.setCursor(head);\n }\n for (var i = 0; i < repeat; i++) {\n if (inVisualBlock) {\n cm.setCursor(offsetCursor(head, i, 0));\n }\n for (var j = 0; j < changes.length; j++) {\n var change = changes[j];\n if (change instanceof InsertModeKey) {\n CodeMirror.lookupKey(change.keyName, 'vim-insert', keyHandler);\n } else {\n var cur = cm.getCursor();\n cm.replaceRange(change, cur, cur);\n }\n }\n }\n if (inVisualBlock) {\n cm.setCursor(offsetCursor(head, 0, 1));\n }\n }\n\n resetVimGlobalState();\n return vimApi;\n };\n // Initialize Vim and make it available as an API.\n CodeMirror.Vim = Vim();\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/keymap/vim.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/keymap/sublime.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n// A rough approximation of Sublime Text's keybindings\n// Depends on addon/search/searchcursor.js and optionally addon/dialog/dialogs.js\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../lib/codemirror\"), require(\"../addon/search/searchcursor\"), require(\"../addon/edit/matchbrackets\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../lib/codemirror\", \"../addon/search/searchcursor\", \"../addon/edit/matchbrackets\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n \"use strict\";\n\n var map = CodeMirror.keyMap.sublime = {fallthrough: \"default\"};\n var cmds = CodeMirror.commands;\n var Pos = CodeMirror.Pos;\n var mac = CodeMirror.keyMap[\"default\"] == CodeMirror.keyMap.macDefault;\n var ctrl = mac ? \"Cmd-\" : \"Ctrl-\";\n\n // This is not exactly Sublime's algorithm. I couldn't make heads or tails of that.\n function findPosSubword(doc, start, dir) {\n if (dir < 0 && start.ch == 0) return doc.clipPos(Pos(start.line - 1));\n var line = doc.getLine(start.line);\n if (dir > 0 && start.ch >= line.length) return doc.clipPos(Pos(start.line + 1, 0));\n var state = \"start\", type;\n for (var pos = start.ch, e = dir < 0 ? 0 : line.length, i = 0; pos != e; pos += dir, i++) {\n var next = line.charAt(dir < 0 ? pos - 1 : pos);\n var cat = next != \"_\" && CodeMirror.isWordChar(next) ? \"w\" : \"o\";\n if (cat == \"w\" && next.toUpperCase() == next) cat = \"W\";\n if (state == \"start\") {\n if (cat != \"o\") { state = \"in\"; type = cat; }\n } else if (state == \"in\") {\n if (type != cat) {\n if (type == \"w\" && cat == \"W\" && dir < 0) pos--;\n if (type == \"W\" && cat == \"w\" && dir > 0) { type = \"w\"; continue; }\n break;\n }\n }\n }\n return Pos(start.line, pos);\n }\n\n function moveSubword(cm, dir) {\n cm.extendSelectionsBy(function(range) {\n if (cm.display.shift || cm.doc.extend || range.empty())\n return findPosSubword(cm.doc, range.head, dir);\n else\n return dir < 0 ? range.from() : range.to();\n });\n }\n\n cmds[map[\"Alt-Left\"] = \"goSubwordLeft\"] = function(cm) { moveSubword(cm, -1); };\n cmds[map[\"Alt-Right\"] = \"goSubwordRight\"] = function(cm) { moveSubword(cm, 1); };\n\n var scrollLineCombo = mac ? \"Ctrl-Alt-\" : \"Ctrl-\";\n\n cmds[map[scrollLineCombo + \"Up\"] = \"scrollLineUp\"] = function(cm) {\n var info = cm.getScrollInfo();\n if (!cm.somethingSelected()) {\n var visibleBottomLine = cm.lineAtHeight(info.top + info.clientHeight, \"local\");\n if (cm.getCursor().line >= visibleBottomLine)\n cm.execCommand(\"goLineUp\");\n }\n cm.scrollTo(null, info.top - cm.defaultTextHeight());\n };\n cmds[map[scrollLineCombo + \"Down\"] = \"scrollLineDown\"] = function(cm) {\n var info = cm.getScrollInfo();\n if (!cm.somethingSelected()) {\n var visibleTopLine = cm.lineAtHeight(info.top, \"local\")+1;\n if (cm.getCursor().line <= visibleTopLine)\n cm.execCommand(\"goLineDown\");\n }\n cm.scrollTo(null, info.top + cm.defaultTextHeight());\n };\n\n cmds[map[\"Shift-\" + ctrl + \"L\"] = \"splitSelectionByLine\"] = function(cm) {\n var ranges = cm.listSelections(), lineRanges = [];\n for (var i = 0; i < ranges.length; i++) {\n var from = ranges[i].from(), to = ranges[i].to();\n for (var line = from.line; line <= to.line; ++line)\n if (!(to.line > from.line && line == to.line && to.ch == 0))\n lineRanges.push({anchor: line == from.line ? from : Pos(line, 0),\n head: line == to.line ? to : Pos(line)});\n }\n cm.setSelections(lineRanges, 0);\n };\n\n map[\"Shift-Tab\"] = \"indentLess\";\n\n cmds[map[\"Esc\"] = \"singleSelectionTop\"] = function(cm) {\n var range = cm.listSelections()[0];\n cm.setSelection(range.anchor, range.head, {scroll: false});\n };\n\n cmds[map[ctrl + \"L\"] = \"selectLine\"] = function(cm) {\n var ranges = cm.listSelections(), extended = [];\n for (var i = 0; i < ranges.length; i++) {\n var range = ranges[i];\n extended.push({anchor: Pos(range.from().line, 0),\n head: Pos(range.to().line + 1, 0)});\n }\n cm.setSelections(extended);\n };\n\n map[\"Shift-Ctrl-K\"] = \"deleteLine\";\n\n function insertLine(cm, above) {\n if (cm.isReadOnly()) return CodeMirror.Pass\n cm.operation(function() {\n var len = cm.listSelections().length, newSelection = [], last = -1;\n for (var i = 0; i < len; i++) {\n var head = cm.listSelections()[i].head;\n if (head.line <= last) continue;\n var at = Pos(head.line + (above ? 0 : 1), 0);\n cm.replaceRange(\"\\n\", at, null, \"+insertLine\");\n cm.indentLine(at.line, null, true);\n newSelection.push({head: at, anchor: at});\n last = head.line + 1;\n }\n cm.setSelections(newSelection);\n });\n }\n\n cmds[map[ctrl + \"Enter\"] = \"insertLineAfter\"] = function(cm) { return insertLine(cm, false); };\n\n cmds[map[\"Shift-\" + ctrl + \"Enter\"] = \"insertLineBefore\"] = function(cm) { return insertLine(cm, true); };\n\n function wordAt(cm, pos) {\n var start = pos.ch, end = start, line = cm.getLine(pos.line);\n while (start && CodeMirror.isWordChar(line.charAt(start - 1))) --start;\n while (end < line.length && CodeMirror.isWordChar(line.charAt(end))) ++end;\n return {from: Pos(pos.line, start), to: Pos(pos.line, end), word: line.slice(start, end)};\n }\n\n cmds[map[ctrl + \"D\"] = \"selectNextOccurrence\"] = function(cm) {\n var from = cm.getCursor(\"from\"), to = cm.getCursor(\"to\");\n var fullWord = cm.state.sublimeFindFullWord == cm.doc.sel;\n if (CodeMirror.cmpPos(from, to) == 0) {\n var word = wordAt(cm, from);\n if (!word.word) return;\n cm.setSelection(word.from, word.to);\n fullWord = true;\n } else {\n var text = cm.getRange(from, to);\n var query = fullWord ? new RegExp(\"\\\\b\" + text + \"\\\\b\") : text;\n var cur = cm.getSearchCursor(query, to);\n if (cur.findNext()) {\n cm.addSelection(cur.from(), cur.to());\n } else {\n cur = cm.getSearchCursor(query, Pos(cm.firstLine(), 0));\n if (cur.findNext())\n cm.addSelection(cur.from(), cur.to());\n }\n }\n if (fullWord)\n cm.state.sublimeFindFullWord = cm.doc.sel;\n };\n\n var mirror = \"(){}[]\";\n function selectBetweenBrackets(cm) {\n var pos = cm.getCursor(), opening = cm.scanForBracket(pos, -1);\n if (!opening) return;\n for (;;) {\n var closing = cm.scanForBracket(pos, 1);\n if (!closing) return;\n if (closing.ch == mirror.charAt(mirror.indexOf(opening.ch) + 1)) {\n cm.setSelection(Pos(opening.pos.line, opening.pos.ch + 1), closing.pos, false);\n return true;\n }\n pos = Pos(closing.pos.line, closing.pos.ch + 1);\n }\n }\n\n cmds[map[\"Shift-\" + ctrl + \"Space\"] = \"selectScope\"] = function(cm) {\n selectBetweenBrackets(cm) || cm.execCommand(\"selectAll\");\n };\n cmds[map[\"Shift-\" + ctrl + \"M\"] = \"selectBetweenBrackets\"] = function(cm) {\n if (!selectBetweenBrackets(cm)) return CodeMirror.Pass;\n };\n\n cmds[map[ctrl + \"M\"] = \"goToBracket\"] = function(cm) {\n cm.extendSelectionsBy(function(range) {\n var next = cm.scanForBracket(range.head, 1);\n if (next && CodeMirror.cmpPos(next.pos, range.head) != 0) return next.pos;\n var prev = cm.scanForBracket(range.head, -1);\n return prev && Pos(prev.pos.line, prev.pos.ch + 1) || range.head;\n });\n };\n\n var swapLineCombo = mac ? \"Cmd-Ctrl-\" : \"Shift-Ctrl-\";\n\n cmds[map[swapLineCombo + \"Up\"] = \"swapLineUp\"] = function(cm) {\n if (cm.isReadOnly()) return CodeMirror.Pass\n var ranges = cm.listSelections(), linesToMove = [], at = cm.firstLine() - 1, newSels = [];\n for (var i = 0; i < ranges.length; i++) {\n var range = ranges[i], from = range.from().line - 1, to = range.to().line;\n newSels.push({anchor: Pos(range.anchor.line - 1, range.anchor.ch),\n head: Pos(range.head.line - 1, range.head.ch)});\n if (range.to().ch == 0 && !range.empty()) --to;\n if (from > at) linesToMove.push(from, to);\n else if (linesToMove.length) linesToMove[linesToMove.length - 1] = to;\n at = to;\n }\n cm.operation(function() {\n for (var i = 0; i < linesToMove.length; i += 2) {\n var from = linesToMove[i], to = linesToMove[i + 1];\n var line = cm.getLine(from);\n cm.replaceRange(\"\", Pos(from, 0), Pos(from + 1, 0), \"+swapLine\");\n if (to > cm.lastLine())\n cm.replaceRange(\"\\n\" + line, Pos(cm.lastLine()), null, \"+swapLine\");\n else\n cm.replaceRange(line + \"\\n\", Pos(to, 0), null, \"+swapLine\");\n }\n cm.setSelections(newSels);\n cm.scrollIntoView();\n });\n };\n\n cmds[map[swapLineCombo + \"Down\"] = \"swapLineDown\"] = function(cm) {\n if (cm.isReadOnly()) return CodeMirror.Pass\n var ranges = cm.listSelections(), linesToMove = [], at = cm.lastLine() + 1;\n for (var i = ranges.length - 1; i >= 0; i--) {\n var range = ranges[i], from = range.to().line + 1, to = range.from().line;\n if (range.to().ch == 0 && !range.empty()) from--;\n if (from < at) linesToMove.push(from, to);\n else if (linesToMove.length) linesToMove[linesToMove.length - 1] = to;\n at = to;\n }\n cm.operation(function() {\n for (var i = linesToMove.length - 2; i >= 0; i -= 2) {\n var from = linesToMove[i], to = linesToMove[i + 1];\n var line = cm.getLine(from);\n if (from == cm.lastLine())\n cm.replaceRange(\"\", Pos(from - 1), Pos(from), \"+swapLine\");\n else\n cm.replaceRange(\"\", Pos(from, 0), Pos(from + 1, 0), \"+swapLine\");\n cm.replaceRange(line + \"\\n\", Pos(to, 0), null, \"+swapLine\");\n }\n cm.scrollIntoView();\n });\n };\n\n cmds[map[ctrl + \"/\"] = \"toggleCommentIndented\"] = function(cm) {\n cm.toggleComment({ indent: true });\n }\n\n cmds[map[ctrl + \"J\"] = \"joinLines\"] = function(cm) {\n var ranges = cm.listSelections(), joined = [];\n for (var i = 0; i < ranges.length; i++) {\n var range = ranges[i], from = range.from();\n var start = from.line, end = range.to().line;\n while (i < ranges.length - 1 && ranges[i + 1].from().line == end)\n end = ranges[++i].to().line;\n joined.push({start: start, end: end, anchor: !range.empty() && from});\n }\n cm.operation(function() {\n var offset = 0, ranges = [];\n for (var i = 0; i < joined.length; i++) {\n var obj = joined[i];\n var anchor = obj.anchor && Pos(obj.anchor.line - offset, obj.anchor.ch), head;\n for (var line = obj.start; line <= obj.end; line++) {\n var actual = line - offset;\n if (line == obj.end) head = Pos(actual, cm.getLine(actual).length + 1);\n if (actual < cm.lastLine()) {\n cm.replaceRange(\" \", Pos(actual), Pos(actual + 1, /^\\s*/.exec(cm.getLine(actual + 1))[0].length));\n ++offset;\n }\n }\n ranges.push({anchor: anchor || head, head: head});\n }\n cm.setSelections(ranges, 0);\n });\n };\n\n cmds[map[\"Shift-\" + ctrl + \"D\"] = \"duplicateLine\"] = function(cm) {\n cm.operation(function() {\n var rangeCount = cm.listSelections().length;\n for (var i = 0; i < rangeCount; i++) {\n var range = cm.listSelections()[i];\n if (range.empty())\n cm.replaceRange(cm.getLine(range.head.line) + \"\\n\", Pos(range.head.line, 0));\n else\n cm.replaceRange(cm.getRange(range.from(), range.to()), range.from());\n }\n cm.scrollIntoView();\n });\n };\n\n map[ctrl + \"T\"] = \"transposeChars\";\n\n function sortLines(cm, caseSensitive) {\n if (cm.isReadOnly()) return CodeMirror.Pass\n var ranges = cm.listSelections(), toSort = [], selected;\n for (var i = 0; i < ranges.length; i++) {\n var range = ranges[i];\n if (range.empty()) continue;\n var from = range.from().line, to = range.to().line;\n while (i < ranges.length - 1 && ranges[i + 1].from().line == to)\n to = range[++i].to().line;\n toSort.push(from, to);\n }\n if (toSort.length) selected = true;\n else toSort.push(cm.firstLine(), cm.lastLine());\n\n cm.operation(function() {\n var ranges = [];\n for (var i = 0; i < toSort.length; i += 2) {\n var from = toSort[i], to = toSort[i + 1];\n var start = Pos(from, 0), end = Pos(to);\n var lines = cm.getRange(start, end, false);\n if (caseSensitive)\n lines.sort();\n else\n lines.sort(function(a, b) {\n var au = a.toUpperCase(), bu = b.toUpperCase();\n if (au != bu) { a = au; b = bu; }\n return a < b ? -1 : a == b ? 0 : 1;\n });\n cm.replaceRange(lines, start, end);\n if (selected) ranges.push({anchor: start, head: end});\n }\n if (selected) cm.setSelections(ranges, 0);\n });\n }\n\n cmds[map[\"F9\"] = \"sortLines\"] = function(cm) { sortLines(cm, true); };\n cmds[map[ctrl + \"F9\"] = \"sortLinesInsensitive\"] = function(cm) { sortLines(cm, false); };\n\n cmds[map[\"F2\"] = \"nextBookmark\"] = function(cm) {\n var marks = cm.state.sublimeBookmarks;\n if (marks) while (marks.length) {\n var current = marks.shift();\n var found = current.find();\n if (found) {\n marks.push(current);\n return cm.setSelection(found.from, found.to);\n }\n }\n };\n\n cmds[map[\"Shift-F2\"] = \"prevBookmark\"] = function(cm) {\n var marks = cm.state.sublimeBookmarks;\n if (marks) while (marks.length) {\n marks.unshift(marks.pop());\n var found = marks[marks.length - 1].find();\n if (!found)\n marks.pop();\n else\n return cm.setSelection(found.from, found.to);\n }\n };\n\n cmds[map[ctrl + \"F2\"] = \"toggleBookmark\"] = function(cm) {\n var ranges = cm.listSelections();\n var marks = cm.state.sublimeBookmarks || (cm.state.sublimeBookmarks = []);\n for (var i = 0; i < ranges.length; i++) {\n var from = ranges[i].from(), to = ranges[i].to();\n var found = cm.findMarks(from, to);\n for (var j = 0; j < found.length; j++) {\n if (found[j].sublimeBookmark) {\n found[j].clear();\n for (var k = 0; k < marks.length; k++)\n if (marks[k] == found[j])\n marks.splice(k--, 1);\n break;\n }\n }\n if (j == found.length)\n marks.push(cm.markText(from, to, {sublimeBookmark: true, clearWhenEmpty: false}));\n }\n };\n\n cmds[map[\"Shift-\" + ctrl + \"F2\"] = \"clearBookmarks\"] = function(cm) {\n var marks = cm.state.sublimeBookmarks;\n if (marks) for (var i = 0; i < marks.length; i++) marks[i].clear();\n marks.length = 0;\n };\n\n cmds[map[\"Alt-F2\"] = \"selectBookmarks\"] = function(cm) {\n var marks = cm.state.sublimeBookmarks, ranges = [];\n if (marks) for (var i = 0; i < marks.length; i++) {\n var found = marks[i].find();\n if (!found)\n marks.splice(i--, 0);\n else\n ranges.push({anchor: found.from, head: found.to});\n }\n if (ranges.length)\n cm.setSelections(ranges, 0);\n };\n\n map[\"Alt-Q\"] = \"wrapLines\";\n\n var cK = ctrl + \"K \";\n\n function modifyWordOrSelection(cm, mod) {\n cm.operation(function() {\n var ranges = cm.listSelections(), indices = [], replacements = [];\n for (var i = 0; i < ranges.length; i++) {\n var range = ranges[i];\n if (range.empty()) { indices.push(i); replacements.push(\"\"); }\n else replacements.push(mod(cm.getRange(range.from(), range.to())));\n }\n cm.replaceSelections(replacements, \"around\", \"case\");\n for (var i = indices.length - 1, at; i >= 0; i--) {\n var range = ranges[indices[i]];\n if (at && CodeMirror.cmpPos(range.head, at) > 0) continue;\n var word = wordAt(cm, range.head);\n at = word.from;\n cm.replaceRange(mod(word.word), word.from, word.to);\n }\n });\n }\n\n map[cK + ctrl + \"Backspace\"] = \"delLineLeft\";\n\n cmds[map[\"Backspace\"] = \"smartBackspace\"] = function(cm) {\n if (cm.somethingSelected()) return CodeMirror.Pass;\n\n var cursor = cm.getCursor();\n var toStartOfLine = cm.getRange({line: cursor.line, ch: 0}, cursor);\n var column = CodeMirror.countColumn(toStartOfLine, null, cm.getOption(\"tabSize\"));\n var indentUnit = cm.getOption(\"indentUnit\");\n\n if (toStartOfLine && !/\\S/.test(toStartOfLine) && column % indentUnit == 0) {\n var prevIndent = new Pos(cursor.line,\n CodeMirror.findColumn(toStartOfLine, column - indentUnit, indentUnit));\n\n // If no smart delete is happening (due to tab sizing) just do a regular delete\n if (prevIndent.ch == cursor.ch) return CodeMirror.Pass;\n\n return cm.replaceRange(\"\", prevIndent, cursor, \"+delete\");\n } else {\n return CodeMirror.Pass;\n }\n };\n\n cmds[map[cK + ctrl + \"K\"] = \"delLineRight\"] = function(cm) {\n cm.operation(function() {\n var ranges = cm.listSelections();\n for (var i = ranges.length - 1; i >= 0; i--)\n cm.replaceRange(\"\", ranges[i].anchor, Pos(ranges[i].to().line), \"+delete\");\n cm.scrollIntoView();\n });\n };\n\n cmds[map[cK + ctrl + \"U\"] = \"upcaseAtCursor\"] = function(cm) {\n modifyWordOrSelection(cm, function(str) { return str.toUpperCase(); });\n };\n cmds[map[cK + ctrl + \"L\"] = \"downcaseAtCursor\"] = function(cm) {\n modifyWordOrSelection(cm, function(str) { return str.toLowerCase(); });\n };\n\n cmds[map[cK + ctrl + \"Space\"] = \"setSublimeMark\"] = function(cm) {\n if (cm.state.sublimeMark) cm.state.sublimeMark.clear();\n cm.state.sublimeMark = cm.setBookmark(cm.getCursor());\n };\n cmds[map[cK + ctrl + \"A\"] = \"selectToSublimeMark\"] = function(cm) {\n var found = cm.state.sublimeMark && cm.state.sublimeMark.find();\n if (found) cm.setSelection(cm.getCursor(), found);\n };\n cmds[map[cK + ctrl + \"W\"] = \"deleteToSublimeMark\"] = function(cm) {\n var found = cm.state.sublimeMark && cm.state.sublimeMark.find();\n if (found) {\n var from = cm.getCursor(), to = found;\n if (CodeMirror.cmpPos(from, to) > 0) { var tmp = to; to = from; from = tmp; }\n cm.state.sublimeKilled = cm.getRange(from, to);\n cm.replaceRange(\"\", from, to);\n }\n };\n cmds[map[cK + ctrl + \"X\"] = \"swapWithSublimeMark\"] = function(cm) {\n var found = cm.state.sublimeMark && cm.state.sublimeMark.find();\n if (found) {\n cm.state.sublimeMark.clear();\n cm.state.sublimeMark = cm.setBookmark(cm.getCursor());\n cm.setCursor(found);\n }\n };\n cmds[map[cK + ctrl + \"Y\"] = \"sublimeYank\"] = function(cm) {\n if (cm.state.sublimeKilled != null)\n cm.replaceSelection(cm.state.sublimeKilled, null, \"paste\");\n };\n\n map[cK + ctrl + \"G\"] = \"clearBookmarks\";\n cmds[map[cK + ctrl + \"C\"] = \"showInCenter\"] = function(cm) {\n var pos = cm.cursorCoords(null, \"local\");\n cm.scrollTo(null, (pos.top + pos.bottom) / 2 - cm.getScrollInfo().clientHeight / 2);\n };\n\n cmds[map[\"Shift-Alt-Up\"] = \"selectLinesUpward\"] = function(cm) {\n cm.operation(function() {\n var ranges = cm.listSelections();\n for (var i = 0; i < ranges.length; i++) {\n var range = ranges[i];\n if (range.head.line > cm.firstLine())\n cm.addSelection(Pos(range.head.line - 1, range.head.ch));\n }\n });\n };\n cmds[map[\"Shift-Alt-Down\"] = \"selectLinesDownward\"] = function(cm) {\n cm.operation(function() {\n var ranges = cm.listSelections();\n for (var i = 0; i < ranges.length; i++) {\n var range = ranges[i];\n if (range.head.line < cm.lastLine())\n cm.addSelection(Pos(range.head.line + 1, range.head.ch));\n }\n });\n };\n\n function getTarget(cm) {\n var from = cm.getCursor(\"from\"), to = cm.getCursor(\"to\");\n if (CodeMirror.cmpPos(from, to) == 0) {\n var word = wordAt(cm, from);\n if (!word.word) return;\n from = word.from;\n to = word.to;\n }\n return {from: from, to: to, query: cm.getRange(from, to), word: word};\n }\n\n function findAndGoTo(cm, forward) {\n var target = getTarget(cm);\n if (!target) return;\n var query = target.query;\n var cur = cm.getSearchCursor(query, forward ? target.to : target.from);\n\n if (forward ? cur.findNext() : cur.findPrevious()) {\n cm.setSelection(cur.from(), cur.to());\n } else {\n cur = cm.getSearchCursor(query, forward ? Pos(cm.firstLine(), 0)\n : cm.clipPos(Pos(cm.lastLine())));\n if (forward ? cur.findNext() : cur.findPrevious())\n cm.setSelection(cur.from(), cur.to());\n else if (target.word)\n cm.setSelection(target.from, target.to);\n }\n };\n cmds[map[ctrl + \"F3\"] = \"findUnder\"] = function(cm) { findAndGoTo(cm, true); };\n cmds[map[\"Shift-\" + ctrl + \"F3\"] = \"findUnderPrevious\"] = function(cm) { findAndGoTo(cm,false); };\n cmds[map[\"Alt-F3\"] = \"findAllUnder\"] = function(cm) {\n var target = getTarget(cm);\n if (!target) return;\n var cur = cm.getSearchCursor(target.query);\n var matches = [];\n var primaryIndex = -1;\n while (cur.findNext()) {\n matches.push({anchor: cur.from(), head: cur.to()});\n if (cur.from().line <= target.from.line && cur.from().ch <= target.from.ch)\n primaryIndex++;\n }\n cm.setSelections(matches, primaryIndex);\n };\n\n map[\"Shift-\" + ctrl + \"[\"] = \"fold\";\n map[\"Shift-\" + ctrl + \"]\"] = \"unfold\";\n map[cK + ctrl + \"0\"] = map[cK + ctrl + \"j\"] = \"unfoldAll\";\n\n map[ctrl + \"I\"] = \"findIncremental\";\n map[\"Shift-\" + ctrl + \"I\"] = \"findIncrementalReverse\";\n map[ctrl + \"H\"] = \"replace\";\n map[\"F3\"] = \"findNext\";\n map[\"Shift-F3\"] = \"findPrev\";\n\n CodeMirror.normalizeKeyMap(map);\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/keymap/sublime.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/keymap/emacs.js": {
"text": "// CodeMirror, copyright (c) by Marijn Haverbeke and others\n// Distributed under an MIT license: http://codemirror.net/LICENSE\n\n(function(mod) {\n if (typeof exports == \"object\" && typeof module == \"object\") // CommonJS\n mod(require(\"../lib/codemirror\"));\n else if (typeof define == \"function\" && define.amd) // AMD\n define([\"../lib/codemirror\"], mod);\n else // Plain browser env\n mod(CodeMirror);\n})(function(CodeMirror) {\n \"use strict\";\n\n var Pos = CodeMirror.Pos;\n function posEq(a, b) { return a.line == b.line && a.ch == b.ch; }\n\n // Kill 'ring'\n\n var killRing = [];\n function addToRing(str) {\n killRing.push(str);\n if (killRing.length > 50) killRing.shift();\n }\n function growRingTop(str) {\n if (!killRing.length) return addToRing(str);\n killRing[killRing.length - 1] += str;\n }\n function getFromRing(n) { return killRing[killRing.length - (n ? Math.min(n, 1) : 1)] || \"\"; }\n function popFromRing() { if (killRing.length > 1) killRing.pop(); return getFromRing(); }\n\n var lastKill = null;\n\n function kill(cm, from, to, mayGrow, text) {\n if (text == null) text = cm.getRange(from, to);\n\n if (mayGrow && lastKill && lastKill.cm == cm && posEq(from, lastKill.pos) && cm.isClean(lastKill.gen))\n growRingTop(text);\n else\n addToRing(text);\n cm.replaceRange(\"\", from, to, \"+delete\");\n\n if (mayGrow) lastKill = {cm: cm, pos: from, gen: cm.changeGeneration()};\n else lastKill = null;\n }\n\n // Boundaries of various units\n\n function byChar(cm, pos, dir) {\n return cm.findPosH(pos, dir, \"char\", true);\n }\n\n function byWord(cm, pos, dir) {\n return cm.findPosH(pos, dir, \"word\", true);\n }\n\n function byLine(cm, pos, dir) {\n return cm.findPosV(pos, dir, \"line\", cm.doc.sel.goalColumn);\n }\n\n function byPage(cm, pos, dir) {\n return cm.findPosV(pos, dir, \"page\", cm.doc.sel.goalColumn);\n }\n\n function byParagraph(cm, pos, dir) {\n var no = pos.line, line = cm.getLine(no);\n var sawText = /\\S/.test(dir < 0 ? line.slice(0, pos.ch) : line.slice(pos.ch));\n var fst = cm.firstLine(), lst = cm.lastLine();\n for (;;) {\n no += dir;\n if (no < fst || no > lst)\n return cm.clipPos(Pos(no - dir, dir < 0 ? 0 : null));\n line = cm.getLine(no);\n var hasText = /\\S/.test(line);\n if (hasText) sawText = true;\n else if (sawText) return Pos(no, 0);\n }\n }\n\n function bySentence(cm, pos, dir) {\n var line = pos.line, ch = pos.ch;\n var text = cm.getLine(pos.line), sawWord = false;\n for (;;) {\n var next = text.charAt(ch + (dir < 0 ? -1 : 0));\n if (!next) { // End/beginning of line reached\n if (line == (dir < 0 ? cm.firstLine() : cm.lastLine())) return Pos(line, ch);\n text = cm.getLine(line + dir);\n if (!/\\S/.test(text)) return Pos(line, ch);\n line += dir;\n ch = dir < 0 ? text.length : 0;\n continue;\n }\n if (sawWord && /[!?.]/.test(next)) return Pos(line, ch + (dir > 0 ? 1 : 0));\n if (!sawWord) sawWord = /\\w/.test(next);\n ch += dir;\n }\n }\n\n function byExpr(cm, pos, dir) {\n var wrap;\n if (cm.findMatchingBracket && (wrap = cm.findMatchingBracket(pos, true))\n && wrap.match && (wrap.forward ? 1 : -1) == dir)\n return dir > 0 ? Pos(wrap.to.line, wrap.to.ch + 1) : wrap.to;\n\n for (var first = true;; first = false) {\n var token = cm.getTokenAt(pos);\n var after = Pos(pos.line, dir < 0 ? token.start : token.end);\n if (first && dir > 0 && token.end == pos.ch || !/\\w/.test(token.string)) {\n var newPos = cm.findPosH(after, dir, \"char\");\n if (posEq(after, newPos)) return pos;\n else pos = newPos;\n } else {\n return after;\n }\n }\n }\n\n // Prefixes (only crudely supported)\n\n function getPrefix(cm, precise) {\n var digits = cm.state.emacsPrefix;\n if (!digits) return precise ? null : 1;\n clearPrefix(cm);\n return digits == \"-\" ? -1 : Number(digits);\n }\n\n function repeated(cmd) {\n var f = typeof cmd == \"string\" ? function(cm) { cm.execCommand(cmd); } : cmd;\n return function(cm) {\n var prefix = getPrefix(cm);\n f(cm);\n for (var i = 1; i < prefix; ++i) f(cm);\n };\n }\n\n function findEnd(cm, pos, by, dir) {\n var prefix = getPrefix(cm);\n if (prefix < 0) { dir = -dir; prefix = -prefix; }\n for (var i = 0; i < prefix; ++i) {\n var newPos = by(cm, pos, dir);\n if (posEq(newPos, pos)) break;\n pos = newPos;\n }\n return pos;\n }\n\n function move(by, dir) {\n var f = function(cm) {\n cm.extendSelection(findEnd(cm, cm.getCursor(), by, dir));\n };\n f.motion = true;\n return f;\n }\n\n function killTo(cm, by, dir) {\n var selections = cm.listSelections(), cursor;\n var i = selections.length;\n while (i--) {\n cursor = selections[i].head;\n kill(cm, cursor, findEnd(cm, cursor, by, dir), true);\n }\n }\n\n function killRegion(cm) {\n if (cm.somethingSelected()) {\n var selections = cm.listSelections(), selection;\n var i = selections.length;\n while (i--) {\n selection = selections[i];\n kill(cm, selection.anchor, selection.head);\n }\n return true;\n }\n }\n\n function addPrefix(cm, digit) {\n if (cm.state.emacsPrefix) {\n if (digit != \"-\") cm.state.emacsPrefix += digit;\n return;\n }\n // Not active yet\n cm.state.emacsPrefix = digit;\n cm.on(\"keyHandled\", maybeClearPrefix);\n cm.on(\"inputRead\", maybeDuplicateInput);\n }\n\n var prefixPreservingKeys = {\"Alt-G\": true, \"Ctrl-X\": true, \"Ctrl-Q\": true, \"Ctrl-U\": true};\n\n function maybeClearPrefix(cm, arg) {\n if (!cm.state.emacsPrefixMap && !prefixPreservingKeys.hasOwnProperty(arg))\n clearPrefix(cm);\n }\n\n function clearPrefix(cm) {\n cm.state.emacsPrefix = null;\n cm.off(\"keyHandled\", maybeClearPrefix);\n cm.off(\"inputRead\", maybeDuplicateInput);\n }\n\n function maybeDuplicateInput(cm, event) {\n var dup = getPrefix(cm);\n if (dup > 1 && event.origin == \"+input\") {\n var one = event.text.join(\"\\n\"), txt = \"\";\n for (var i = 1; i < dup; ++i) txt += one;\n cm.replaceSelection(txt);\n }\n }\n\n function addPrefixMap(cm) {\n cm.state.emacsPrefixMap = true;\n cm.addKeyMap(prefixMap);\n cm.on(\"keyHandled\", maybeRemovePrefixMap);\n cm.on(\"inputRead\", maybeRemovePrefixMap);\n }\n\n function maybeRemovePrefixMap(cm, arg) {\n if (typeof arg == \"string\" && (/^\\d$/.test(arg) || arg == \"Ctrl-U\")) return;\n cm.removeKeyMap(prefixMap);\n cm.state.emacsPrefixMap = false;\n cm.off(\"keyHandled\", maybeRemovePrefixMap);\n cm.off(\"inputRead\", maybeRemovePrefixMap);\n }\n\n // Utilities\n\n function setMark(cm) {\n cm.setCursor(cm.getCursor());\n cm.setExtending(!cm.getExtending());\n cm.on(\"change\", function() { cm.setExtending(false); });\n }\n\n function clearMark(cm) {\n cm.setExtending(false);\n cm.setCursor(cm.getCursor());\n }\n\n function getInput(cm, msg, f) {\n if (cm.openDialog)\n cm.openDialog(msg + \": <input type=\\\"text\\\" style=\\\"width: 10em\\\"/>\", f, {bottom: true});\n else\n f(prompt(msg, \"\"));\n }\n\n function operateOnWord(cm, op) {\n var start = cm.getCursor(), end = cm.findPosH(start, 1, \"word\");\n cm.replaceRange(op(cm.getRange(start, end)), start, end);\n cm.setCursor(end);\n }\n\n function toEnclosingExpr(cm) {\n var pos = cm.getCursor(), line = pos.line, ch = pos.ch;\n var stack = [];\n while (line >= cm.firstLine()) {\n var text = cm.getLine(line);\n for (var i = ch == null ? text.length : ch; i > 0;) {\n var ch = text.charAt(--i);\n if (ch == \")\")\n stack.push(\"(\");\n else if (ch == \"]\")\n stack.push(\"[\");\n else if (ch == \"}\")\n stack.push(\"{\");\n else if (/[\\(\\{\\[]/.test(ch) && (!stack.length || stack.pop() != ch))\n return cm.extendSelection(Pos(line, i));\n }\n --line; ch = null;\n }\n }\n\n function quit(cm) {\n cm.execCommand(\"clearSearch\");\n clearMark(cm);\n }\n\n // Actual keymap\n\n var keyMap = CodeMirror.keyMap.emacs = CodeMirror.normalizeKeyMap({\n \"Ctrl-W\": function(cm) {kill(cm, cm.getCursor(\"start\"), cm.getCursor(\"end\"));},\n \"Ctrl-K\": repeated(function(cm) {\n var start = cm.getCursor(), end = cm.clipPos(Pos(start.line));\n var text = cm.getRange(start, end);\n if (!/\\S/.test(text)) {\n text += \"\\n\";\n end = Pos(start.line + 1, 0);\n }\n kill(cm, start, end, true, text);\n }),\n \"Alt-W\": function(cm) {\n addToRing(cm.getSelection());\n clearMark(cm);\n },\n \"Ctrl-Y\": function(cm) {\n var start = cm.getCursor();\n cm.replaceRange(getFromRing(getPrefix(cm)), start, start, \"paste\");\n cm.setSelection(start, cm.getCursor());\n },\n \"Alt-Y\": function(cm) {cm.replaceSelection(popFromRing(), \"around\", \"paste\");},\n\n \"Ctrl-Space\": setMark, \"Ctrl-Shift-2\": setMark,\n\n \"Ctrl-F\": move(byChar, 1), \"Ctrl-B\": move(byChar, -1),\n \"Right\": move(byChar, 1), \"Left\": move(byChar, -1),\n \"Ctrl-D\": function(cm) { killTo(cm, byChar, 1); },\n \"Delete\": function(cm) { killRegion(cm) || killTo(cm, byChar, 1); },\n \"Ctrl-H\": function(cm) { killTo(cm, byChar, -1); },\n \"Backspace\": function(cm) { killRegion(cm) || killTo(cm, byChar, -1); },\n\n \"Alt-F\": move(byWord, 1), \"Alt-B\": move(byWord, -1),\n \"Alt-D\": function(cm) { killTo(cm, byWord, 1); },\n \"Alt-Backspace\": function(cm) { killTo(cm, byWord, -1); },\n\n \"Ctrl-N\": move(byLine, 1), \"Ctrl-P\": move(byLine, -1),\n \"Down\": move(byLine, 1), \"Up\": move(byLine, -1),\n \"Ctrl-A\": \"goLineStart\", \"Ctrl-E\": \"goLineEnd\",\n \"End\": \"goLineEnd\", \"Home\": \"goLineStart\",\n\n \"Alt-V\": move(byPage, -1), \"Ctrl-V\": move(byPage, 1),\n \"PageUp\": move(byPage, -1), \"PageDown\": move(byPage, 1),\n\n \"Ctrl-Up\": move(byParagraph, -1), \"Ctrl-Down\": move(byParagraph, 1),\n\n \"Alt-A\": move(bySentence, -1), \"Alt-E\": move(bySentence, 1),\n \"Alt-K\": function(cm) { killTo(cm, bySentence, 1); },\n\n \"Ctrl-Alt-K\": function(cm) { killTo(cm, byExpr, 1); },\n \"Ctrl-Alt-Backspace\": function(cm) { killTo(cm, byExpr, -1); },\n \"Ctrl-Alt-F\": move(byExpr, 1), \"Ctrl-Alt-B\": move(byExpr, -1),\n\n \"Shift-Ctrl-Alt-2\": function(cm) {\n var cursor = cm.getCursor();\n cm.setSelection(findEnd(cm, cursor, byExpr, 1), cursor);\n },\n \"Ctrl-Alt-T\": function(cm) {\n var leftStart = byExpr(cm, cm.getCursor(), -1), leftEnd = byExpr(cm, leftStart, 1);\n var rightEnd = byExpr(cm, leftEnd, 1), rightStart = byExpr(cm, rightEnd, -1);\n cm.replaceRange(cm.getRange(rightStart, rightEnd) + cm.getRange(leftEnd, rightStart) +\n cm.getRange(leftStart, leftEnd), leftStart, rightEnd);\n },\n \"Ctrl-Alt-U\": repeated(toEnclosingExpr),\n\n \"Alt-Space\": function(cm) {\n var pos = cm.getCursor(), from = pos.ch, to = pos.ch, text = cm.getLine(pos.line);\n while (from && /\\s/.test(text.charAt(from - 1))) --from;\n while (to < text.length && /\\s/.test(text.charAt(to))) ++to;\n cm.replaceRange(\" \", Pos(pos.line, from), Pos(pos.line, to));\n },\n \"Ctrl-O\": repeated(function(cm) { cm.replaceSelection(\"\\n\", \"start\"); }),\n \"Ctrl-T\": repeated(function(cm) {\n cm.execCommand(\"transposeChars\");\n }),\n\n \"Alt-C\": repeated(function(cm) {\n operateOnWord(cm, function(w) {\n var letter = w.search(/\\w/);\n if (letter == -1) return w;\n return w.slice(0, letter) + w.charAt(letter).toUpperCase() + w.slice(letter + 1).toLowerCase();\n });\n }),\n \"Alt-U\": repeated(function(cm) {\n operateOnWord(cm, function(w) { return w.toUpperCase(); });\n }),\n \"Alt-L\": repeated(function(cm) {\n operateOnWord(cm, function(w) { return w.toLowerCase(); });\n }),\n\n \"Alt-;\": \"toggleComment\",\n\n \"Ctrl-/\": repeated(\"undo\"), \"Shift-Ctrl--\": repeated(\"undo\"),\n \"Ctrl-Z\": repeated(\"undo\"), \"Cmd-Z\": repeated(\"undo\"),\n \"Shift-Alt-,\": \"goDocStart\", \"Shift-Alt-.\": \"goDocEnd\",\n \"Ctrl-S\": \"findNext\", \"Ctrl-R\": \"findPrev\", \"Ctrl-G\": quit, \"Shift-Alt-5\": \"replace\",\n \"Alt-/\": \"autocomplete\",\n \"Ctrl-J\": \"newlineAndIndent\", \"Enter\": false, \"Tab\": \"indentAuto\",\n\n \"Alt-G G\": function(cm) {\n var prefix = getPrefix(cm, true);\n if (prefix != null && prefix > 0) return cm.setCursor(prefix - 1);\n\n getInput(cm, \"Goto line\", function(str) {\n var num;\n if (str && !isNaN(num = Number(str)) && num == (num|0) && num > 0)\n cm.setCursor(num - 1);\n });\n },\n\n \"Ctrl-X Tab\": function(cm) {\n cm.indentSelection(getPrefix(cm, true) || cm.getOption(\"indentUnit\"));\n },\n \"Ctrl-X Ctrl-X\": function(cm) {\n cm.setSelection(cm.getCursor(\"head\"), cm.getCursor(\"anchor\"));\n },\n \"Ctrl-X Ctrl-S\": \"save\",\n \"Ctrl-X Ctrl-W\": \"save\",\n \"Ctrl-X S\": \"saveAll\",\n \"Ctrl-X F\": \"open\",\n \"Ctrl-X U\": repeated(\"undo\"),\n \"Ctrl-X K\": \"close\",\n \"Ctrl-X Delete\": function(cm) { kill(cm, cm.getCursor(), bySentence(cm, cm.getCursor(), 1), true); },\n \"Ctrl-X H\": \"selectAll\",\n\n \"Ctrl-Q Tab\": repeated(\"insertTab\"),\n \"Ctrl-U\": addPrefixMap\n });\n\n var prefixMap = {\"Ctrl-G\": clearPrefix};\n function regPrefix(d) {\n prefixMap[d] = function(cm) { addPrefix(cm, d); };\n keyMap[\"Ctrl-\" + d] = function(cm) { addPrefix(cm, d); };\n prefixPreservingKeys[\"Ctrl-\" + d] = true;\n }\n for (var i = 0; i < 10; ++i) regPrefix(String(i));\n regPrefix(\"-\");\n});\n",
"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/codemirror/keymap/emacs.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/codemirror/readme": {
"title": "$:/plugins/tiddlywiki/codemirror/readme",
"text": "This plugin provides an enhanced text editor component based on [[CodeMirror|http://codemirror.net]]. It provides several advantages over the default browser text editor:\n\n* Code colouring for many languages (see [[the official documentation here|http://codemirror.net/mode/index.html]])\n* Auto closing brackets and tags\n* Folding brackets, comments, and tags\n* Auto-completion\n\n[[Source code|https://github.com/Jermolene/TiddlyWiki5/blob/master/plugins/tiddlywiki/codemirror]]\n\nBased on ~CodeMirror version 5.13.2\n"
},
"$:/plugins/tiddlywiki/codemirror/styles": {
"title": "$:/plugins/tiddlywiki/codemirror/styles",
"tags": "[[$:/tags/Stylesheet]]",
"text": "/* Make the editor resize to fit its content */\n\n.CodeMirror {\n\theight: auto;\n\tborder: 1px solid #ddd;\n\tline-height: 1.5;\n\tfont-family: \"Monaco\", monospace;\n}\n\n.CodeMirror-scroll {\n\toverflow-x: auto;\n\toverflow-y: hidden;\t\n}\n"
},
"$:/plugins/tiddlywiki/codemirror/usage": {
"title": "$:/plugins/tiddlywiki/codemirror/usage",
"text": "! Setting ~CodeMirror Content Types\n\nYou can determine which tiddler content types are edited by the ~CodeMirror widget by creating or modifying special tiddlers whose prefix is comprised of the string `$:/config/EditorTypeMappings/` concatenated with the content type. The text of that tiddler gives the editor type to be used (eg, ''text'', ''bitmap'', ''codemirror'').\n\nThe current editor type mappings are shown in [[$:/ControlPanel]] under the \"Advanced\" tab.\n\n! ~CodeMirror Configuration\n\nYou can configure the ~CodeMirror plugin by creating a tiddler called [[$:/config/CodeMirror]] containing a JSON configuration object. The configuration tiddler must have its type field set to `application/json` to take effect.\n\nSee http://codemirror.net/ for details of available configuration options.\n\nFor example:\n\n```\n{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/dialog/dialog.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/search/searchcursor.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js\",\n \"$:/plugins/tiddlywiki/codemirror/keymap/vim.js\",\n \"$:/plugins/tiddlywiki/codemirror/keymap/emacs.js\"\n ],\n \"configuration\": {\n \"keyMap\": \"vim\",\n \"matchBrackets\":true,\n \"showCursorWhenSelecting\": true\n }\n}\n```\n\n!! Basic working configuration\n\n# Create a tiddler called `$:/config/CodeMirror`\n\n# The type of the tiddler has to be set to `application/json`\n\n# The text of the tiddler is the following: \n\n```\n{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js\"\n ],\n \"configuration\": {\n \"matchBrackets\":true,\n \"showCursorWhenSelecting\": true\n }\n}\n\n```\n\n# You should see line numbers when editing a tiddler\n# When editing a tiddler, no matter what the type of the tiddler is set to, you should see matching brackets being highlighted whenever the cursor is next to one of them\n# If you edit a tiddler with the type `application/javascript` or `application/json` you should see the code being syntax highlighted\n\n!! Add HTML syntax highlighting\n\n# Create a tiddler `$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js`\n## Add a field `module-type` and set it to ''library''\n## Set the field `type` to ''application/javascript''\n## Set the text field of the tiddler with the javascript code from this link : [[https://raw.githubusercontent.com/codemirror/CodeMirror/master/mode/xml/xml.js]]\n# Set the text field of the tiddler `$:/config/CodeMirror` to:\n\n```\n{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js\"\n ],\n \"configuration\": {\n \"showCursorWhenSelecting\": true,\n \"matchBrackets\":true\n }\n}\n```\n# Edit a tiddler with the type `text/html` and write some html code. You should see your code being coloured\n\n!! Add a non-existing language mode\n\nHere's an example of adding a new language mode - in this case, the language C.\n\n\n# Create a tiddler `$:/plugins/tiddlywiki/codemirror/mode/clike/clike.js`\n## Add a field `module-type` and set it to ''library''\n## Set the field `type` to ''application/javascript''\n## Set the text field of the tiddler with the javascript code from this link : [[https://raw.githubusercontent.com/codemirror/CodeMirror/master/mode/clike/clike.js]]\n# Set the text field of the tiddler `$:/config/CodeMirror` to:\n\n```\n{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/clike/clike.js\"\n ],\n \"configuration\": {\n \"showCursorWhenSelecting\": true\n }\n}\n```\n\n# Add the correct ~EditorTypeMappings tiddler\n## Find the matching MIME type. If you go on the [[CodeMirror documentation for language modes|http://codemirror.net/mode/index.html]] you can see the [[documentation for the c-like mode|http://codemirror.net/mode/clike/index.html]]. In this documentation, at the end you will be told the MIME types defined. Here it's ''text/x-csrc''\n## Add the tiddler: `$:/config/EditorTypeMappings/text/x-csrc` and fill the text field with : ''codemirror''\n\nIf you edit a tiddler with the type `text/x-csrc` and write some code in C, you should see your text being coloured.\n\n!! Add matching tags\n\n# Add XML and HTML colouring\n# Create a tiddler `$:/plugins/tiddlywiki/codemirror/addon/edit/matchtags.js`\n## Add a field `module-type` and set it to ''library''\n## Set the field `type` to ''application/javascript''\n## Set the text field of the tiddler with the javascript code from this link : [[http://codemirror.net/addon/edit/matchtags.js]]\n# Set the text field of the tiddler `$:/config/CodeMirror` to:\n\n```\n{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/edit/matchtags.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js\"\n ],\n \"configuration\": {\n \"showCursorWhenSelecting\": true,\n \"matchTags\": {\"bothTags\": true},\n \"extraKeys\": {\"Ctrl-J\": \"toMatchingTag\"}\n }\n}\n```\n\nEdit a tiddler that has the type :`text/htm` and write this code:\n\n```\n<html>\n <div id=\"click here and press CTRL+J\">\n <ul>\n <li>\n </li>\n </ul>\n </div>\n</html>\n```\n\nIf you click on a tag and press CTRL+J, your cursor will select the matching tag. Supposedly, it should highlight the pair when clicking a tag. However, that part doesn't work.\n\n!! Adding closing tags\n\n# Add the xml mode (see \"Add XML and HTML colouring\")\n# Create a tiddler `$:/plugins/tiddlywiki/codemirror/addon/edit/closetags.js`\n## Add a field `module-type` and set it to ''library''\n## Set the field `type` to ''application/javascript''\n## Set the text field of the tiddler with the javascript code from this link : [[http://codemirror.net/addon/edit/closetag.js]]\n\n# Set the text field of the tiddler `$:/config/CodeMirror` to:\n\n```\n{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/edit/closetags.js\"\n ],\n \"configuration\": {\n \"showCursorWhenSelecting\": true,\n \"autoCloseTags\":true\n }\n}\n```\n\nIf you edit a tiddler with the type`text/html` and write:\n\n```\n<html>\n```\n\nThen the closing tag ''</html>'' should automatically appear.\n\n!! Add closing brackets\n\n# Create a tiddler `$:/plugins/tiddlywiki/codemirror/addon/edit/closebrackets.js`\n## Add a field `module-type` and set it to ''library''\n## Set the field `type` to ''application/javascript''\n## Set the text field of the tiddler with the javascript code from this link : [[http://codemirror.net/addon/edit/closebrackets.js]]\n# Set the text field of the tiddler `$:/config/CodeMirror` to:\n\n```\n{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/edit/matchbrackets.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/edit/closebrackets.js\"\n ],\n\n \"configuration\": {\n\n \"showCursorWhenSelecting\": true,\n \"matchBrackets\":true,\n \"autoCloseBrackets\":true\n }\n}\n```\n\n# If you try to edit any tiddler and write `if(` you should see the bracket closing itself automatically (you will get \"if()\"). It works with (), [], and {}\n# If you try and edit a tiddler with the type `application/javascript`, it will auto-close `()`,`[]`,`{}`,`''` and `\"\"`\n\n!! Adding folding tags\n\n# Create a tiddler `$:/plugins/tiddlywiki/codemirror/addon/fold/foldcode.js`\n## Add a field `module-type` and set it to ''library''\n## Set the field `type` to ''application/javascript''\n## Set the text field of the tiddler with the javascript code from this link : [[http://codemirror.net/addon/fold/foldcode.js]]\n# Repeat the above process for the following tiddlers, but replace the code with the one from the given link:\n## Create a tiddler `$:/plugins/tiddlywiki/codemirror/addon/fold/xml-fold.js`, the code can be found here [[https://raw.githubusercontent.com/codemirror/CodeMirror/master/addon/fold/xml-fold.js]]\n## Create a tiddler `$:/plugins/tiddlywiki/codemirror/addon/fold/foldgutter.js`, the code can be found here [[http://codemirror.net/addon/fold/foldgutter.js]]\n# Create a tiddler `$:/plugins/tiddlywiki/codemirror/addon/fold/foldgutter.css`\n## Add the tag `$:/tags/Stylesheet`\n## Set the text field of the tiddler with the css code from this link : [[http://codemirror.net/addon/fold/foldgutter.css]]\n# Set the text field of the tiddler `$:/config/CodeMirror` to:\n\n```\n{\n \"require\": [\n \"$:/plugins/tiddlywiki/codemirror/mode/javascript/javascript.js\",\n \"$:/plugins/tiddlywiki/codemirror/mode/xml/xml.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/fold/foldcode.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/fold/xml-fold.js\",\n \"$:/plugins/tiddlywiki/codemirror/addon/fold/foldgutter.js\"\n ],\n \"configuration\": {\n \"showCursorWhenSelecting\": true,\n \"matchTags\": {\"bothTags\": true},\n \"foldGutter\": true,\n \"gutters\": [\"CodeMirror-linenumbers\", \"CodeMirror-foldgutter\"]\n }\n}\n```\n\nNow if you type the below code in a tiddler with the type `text/html`:\n\n```\n<html>\n <div>\n <ul>\n\n </ul>\n </div>\n</html>\n```\n\nYou should see little arrows just next to the line numbers. Clicking on it will have the effect to fold the code (or unfold it).\n"
}
}
}
// CodeMirror, copyright (c) by Marijn Haverbeke and others
// Distributed under an MIT license: http://codemirror.net/LICENSE
/***
|''Name''|tiddlywiki.js|
|''Description''|Enables TiddlyWikiy syntax highlighting using CodeMirror|
|''Author''|PMario|
|''Version''|0.1.7|
|''Status''|''stable''|
|''Source''|[[GitHub|https://github.com/pmario/CodeMirror2/blob/tw-syntax/mode/tiddlywiki]]|
|''Documentation''|http://codemirror.tiddlyspace.com/|
|''License''|[[MIT License|http://www.opensource.org/licenses/mit-license.php]]|
|''CoreVersion''|2.5.0|
|''Requires''|codemirror.js|
|''Keywords''|syntax highlighting color code mirror codemirror|
! Info
CoreVersion parameter is needed for TiddlyWiki only!
! Modified by Stan to support basic LaTeX equation highlighting.
***/
// CodeMirror, copyright (c) by Marijn Haverbeke and others
// Distributed under an MIT license: http://codemirror.net/LICENSE
/*
* Author: Constantin Jucovschi (c.jucovschi@jacobs-university.de)
* Licence: MIT
*/
(function(mod) {
if (typeof exports == "object" && typeof module == "object") // CommonJS
mod(require("../../lib/codemirror"));
else if (typeof define == "function" && define.amd) // AMD
define(["../../lib/codemirror"], mod);
else // Plain browser env
mod(CodeMirror);
})(function(CodeMirror) {
"use strict";
CodeMirror.defineMode("tiddlywiki", function() {
"use strict";
// TiddlyWiki variables
var keywords = {
"allTags": true, "closeAll": true, "list": true,
"newJournal": true, "newTiddler": true,
"permaview": true, "saveChanges": true,
"search": true, "slider": true, "tabs": true,
"tag": true, "tagging": true, "tags": true,
"tiddler": true, "timeline": true,
"today": true, "version": true, "option": true,
"with": true, "filter": true
};
var isSpaceName = /[\w_\-]/i,
reHR = /^\-\-\-\-+$/, // <hr>
reWikiCommentStart = /^\/\*\*\*$/, // /***
reWikiCommentStop = /^\*\*\*\/$/, // ***/
reBlockQuote = /^<<<$/,
reJsCodeStart = /^\/\/\{\{\{$/, // //{{{ js block start
reJsCodeStop = /^\/\/\}\}\}$/, // //}}} js stop
reXmlCodeStart = /^<!--\{\{\{-->$/, // xml block start
reXmlCodeStop = /^<!--\}\}\}-->$/, // xml stop
reCodeBlockStart = /^\{\{\{$/, // {{{ TW text div block start
reCodeBlockStop = /^\}\}\}$/, // }}} TW text stop
reUntilCodeStop = /.*?\}\}\}/;
// end of TiddlyWiki variables
function pushCommand(state, command) {
state.cmdState.push(command);
}
function peekCommand(state) {
if (state.cmdState.length > 0) {
return state.cmdState[state.cmdState.length - 1];
} else {
return null;
}
}
function popCommand(state) {
var plug = state.cmdState.pop();
if (plug) {
plug.closeBracket();
}
}
// returns the non-default plugin closest to the end of the list
function getMostPowerful(state) {
var context = state.cmdState;
for (var i = context.length - 1; i >= 0; i--) {
var plug = context[i];
if (plug.name == "DEFAULT") {
continue;
}
return plug;
}
return { styleIdentifier: function() { return null; } };
}
function addPluginPattern(pluginName, cmdStyle, styles) {
return function () {
this.name = pluginName;
this.bracketNo = 0;
this.style = cmdStyle;
this.styles = styles;
this.argument = null; // \begin and \end have arguments that follow. These are stored in the plugin
this.styleIdentifier = function() {
return this.styles[this.bracketNo - 1] || null;
};
this.openBracket = function() {
this.bracketNo++;
return "bracket";
};
this.closeBracket = function() {};
};
}
var plugins = {};
plugins["importmodule"] = addPluginPattern("importmodule", "tag", ["string", "builtin"]);
plugins["documentclass"] = addPluginPattern("documentclass", "tag", ["", "atom"]);
plugins["usepackage"] = addPluginPattern("usepackage", "tag", ["atom"]);
plugins["begin"] = addPluginPattern("begin", "tag", ["atom"]);
plugins["end"] = addPluginPattern("end", "tag", ["atom"]);
plugins["DEFAULT"] = function () {
this.name = "DEFAULT";
this.style = "tag";
this.styleIdentifier = this.openBracket = this.closeBracket = function() {};
};
function setState(state, f) {
state.f = f;
}
// called when in a normal (no environment) context
function normal(source, state) {
var plug;
// tiddlywiki formatting
// check start of blocks
var sol = source.sol(), ch = source.peek();
state.block = false; // indicates the start of a code block.
// check start of blocks
if (sol && /[\/\*!#;:>|]/.test(ch)) {
if (ch == "!") { // tw header
source.skipToEnd();
return "header";
}
if (ch == "*") { // tw list
source.eatWhile('*');
return "comment";
}
if (ch == "#") { // tw numbered list
source.eatWhile('#');
return "comment";
}
if (ch == ";") { // definition list, term
source.eatWhile(';');
return "comment";
}
if (ch == ":") { // definition list, description
source.eatWhile(':');
return "comment";
}
if (ch == ">") { // single line quote
source.eatWhile(">");
return "quote";
}
// starting with '|' crashes the system
/*
if (ch == '|')
return 'header';
*/
}
// rudimentary html:// file:// link matching. TW knows much more ...
if (/[hf]/i.test(ch) &&
/[ti]/i.test(source.peek()) &&
stream.match(/\b(ttps?|tp|ile):\/\/[\-A-Z0-9+&@#\/%?=~_|$!:,.;]*[A-Z0-9+&@#\/%=~_|$]/i))
return "link";
// end of tiddlywiki formatting
// Do we look like '\command' ? If so, attempt to apply the plugin 'command'
if (source.match(/^\\[a-zA-Z@]+/)) {
var cmdName = source.current().slice(1);
plug = plugins[cmdName] || plugins["DEFAULT"];
plug = new plug();
pushCommand(state, plug);
setState(state, beginParams);
return plug.style;
}
// escape characters
if (source.match(/^\\[$&%#{}_]/)) {
return "tag";
}
// white space control characters
if (source.match(/^\\[,;!\/\\]/)) {
return "tag";
}
// find if we're starting various math modes
if (source.match("\\[")) {
setState(state, function(source, state){ return inMathMode(source, state, "\\]"); });
return "keyword";
}
if (source.match("$$")) {
setState(state, function(source, state){ return inMathMode(source, state, "$$"); });
return "keyword";
}
if (source.match("$")) {
setState(state, function(source, state){ return inMathMode(source, state, "$"); });
return "keyword";
}
var ch = source.next();
if (ch == "%") {
source.skipToEnd();
return "comment";
} else if (ch == '}' || ch == ']') {
plug = peekCommand(state);
if (plug) {
plug.closeBracket(ch);
setState(state, beginParams);
} else {
return "error";
}
return "bracket";
} else if (ch == '{' || ch == '[') {
plug = plugins["DEFAULT"];
plug = new plug();
pushCommand(state, plug);
return "bracket";
} else if (/\d/.test(ch)) {
source.eatWhile(/[\w.%]/);
return "atom";
} else {
source.eatWhile(/[\w\-_]/);
plug = getMostPowerful(state);
if (plug.name == 'begin') {
plug.argument = source.current();
}
return plug.styleIdentifier();
}
}
function inMathMode(source, state, endModeSeq) {
if (source.eatSpace()) {
return null;
}
if (source.match(endModeSeq)) {
setState(state, normal);
return "keyword";
}
if (source.match(/^\\[a-zA-Z@]+/)) {
return "tag";
}
if (source.match(/^[a-zA-Z]+/)) {
return "variable-2";
}
// escape characters
if (source.match(/^\\[$&%#{}_]/)) {
return "tag";
}
// white space control characters
if (source.match(/^\\[,;!\/]/)) {
return "tag";
}
// special math-mode characters
if (source.match(/^[\^_&]/)) {
return "tag";
}
// non-special characters
if (source.match(/^[+\-<>|=,\/@!*:;'"`~#?]/)) {
return null;
}
if (source.match(/^(\d+\.\d*|\d*\.\d+|\d+)/)) {
return "number";
}
var ch = source.next();
if (ch == "{" || ch == "}" || ch == "[" || ch == "]" || ch == "(" || ch == ")") {
return "bracket";
}
if (ch == "%") {
source.skipToEnd();
return "comment";
}
return "error";
}
function beginParams(source, state) {
var ch = source.peek(), lastPlug;
if (ch == '{' || ch == '[') {
lastPlug = peekCommand(state);
lastPlug.openBracket(ch);
source.eat(ch);
setState(state, normal);
return "bracket";
}
if (/[ \t\r]/.test(ch)) {
source.eat(ch);
return null;
}
setState(state, normal);
popCommand(state);
return normal(source, state);
}
return {
startState: function() {
return {
cmdState: [],
f: normal
};
},
copyState: function(s) {
return {
cmdState: s.cmdState.slice(),
f: s.f
};
},
token: function(source, state) {
return state.f(source, state);
},
blankLine: function(state) {
state.f = normal;
state.cmdState.length = 0;
},
lineComment: "%"
};
});
CodeMirror.defineMIME("text/vnd.tiddlywiki", "tiddlywiki");
});
.cm-s-smoothy {
font-size: 1em;
line-height: 1.5em;
font-family: inconsolata, monospace;
letter-spacing: 0.3px;
word-spacing: 1px;
background: #FFFFFF;
color: #000000;
}
.cm-s-smoothy .CodeMirror-lines {
padding: 8px 0;
}
.cm-s-smoothy .CodeMirror-gutters {
box-shadow: 1px 0 2px 0 rgba(0, 0, 0, 0.5);
-webkit-box-shadow: 1px 0 2px 0 rgba(0, 0, 0, 0.5);
background-color: #FFFFFF;
padding-right: 10px;
z-index: 3;
border: none;
}
.cm-s-smoothy div.CodeMirror-cursor {
border-left: 3px solid #000000;
}
.cm-s-smoothy .CodeMirror-activeline-background {
background: #00000008;
}
.cm-s-smoothy .CodeMirror-selected {
background: #FFFD0054;
}
.cm-s-smoothy .cm-comment {
color: #CFCFCF;
background: #112B0A00;
}
.cm-s-smoothy .cm-keyword {
color: #D8B229;
}
.cm-s-smoothy .cm-string {
color: #704D3D;
}
.cm-s-smoothy .cm-property {
color: #55A2EA;
}
.cm-s-smoothy .cm-variable-2 {
color: #BAA827;
}
.cm-s-smoothy .cm-atom {
color: #55A2EA;
}
.cm-s-smoothy .cm-number {
color: #55A2EA;
}
.cm-s-smoothy .cm-operator {
color: #D8B229;
}
.cm-s-smoothy .CodeMirror-linenumber {
color: #CFCFCF;
}
{
"tiddlers": {
"$:/config/HighlightPlugin/TypeMappings/application/javascript": {
"title": "$:/config/HighlightPlugin/TypeMappings/application/javascript",
"text": "javascript"
},
"$:/config/HighlightPlugin/TypeMappings/application/json": {
"title": "$:/config/HighlightPlugin/TypeMappings/application/json",
"text": "json"
},
"$:/config/HighlightPlugin/TypeMappings/text/css": {
"title": "$:/config/HighlightPlugin/TypeMappings/text/css",
"text": "css"
},
"$:/config/HighlightPlugin/TypeMappings/text/html": {
"title": "$:/config/HighlightPlugin/TypeMappings/text/html",
"text": "html"
},
"$:/config/HighlightPlugin/TypeMappings/image/svg+xml": {
"title": "$:/config/HighlightPlugin/TypeMappings/image/svg+xml",
"text": "xml"
},
"$:/config/HighlightPlugin/TypeMappings/text/x-markdown": {
"title": "$:/config/HighlightPlugin/TypeMappings/text/x-markdown",
"text": "markdown"
},
"$:/plugins/tiddlywiki/highlight/highlight.js": {
"text": "var hljs = require(\"$:/plugins/tiddlywiki/highlight/highlight.js\");\n!function(e){\"undefined\"!=typeof exports?e(exports):(window.hljs=e({}),\"function\"==typeof define&&define.amd&&define(\"hljs\",[],function(){return window.hljs}))}(function(e){function n(e){return e.replace(/&/gm,\"&\").replace(/</gm,\"<\").replace(/>/gm,\">\")}function t(e){return e.nodeName.toLowerCase()}function r(e,n){var t=e&&e.exec(n);return t&&0==t.index}function a(e){return/^(no-?highlight|plain|text)$/i.test(e)}function i(e){var n,t,r,i=e.className+\" \";if(i+=e.parentNode?e.parentNode.className:\"\",t=/\\blang(?:uage)?-([\\w-]+)\\b/i.exec(i))return w(t[1])?t[1]:\"no-highlight\";for(i=i.split(/\\s+/),n=0,r=i.length;r>n;n++)if(w(i[n])||a(i[n]))return i[n]}function o(e,n){var t,r={};for(t in e)r[t]=e[t];if(n)for(t in n)r[t]=n[t];return r}function u(e){var n=[];return function r(e,a){for(var i=e.firstChild;i;i=i.nextSibling)3==i.nodeType?a+=i.nodeValue.length:1==i.nodeType&&(n.push({event:\"start\",offset:a,node:i}),a=r(i,a),t(i).match(/br|hr|img|input/)||n.push({event:\"stop\",offset:a,node:i}));return a}(e,0),n}function c(e,r,a){function i(){return e.length&&r.length?e[0].offset!=r[0].offset?e[0].offset<r[0].offset?e:r:\"start\"==r[0].event?e:r:e.length?e:r}function o(e){function r(e){return\" \"+e.nodeName+'=\"'+n(e.value)+'\"'}f+=\"<\"+t(e)+Array.prototype.map.call(e.attributes,r).join(\"\")+\">\"}function u(e){f+=\"</\"+t(e)+\">\"}function c(e){(\"start\"==e.event?o:u)(e.node)}for(var s=0,f=\"\",l=[];e.length||r.length;){var g=i();if(f+=n(a.substr(s,g[0].offset-s)),s=g[0].offset,g==e){l.reverse().forEach(u);do c(g.splice(0,1)[0]),g=i();while(g==e&&g.length&&g[0].offset==s);l.reverse().forEach(o)}else\"start\"==g[0].event?l.push(g[0].node):l.pop(),c(g.splice(0,1)[0])}return f+n(a.substr(s))}function s(e){function n(e){return e&&e.source||e}function t(t,r){return new RegExp(n(t),\"m\"+(e.cI?\"i\":\"\")+(r?\"g\":\"\"))}function r(a,i){if(!a.compiled){if(a.compiled=!0,a.k=a.k||a.bK,a.k){var u={},c=function(n,t){e.cI&&(t=t.toLowerCase()),t.split(\" \").forEach(function(e){var t=e.split(\"|\");u[t[0]]=[n,t[1]?Number(t[1]):1]})};\"string\"==typeof a.k?c(\"keyword\",a.k):Object.keys(a.k).forEach(function(e){c(e,a.k[e])}),a.k=u}a.lR=t(a.l||/\\b\\w+\\b/,!0),i&&(a.bK&&(a.b=\"\\\\b(\"+a.bK.split(\" \").join(\"|\")+\")\\\\b\"),a.b||(a.b=/\\B|\\b/),a.bR=t(a.b),a.e||a.eW||(a.e=/\\B|\\b/),a.e&&(a.eR=t(a.e)),a.tE=n(a.e)||\"\",a.eW&&i.tE&&(a.tE+=(a.e?\"|\":\"\")+i.tE)),a.i&&(a.iR=t(a.i)),void 0===a.r&&(a.r=1),a.c||(a.c=[]);var s=[];a.c.forEach(function(e){e.v?e.v.forEach(function(n){s.push(o(e,n))}):s.push(\"self\"==e?a:e)}),a.c=s,a.c.forEach(function(e){r(e,a)}),a.starts&&r(a.starts,i);var f=a.c.map(function(e){return e.bK?\"\\\\.?(\"+e.b+\")\\\\.?\":e.b}).concat([a.tE,a.i]).map(n).filter(Boolean);a.t=f.length?t(f.join(\"|\"),!0):{exec:function(){return null}}}}r(e)}function f(e,t,a,i){function o(e,n){for(var t=0;t<n.c.length;t++)if(r(n.c[t].bR,e))return n.c[t]}function u(e,n){if(r(e.eR,n)){for(;e.endsParent&&e.parent;)e=e.parent;return e}return e.eW?u(e.parent,n):void 0}function c(e,n){return!a&&r(n.iR,e)}function g(e,n){var t=N.cI?n[0].toLowerCase():n[0];return e.k.hasOwnProperty(t)&&e.k[t]}function h(e,n,t,r){var a=r?\"\":E.classPrefix,i='<span class=\"'+a,o=t?\"\":\"</span>\";return i+=e+'\">',i+n+o}function p(){if(!L.k)return n(y);var e=\"\",t=0;L.lR.lastIndex=0;for(var r=L.lR.exec(y);r;){e+=n(y.substr(t,r.index-t));var a=g(L,r);a?(B+=a[1],e+=h(a[0],n(r[0]))):e+=n(r[0]),t=L.lR.lastIndex,r=L.lR.exec(y)}return e+n(y.substr(t))}function d(){var e=\"string\"==typeof L.sL;if(e&&!x[L.sL])return n(y);var t=e?f(L.sL,y,!0,M[L.sL]):l(y,L.sL.length?L.sL:void 0);return L.r>0&&(B+=t.r),e&&(M[L.sL]=t.top),h(t.language,t.value,!1,!0)}function b(){return void 0!==L.sL?d():p()}function v(e,t){var 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"type": "application/javascript",
"title": "$:/plugins/tiddlywiki/highlight/highlight.js",
"module-type": "library"
},
"$:/plugins/tiddlywiki/highlight/highlight.css": {
"text": "/*\n\nOriginal style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org>\n\n*/\n\n.hljs {\n display: block;\n overflow-x: auto;\n padding: 0.5em;\n background: #f0f0f0;\n -webkit-text-size-adjust: none;\n}\n\n.hljs,\n.hljs-subst,\n.hljs-tag .hljs-title,\n.nginx .hljs-title {\n color: black;\n}\n\n.hljs-string,\n.hljs-title,\n.hljs-constant,\n.hljs-parent,\n.hljs-tag .hljs-value,\n.hljs-rule .hljs-value,\n.hljs-preprocessor,\n.hljs-pragma,\n.hljs-name,\n.haml .hljs-symbol,\n.ruby .hljs-symbol,\n.ruby .hljs-symbol .hljs-string,\n.hljs-template_tag,\n.django .hljs-variable,\n.smalltalk .hljs-class,\n.hljs-addition,\n.hljs-flow,\n.hljs-stream,\n.bash .hljs-variable,\n.pf .hljs-variable,\n.apache .hljs-tag,\n.apache .hljs-cbracket,\n.tex .hljs-command,\n.tex .hljs-special,\n.erlang_repl .hljs-function_or_atom,\n.asciidoc .hljs-header,\n.markdown .hljs-header,\n.coffeescript .hljs-attribute,\n.tp .hljs-variable {\n color: #800;\n}\n\n.smartquote,\n.hljs-comment,\n.hljs-annotation,\n.diff .hljs-header,\n.hljs-chunk,\n.asciidoc .hljs-blockquote,\n.markdown .hljs-blockquote {\n color: #888;\n}\n\n.hljs-number,\n.hljs-date,\n.hljs-regexp,\n.hljs-literal,\n.hljs-hexcolor,\n.smalltalk .hljs-symbol,\n.smalltalk .hljs-char,\n.go .hljs-constant,\n.hljs-change,\n.lasso .hljs-variable,\n.makefile .hljs-variable,\n.asciidoc .hljs-bullet,\n.markdown .hljs-bullet,\n.asciidoc .hljs-link_url,\n.markdown .hljs-link_url {\n color: #080;\n}\n\n.hljs-label,\n.ruby .hljs-string,\n.hljs-decorator,\n.hljs-filter .hljs-argument,\n.hljs-localvars,\n.hljs-array,\n.hljs-attr_selector,\n.hljs-important,\n.hljs-pseudo,\n.hljs-pi,\n.haml .hljs-bullet,\n.hljs-doctype,\n.hljs-deletion,\n.hljs-envvar,\n.hljs-shebang,\n.apache .hljs-sqbracket,\n.nginx .hljs-built_in,\n.tex .hljs-formula,\n.erlang_repl .hljs-reserved,\n.hljs-prompt,\n.asciidoc .hljs-link_label,\n.markdown .hljs-link_label,\n.vhdl .hljs-attribute,\n.clojure .hljs-attribute,\n.asciidoc .hljs-attribute,\n.lasso .hljs-attribute,\n.coffeescript .hljs-property,\n.hljs-phony {\n color: #88f;\n}\n\n.hljs-keyword,\n.hljs-id,\n.hljs-title,\n.hljs-built_in,\n.css .hljs-tag,\n.hljs-doctag,\n.smalltalk .hljs-class,\n.hljs-winutils,\n.bash .hljs-variable,\n.pf .hljs-variable,\n.apache .hljs-tag,\n.hljs-type,\n.hljs-typename,\n.tex .hljs-command,\n.asciidoc .hljs-strong,\n.markdown .hljs-strong,\n.hljs-request,\n.hljs-status,\n.tp .hljs-data,\n.tp .hljs-io {\n font-weight: bold;\n}\n\n.asciidoc .hljs-emphasis,\n.markdown .hljs-emphasis,\n.tp .hljs-units {\n font-style: italic;\n}\n\n.nginx .hljs-built_in {\n font-weight: normal;\n}\n\n.coffeescript .javascript,\n.javascript .xml,\n.lasso .markup,\n.tex .hljs-formula,\n.xml .javascript,\n.xml .vbscript,\n.xml .css,\n.xml .hljs-cdata {\n opacity: 0.5;\n}\n",
"type": "text/css",
"title": "$:/plugins/tiddlywiki/highlight/highlight.css",
"tags": "[[$:/tags/Stylesheet]]"
},
"$:/plugins/tiddlywiki/highlight/highlightblock.js": {
"text": "/*\\\ntitle: $:/plugins/tiddlywiki/highlight/highlightblock.js\ntype: application/javascript\nmodule-type: widget\n\nWraps up the fenced code blocks parser for highlight and use in TiddlyWiki5\n\n\\*/\n(function() {\n\n/*jslint node: true, browser: true */\n/*global $tw: false */\n\"use strict\";\n\nvar TYPE_MAPPINGS_BASE = \"$:/config/HighlightPlugin/TypeMappings/\";\n\nvar CodeBlockWidget = require(\"$:/core/modules/widgets/codeblock.js\").codeblock;\n\nvar hljs = require(\"$:/plugins/tiddlywiki/highlight/highlight.js\");\n\nhljs.configure({tabReplace: \" \"});\t\n\nCodeBlockWidget.prototype.postRender = function() {\n\tvar domNode = this.domNodes[0],\n\t\tlanguage = this.language,\n\t\ttiddler = this.wiki.getTiddler(TYPE_MAPPINGS_BASE + language);\n\tif(tiddler) {\n\t\tlanguage = tiddler.fields.text || \"\";\n\t}\n\tif(language && hljs.listLanguages().indexOf(language) !== -1) {\n\t\tdomNode.className = language.toLowerCase() + \" hljs\";\n\t\tif($tw.browser && !domNode.isTiddlyWikiFakeDom) {\n\t\t\thljs.highlightBlock(domNode);\t\t\t\n\t\t} else {\n\t\t\tvar text = domNode.textContent;\n\t\t\tdomNode.children[0].innerHTML = hljs.fixMarkup(hljs.highlight(language,text).value);\n\t\t\t// If we're using the fakedom then specially save the original raw text\n\t\t\tif(domNode.isTiddlyWikiFakeDom) {\n\t\t\t\tdomNode.children[0].textInnerHTML = text;\n\t\t\t}\n\t\t}\n\t}\t\n};\n\n})();\n",
"title": "$:/plugins/tiddlywiki/highlight/highlightblock.js",
"type": "application/javascript",
"module-type": "widget"
},
"$:/plugins/tiddlywiki/highlight/license": {
"title": "$:/plugins/tiddlywiki/highlight/license",
"type": "text/plain",
"text": "Copyright (c) 2006, Ivan Sagalaev\nAll rights reserved.\nRedistribution and use in source and binary forms, with or without\nmodification, are permitted provided that the following conditions are met:\n\n * Redistributions of source code must retain the above copyright\n notice, this list of conditions and the following disclaimer.\n * Redistributions in binary form must reproduce the above copyright\n notice, this list of conditions and the following disclaimer in the\n documentation and/or other materials provided with the distribution.\n * Neither the name of highlight.js nor the names of its contributors\n may be used to endorse or promote products derived from this software\n without specific prior written permission.\n\nTHIS SOFTWARE IS PROVIDED BY THE REGENTS AND CONTRIBUTORS ``AS IS'' AND ANY\nEXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\nWARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\nDISCLAIMED. IN NO EVENT SHALL THE REGENTS AND CONTRIBUTORS BE LIABLE FOR ANY\nDIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES\n(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\nLOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND\nON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT\n(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS\nSOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n"
},
"$:/plugins/tiddlywiki/highlight/readme": {
"title": "$:/plugins/tiddlywiki/highlight/readme",
"text": "This plugin provides syntax highlighting of code blocks using v8.8.0 of [[highlight.js|https://github.com/isagalaev/highlight.js]] from Ivan Sagalaev.\n\n! Usage\n\nWhen the plugin is installed it automatically applies highlighting to all codeblocks defined with triple backticks or with the CodeBlockWidget.\n\nThe language can optionally be specified after the opening triple braces:\n\n<$codeblock code=\"\"\"```css\n * { margin: 0; padding: 0; } /* micro reset */\n\nhtml { font-size: 62.5%; }\nbody { font-size: 14px; font-size: 1.4rem; } /* =14px */\nh1 { font-size: 24px; font-size: 2.4rem; } /* =24px */\n```\"\"\"/>\n\nIf no language is specified highlight.js will attempt to automatically detect the language.\n\n! Built-in Language Brushes\n\nThe plugin includes support for the following languages (referred to as \"brushes\" by highlight.js):\n\n* apache\n* bash\n* coffeescript\n* cpp\n* cs\n* css\n* diff\n* http\n* ini\n* java\n* javascript\n* json\n* makefile\n* markdown\n* nginx\n* objectivec\n* perl\n* php\n* python\n* ruby\n* sql\n* xml\n\nYou can also specify the language as a MIME content type (eg `text/html` or `text/css`). The mapping is accomplished via mapping tiddlers whose titles start with `$:/config/HighlightPlugin/TypeMappings/`.\n"
},
"$:/plugins/tiddlywiki/highlight/styles": {
"title": "$:/plugins/tiddlywiki/highlight/styles",
"tags": "[[$:/tags/Stylesheet]]",
"text": ".hljs{display:block;overflow-x:auto;padding:.5em;color:#333;background:#f8f8f8;-webkit-text-size-adjust:none}.hljs-comment,.diff .hljs-header,.hljs-javadoc{color:#998;font-style:italic}.hljs-keyword,.css .rule .hljs-keyword,.hljs-winutils,.nginx .hljs-title,.hljs-subst,.hljs-request,.hljs-status{color:#333;font-weight:bold}.hljs-number,.hljs-hexcolor,.ruby .hljs-constant{color:teal}.hljs-string,.hljs-tag .hljs-value,.hljs-phpdoc,.hljs-dartdoc,.tex .hljs-formula{color:#d14}.hljs-title,.hljs-id,.scss .hljs-preprocessor{color:#900;font-weight:bold}.hljs-list .hljs-keyword,.hljs-subst{font-weight:normal}.hljs-class .hljs-title,.hljs-type,.vhdl .hljs-literal,.tex .hljs-command{color:#458;font-weight:bold}.hljs-tag,.hljs-tag .hljs-title,.hljs-rule .hljs-property,.django .hljs-tag .hljs-keyword{color:navy;font-weight:normal}.hljs-attribute,.hljs-variable,.lisp .hljs-body,.hljs-name{color:teal}.hljs-regexp{color:#009926}.hljs-symbol,.ruby .hljs-symbol .hljs-string,.lisp .hljs-keyword,.clojure .hljs-keyword,.scheme .hljs-keyword,.tex .hljs-special,.hljs-prompt{color:#990073}.hljs-built_in{color:#0086b3}.hljs-preprocessor,.hljs-pragma,.hljs-pi,.hljs-doctype,.hljs-shebang,.hljs-cdata{color:#999;font-weight:bold}.hljs-deletion{background:#fdd}.hljs-addition{background:#dfd}.diff .hljs-change{background:#0086b3}.hljs-chunk{color:#aaa}"
},
"$:/plugins/tiddlywiki/highlight/usage": {
"title": "$:/plugins/tiddlywiki/highlight/usage",
"text": "! Usage\n\nFenced code blocks can have a language specifier added to trigger highlighting in a specific language. Otherwise heuristics are used to detect the language.\n\n```\n ```js\n var a = b + c; // Highlighted as JavaScript\n ```\n```\n! Adding Themes\n\nYou can add themes from highlight.js by copying the CSS to a new tiddler and tagging it with [[$:/tags/Stylesheet]]. The available themes can be found on GitHub:\n\nhttps://github.com/isagalaev/highlight.js/tree/master/src/styles\n"
}
}
}
/*
Orginal Style from ethanschoonover.com/solarized (c) Jeremy Hull <sourdrums@gmail.com>
*/
.hljs {
display: block;
overflow-x: auto;
padding: 0.5em;
background: #002b36;
color: #839496;
-webkit-text-size-adjust: none;
}
.hljs-comment,
.hljs-template_comment,
.diff .hljs-header,
.hljs-doctype,
.hljs-pi,
.lisp .hljs-string,
.hljs-javadoc {
color: #586e75;
}
/* Solarized Green */
.hljs-keyword,
.hljs-winutils,
.method,
.hljs-addition,
.css .hljs-tag,
.hljs-request,
.hljs-status,
.nginx .hljs-title {
color: #859900;
}
/* Solarized Cyan */
.hljs-number,
.hljs-command,
.hljs-string,
.hljs-tag .hljs-value,
.hljs-rules .hljs-value,
.hljs-phpdoc,
.hljs-dartdoc,
.tex .hljs-formula,
.hljs-regexp,
.hljs-hexcolor,
.hljs-link_url {
color: #2aa198;
}
/* Solarized Blue */
.hljs-title,
.hljs-localvars,
.hljs-chunk,
.hljs-decorator,
.hljs-built_in,
.hljs-identifier,
.vhdl .hljs-literal,
.hljs-id,
.css .hljs-function {
color: #268bd2;
}
/* Solarized Yellow */
.hljs-attribute,
.hljs-variable,
.lisp .hljs-body,
.smalltalk .hljs-number,
.hljs-constant,
.hljs-class .hljs-title,
.hljs-parent,
.hljs-type,
.hljs-link_reference {
color: #b58900;
}
/* Solarized Orange */
.hljs-preprocessor,
.hljs-preprocessor .hljs-keyword,
.hljs-pragma,
.hljs-shebang,
.hljs-symbol,
.hljs-symbol .hljs-string,
.diff .hljs-change,
.hljs-special,
.hljs-attr_selector,
.hljs-subst,
.hljs-cdata,
.css .hljs-pseudo,
.hljs-header {
color: #cb4b16;
}
/* Solarized Red */
.hljs-deletion,
.hljs-important {
color: #dc322f;
}
/* Solarized Violet */
.hljs-link_label {
color: #6c71c4;
}
.tex .hljs-formula {
background: #073642;
}
just notes
Only Diff By Const
{
"tiddlers": {
"$:/info/browser": {
"title": "$:/info/browser",
"text": "no"
},
"$:/info/node": {
"title": "$:/info/node",
"text": "yes"
}
}
}
$:/themes/tiddlywiki/vanilla
{
"tiddlers": {
"$:/themes/tiddlywiki/snowwhite/base": {
"title": "$:/themes/tiddlywiki/snowwhite/base",
"tags": "[[$:/tags/Stylesheet]]",
"text": "\\rules only filteredtranscludeinline transcludeinline macrodef macrocallinline\n\n.tc-sidebar-header {\n\ttext-shadow: 0 1px 0 <<colour sidebar-foreground-shadow>>;\n}\n\n.tc-tiddler-info {\n\t<<box-shadow \"inset 1px 2px 3px rgba(0,0,0,0.1)\">>\n}\n\n@media screen {\n\t.tc-tiddler-frame {\n\t\t<<box-shadow \"1px 1px 5px rgba(0, 0, 0, 0.3)\">>\n\t}\n}\n\n@media (max-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\t.tc-tiddler-frame {\n\t\t<<box-shadow none>>\n\t}\n}\n\n.tc-page-controls button svg, .tc-tiddler-controls button svg, .tc-topbar button svg {\n\t<<transition \"fill 150ms ease-in-out\">>\n}\n\n.tc-tiddler-controls button.tc-selected,\n.tc-page-controls button.tc-selected {\n\t<<filter \"drop-shadow(0px -1px 2px rgba(0,0,0,0.25))\">>\n}\n\n.tc-tiddler-frame input.tc-edit-texteditor {\n\t<<box-shadow \"inset 0 1px 8px rgba(0, 0, 0, 0.15)\">>\n}\n\n.tc-edit-tags {\n\t<<box-shadow \"inset 0 1px 8px rgba(0, 0, 0, 0.15)\">>\n}\n\n.tc-tiddler-frame .tc-edit-tags input.tc-edit-texteditor {\n\t<<box-shadow \"none\">>\n\tborder: none;\n\toutline: none;\n}\n\ncanvas.tc-edit-bitmapeditor {\n\t<<box-shadow \"2px 2px 5px rgba(0, 0, 0, 0.5)\">>\n}\n\n.tc-drop-down {\n\tborder-radius: 4px;\n\t<<box-shadow \"2px 2px 10px rgba(0, 0, 0, 0.5)\">>\n}\n\n.tc-block-dropdown {\n\tborder-radius: 4px;\n\t<<box-shadow \"2px 2px 10px rgba(0, 0, 0, 0.5)\">>\n}\n\n.tc-modal {\n\tborder-radius: 6px;\n\t<<box-shadow \"0 3px 7px rgba(0,0,0,0.3)\">>\n}\n\n.tc-modal-footer {\n\tborder-radius: 0 0 6px 6px;\n\t<<box-shadow \"inset 0 1px 0 #fff\">>;\n}\n\n\n.tc-alert {\n\tborder-radius: 6px;\n\t<<box-shadow \"0 3px 7px rgba(0,0,0,0.6)\">>\n}\n\n.tc-notification {\n\tborder-radius: 6px;\n\t<<box-shadow \"0 3px 7px rgba(0,0,0,0.3)\">>\n\ttext-shadow: 0 1px 0 rgba(255,255,255, 0.8);\n}\n\n.tc-sidebar-lists .tc-tab-set .tc-tab-divider {\n\tborder-top: none;\n\theight: 1px;\n\t<<background-linear-gradient \"left, rgba(0,0,0,0.15) 0%, rgba(0,0,0,0.0) 100%\">>\n}\n\n.tc-more-sidebar > .tc-tab-set > .tc-tab-buttons > button {\n\t<<background-linear-gradient \"left, rgba(0,0,0,0.01) 0%, rgba(0,0,0,0.1) 100%\">>\n}\n\n.tc-more-sidebar > .tc-tab-set > .tc-tab-buttons > button.tc-tab-selected {\n\t<<background-linear-gradient \"left, rgba(0,0,0,0.05) 0%, rgba(255,255,255,0.05) 100%\">>\n}\n\n.tc-message-box img {\n\t<<box-shadow \"1px 1px 3px rgba(0,0,0,0.5)\">>\n}\n\n.tc-plugin-info {\n\t<<box-shadow \"1px 1px 3px rgba(0,0,0,0.5)\">>\n}\n"
}
}
}
{
"tiddlers": {
"$:/themes/tiddlywiki/vanilla/themetweaks": {
"title": "$:/themes/tiddlywiki/vanilla/themetweaks",
"tags": "$:/tags/ControlPanel/Appearance",
"caption": "{{$:/language/ThemeTweaks/ThemeTweaks}}",
"text": "\\define lingo-base() $:/language/ThemeTweaks/\n\n\\define replacement-text()\n[img[$(imageTitle)$]]\n\\end\n\n\\define backgroundimage-dropdown()\n<div class=\"tc-drop-down-wrapper\">\n<$button popup=<<qualify \"$:/state/popup/themetweaks/backgroundimage\">> class=\"tc-btn-invisible tc-btn-dropdown\">{{$:/core/images/down-arrow}}</$button>\n<$reveal state=<<qualify \"$:/state/popup/themetweaks/backgroundimage\">> type=\"popup\" position=\"belowleft\" text=\"\" default=\"\">\n<div class=\"tc-drop-down\">\n<$macrocall $name=\"image-picker\" actions=\"\"\"\n\n<$action-setfield\n\t$tiddler=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimage\"\n\t$value=<<imageTitle>>\n/>\n\n\"\"\"/>\n</div>\n</$reveal>\n</div>\n\\end\n\n\\define backgroundimageattachment-dropdown()\n<$select tiddler=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment\" default=\"scroll\">\n<option value=\"scroll\"><<lingo Settings/BackgroundImageAttachment/Scroll>></option>\n<option value=\"fixed\"><<lingo Settings/BackgroundImageAttachment/Fixed>></option>\n</$select>\n\\end\n\n\\define backgroundimagesize-dropdown()\n<$select tiddler=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize\" default=\"scroll\">\n<option value=\"auto\"><<lingo Settings/BackgroundImageSize/Auto>></option>\n<option value=\"cover\"><<lingo Settings/BackgroundImageSize/Cover>></option>\n<option value=\"contain\"><<lingo Settings/BackgroundImageSize/Contain>></option>\n</$select>\n\\end\n\n<<lingo ThemeTweaks/Hint>>\n\n! <<lingo Options>>\n\n|<$link to=\"$:/themes/tiddlywiki/vanilla/options/sidebarlayout\"><<lingo Options/SidebarLayout>></$link> |<$select tiddler=\"$:/themes/tiddlywiki/vanilla/options/sidebarlayout\"><option value=\"fixed-fluid\"><<lingo Options/SidebarLayout/Fixed-Fluid>></option><option value=\"fluid-fixed\"><<lingo Options/SidebarLayout/Fluid-Fixed>></option></$select> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/options/stickytitles\"><<lingo Options/StickyTitles>></$link><br>//<<lingo Options/StickyTitles/Hint>>// |<$select tiddler=\"$:/themes/tiddlywiki/vanilla/options/stickytitles\"><option value=\"no\">{{$:/language/No}}</option><option value=\"yes\">{{$:/language/Yes}}</option></$select> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/options/codewrapping\"><<lingo Options/CodeWrapping>></$link> |<$select tiddler=\"$:/themes/tiddlywiki/vanilla/options/codewrapping\"><option value=\"pre\">{{$:/language/No}}</option><option value=\"pre-wrap\">{{$:/language/Yes}}</option></$select> |\n\n! <<lingo Settings>>\n\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/fontfamily\"><<lingo Settings/FontFamily>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/settings/fontfamily\" default=\"\" tag=\"input\"/> | |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/codefontfamily\"><<lingo Settings/CodeFontFamily>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/settings/codefontfamily\" default=\"\" tag=\"input\"/> | |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimage\"><<lingo Settings/BackgroundImage>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimage\" default=\"\" tag=\"input\"/> |<<backgroundimage-dropdown>> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment\"><<lingo Settings/BackgroundImageAttachment>></$link> |<<backgroundimageattachment-dropdown>> | |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize\"><<lingo Settings/BackgroundImageSize>></$link> |<<backgroundimagesize-dropdown>> | |\n\n! <<lingo Metrics>>\n\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/fontsize\"><<lingo Metrics/FontSize>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/fontsize\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/lineheight\"><<lingo Metrics/LineHeight>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/lineheight\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize\"><<lingo Metrics/BodyFontSize>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/bodylineheight\"><<lingo Metrics/BodyLineHeight>></$link> |<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/bodylineheight\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/storyleft\"><<lingo Metrics/StoryLeft>></$link><br>//<<lingo Metrics/StoryLeft/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/storyleft\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/storytop\"><<lingo Metrics/StoryTop>></$link><br>//<<lingo Metrics/StoryTop/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/storytop\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/storyright\"><<lingo Metrics/StoryRight>></$link><br>//<<lingo Metrics/StoryRight/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/storyright\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/storywidth\"><<lingo Metrics/StoryWidth>></$link><br>//<<lingo Metrics/StoryWidth/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/storywidth\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth\"><<lingo Metrics/TiddlerWidth>></$link><br>//<<lingo Metrics/TiddlerWidth/Hint>>//<br> |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint\"><<lingo Metrics/SidebarBreakpoint>></$link><br>//<<lingo Metrics/SidebarBreakpoint/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint\" default=\"\" tag=\"input\"/> |\n|<$link to=\"$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth\"><<lingo Metrics/SidebarWidth>></$link><br>//<<lingo Metrics/SidebarWidth/Hint>>// |^<$edit-text tiddler=\"$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth\" default=\"\" tag=\"input\"/> |\n"
},
"$:/themes/tiddlywiki/vanilla/base": {
"title": "$:/themes/tiddlywiki/vanilla/base",
"tags": "[[$:/tags/Stylesheet]]",
"text": "\\define custom-background-datauri()\n<$set name=\"background\" value={{$:/themes/tiddlywiki/vanilla/settings/backgroundimage}}>\n<$list filter=\"[<background>is[image]]\">\n`background: url(`\n<$list filter=\"[<background>!has[_canonical_uri]]\">\n<$macrocall $name=\"datauri\" title={{$:/themes/tiddlywiki/vanilla/settings/backgroundimage}}/>\n</$list>\n<$list filter=\"[<background>has[_canonical_uri]]\">\n<$view tiddler={{$:/themes/tiddlywiki/vanilla/settings/backgroundimage}} field=\"_canonical_uri\"/>\n</$list>\n`) center center;`\n`background-attachment: `{{$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment}}`;\n-webkit-background-size:` {{$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize}}`;\n-moz-background-size:` {{$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize}}`;\n-o-background-size:` {{$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize}}`;\nbackground-size:` {{$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize}}`;`\n</$list>\n</$set>\n\\end\n\n\\define if-fluid-fixed(text,hiddenSidebarText)\n<$reveal state=\"$:/themes/tiddlywiki/vanilla/options/sidebarlayout\" type=\"match\" text=\"fluid-fixed\">\n$text$\n<$reveal state=\"$:/state/sidebar\" type=\"nomatch\" text=\"yes\" default=\"yes\">\n$hiddenSidebarText$\n</$reveal>\n</$reveal>\n\\end\n\n\\define if-editor-height-fixed(then,else)\n<$reveal state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"fixed\">\n$then$\n</$reveal>\n<$reveal state=\"$:/config/TextEditor/EditorHeight/Mode\" type=\"match\" text=\"auto\">\n$else$\n</$reveal>\n\\end\n\n\\rules only filteredtranscludeinline transcludeinline macrodef macrocallinline macrocallblock\n\n/*\n** Start with the normalize CSS reset, and then belay some of its effects\n*/\n\n{{$:/themes/tiddlywiki/vanilla/reset}}\n\n*, input[type=\"search\"] {\n\tbox-sizing: border-box;\n\t-moz-box-sizing: border-box;\n\t-webkit-box-sizing: border-box;\n}\n\nhtml button {\n\tline-height: 1.2;\n\tcolor: <<colour button-foreground>>;\n\tbackground: <<colour button-background>>;\n\tborder-color: <<colour button-border>>;\n}\n\n/*\n** Basic element styles\n*/\n\nhtml {\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/fontfamily}};\n\ttext-rendering: optimizeLegibility; /* Enables kerning and ligatures etc. */\n\t-webkit-font-smoothing: antialiased;\n\t-moz-osx-font-smoothing: grayscale;\n}\n\nhtml:-webkit-full-screen {\n\tbackground-color: <<colour page-background>>;\n}\n\nbody.tc-body {\n\tfont-size: {{$:/themes/tiddlywiki/vanilla/metrics/fontsize}};\n\tline-height: {{$:/themes/tiddlywiki/vanilla/metrics/lineheight}};\n\tword-wrap: break-word;\n\t<<custom-background-datauri>>\n\tcolor: <<colour foreground>>;\n\tbackground-color: <<colour page-background>>;\n\tfill: <<colour foreground>>;\n}\n\nh1, h2, h3, h4, h5, h6 {\n\tline-height: 1.2;\n\tfont-weight: 300;\n}\n\npre {\n\tdisplay: block;\n\tpadding: 14px;\n\tmargin-top: 1em;\n\tmargin-bottom: 1em;\n\tword-break: normal;\n\tword-wrap: break-word;\n\twhite-space: {{$:/themes/tiddlywiki/vanilla/options/codewrapping}};\n\tbackground-color: <<colour pre-background>>;\n\tborder: 1px solid <<colour pre-border>>;\n\tpadding: 0 3px 2px;\n\tborder-radius: 3px;\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/codefontfamily}};\n}\n\ncode {\n\tcolor: <<colour code-foreground>>;\n\tbackground-color: <<colour code-background>>;\n\tborder: 1px solid <<colour code-border>>;\n\twhite-space: {{$:/themes/tiddlywiki/vanilla/options/codewrapping}};\n\tpadding: 0 3px 2px;\n\tborder-radius: 3px;\n\tfont-family: {{$:/themes/tiddlywiki/vanilla/settings/codefontfamily}};\n}\n\nblockquote {\n\tborder-left: 5px solid <<colour blockquote-bar>>;\n\tmargin-left: 25px;\n\tpadding-left: 10px;\n\tquotes: \"\\201C\"\"\\201D\"\"\\2018\"\"\\2019\";\n}\n\nblockquote.tc-big-quote {\n\tfont-family: Georgia, serif;\n\tposition: relative;\n\tbackground: <<colour pre-background>>;\n\tborder-left: none;\n\tmargin-left: 50px;\n\tmargin-right: 50px;\n\tpadding: 10px;\n border-radius: 8px;\n}\n\nblockquote.tc-big-quote cite:before {\n\tcontent: \"\\2014 \\2009\";\n}\n\nblockquote.tc-big-quote:before {\n\tfont-family: Georgia, serif;\n\tcolor: <<colour blockquote-bar>>;\n\tcontent: open-quote;\n\tfont-size: 8em;\n\tline-height: 0.1em;\n\tmargin-right: 0.25em;\n\tvertical-align: -0.4em;\n\tposition: absolute;\n left: -50px;\n top: 42px;\n}\n\nblockquote.tc-big-quote:after {\n\tfont-family: Georgia, serif;\n\tcolor: <<colour blockquote-bar>>;\n\tcontent: close-quote;\n\tfont-size: 8em;\n\tline-height: 0.1em;\n\tmargin-right: 0.25em;\n\tvertical-align: -0.4em;\n\tposition: absolute;\n right: -80px;\n bottom: -20px;\n}\n\ndl dt {\n\tfont-weight: bold;\n\tmargin-top: 6px;\n}\n\ntextarea,\ninput[type=text],\ninput[type=search],\ninput[type=\"\"],\ninput:not([type]) {\n\tcolor: <<colour foreground>>;\n\tbackground: <<colour background>>;\n}\n\n.tc-muted {\n\tcolor: <<colour muted-foreground>>;\n}\n\nsvg.tc-image-button {\n\tpadding: 0px 1px 1px 0px;\n}\n\n.tc-icon-wrapper > svg {\n\twidth: 1em;\n\theight: 1em;\n}\n\nkbd {\n\tdisplay: inline-block;\n\tpadding: 3px 5px;\n\tfont-size: 0.8em;\n\tline-height: 1.2;\n\tcolor: <<colour foreground>>;\n\tvertical-align: middle;\n\tbackground-color: <<colour background>>;\n\tborder: solid 1px <<colour muted-foreground>>;\n\tborder-bottom-color: <<colour muted-foreground>>;\n\tborder-radius: 3px;\n\tbox-shadow: inset 0 -1px 0 <<colour muted-foreground>>;\n}\n\n/*\nMarkdown likes putting code elements inside pre elements\n*/\npre > code {\n\tpadding: 0;\n\tborder: none;\n\tbackground-color: inherit;\n\tcolor: inherit;\n}\n\ntable {\n\tborder: 1px solid <<colour table-border>>;\n\twidth: auto;\n\tmax-width: 100%;\n\tcaption-side: bottom;\n\tmargin-top: 1em;\n\tmargin-bottom: 1em;\n}\n\ntable th, table td {\n\tpadding: 0 7px 0 7px;\n\tborder-top: 1px solid <<colour table-border>>;\n\tborder-left: 1px solid <<colour table-border>>;\n}\n\ntable thead tr td, table th {\n\tbackground-color: <<colour table-header-background>>;\n\tfont-weight: bold;\n}\n\ntable tfoot tr td {\n\tbackground-color: <<colour table-footer-background>>;\n}\n\n.tc-csv-table {\n\twhite-space: nowrap;\n}\n\n.tc-tiddler-frame img,\n.tc-tiddler-frame svg,\n.tc-tiddler-frame canvas,\n.tc-tiddler-frame embed,\n.tc-tiddler-frame iframe {\n\tmax-width: 100%;\n}\n\n.tc-tiddler-body > embed,\n.tc-tiddler-body > iframe {\n\twidth: 100%;\n\theight: 600px;\n}\n\n/*\n** Links\n*/\n\nbutton.tc-tiddlylink,\na.tc-tiddlylink {\n\ttext-decoration: none;\n\tfont-weight: normal;\n\tcolor: <<colour tiddler-link-foreground>>;\n\t-webkit-user-select: inherit; /* Otherwise the draggable attribute makes links impossible to select */\n}\n\n.tc-sidebar-lists a.tc-tiddlylink {\n\tcolor: <<colour sidebar-tiddler-link-foreground>>;\n}\n\n.tc-sidebar-lists a.tc-tiddlylink:hover {\n\tcolor: <<colour sidebar-tiddler-link-foreground-hover>>;\n}\n\nbutton.tc-tiddlylink:hover,\na.tc-tiddlylink:hover {\n\ttext-decoration: underline;\n}\n\na.tc-tiddlylink-resolves {\n}\n\na.tc-tiddlylink-shadow {\n\tfont-weight: bold;\n}\n\na.tc-tiddlylink-shadow.tc-tiddlylink-resolves {\n\tfont-weight: normal;\n}\n\na.tc-tiddlylink-missing {\n\tfont-style: italic;\n}\n\na.tc-tiddlylink-external {\n\ttext-decoration: underline;\n\tcolor: <<colour external-link-foreground>>;\n\tbackground-color: <<colour external-link-background>>;\n}\n\na.tc-tiddlylink-external:visited {\n\tcolor: <<colour external-link-foreground-visited>>;\n\tbackground-color: <<colour external-link-background-visited>>;\n}\n\na.tc-tiddlylink-external:hover {\n\tcolor: <<colour external-link-foreground-hover>>;\n\tbackground-color: <<colour external-link-background-hover>>;\n}\n\n/*\n** Drag and drop styles\n*/\n\n.tc-tiddler-dragger {\n\tposition: relative;\n\tz-index: -10000;\n}\n\n.tc-tiddler-dragger-inner {\n\tposition: absolute;\n\ttop: -1000px;\n\tleft: -1000px;\n\tdisplay: inline-block;\n\tpadding: 8px 20px;\n\tfont-size: 16.9px;\n\tfont-weight: bold;\n\tline-height: 20px;\n\tcolor: <<colour dragger-foreground>>;\n\ttext-shadow: 0 1px 0 rgba(0, 0, 0, 1);\n\twhite-space: nowrap;\n\tvertical-align: baseline;\n\tbackground-color: <<colour dragger-background>>;\n\tborder-radius: 20px;\n}\n\n.tc-tiddler-dragger-cover {\n\tposition: absolute;\n\tbackground-color: <<colour page-background>>;\n}\n\n.tc-dropzone {\n\tposition: relative;\n}\n\n.tc-dropzone.tc-dragover:before {\n\tz-index: 10000;\n\tdisplay: block;\n\tposition: fixed;\n\ttop: 0;\n\tleft: 0;\n\tright: 0;\n\tbackground: <<colour dropzone-background>>;\n\ttext-align: center;\n\tcontent: \"<<lingo DropMessage>>\";\n}\n\n.tc-droppable > .tc-droppable-placeholder {\n\tdisplay: none;\n}\n\n.tc-droppable.tc-dragover > .tc-droppable-placeholder {\n\tdisplay: block;\n\tborder: 2px dashed <<colour dropzone-background>>;\n}\n\n.tc-draggable {\n\tcursor: move;\n}\n\n/*\n** Plugin reload warning\n*/\n\n.tc-plugin-reload-warning {\n\tz-index: 1000;\n\tdisplay: block;\n\tposition: fixed;\n\ttop: 0;\n\tleft: 0;\n\tright: 0;\n\tbackground: <<colour alert-background>>;\n\ttext-align: center;\n}\n\n/*\n** Buttons\n*/\n\nbutton svg, button img, label svg, label img {\n\tvertical-align: middle;\n}\n\n.tc-btn-invisible {\n\tpadding: 0;\n\tmargin: 0;\n\tbackground: none;\n\tborder: none;\n cursor: pointer;\n}\n\n.tc-btn-boxed {\n\tfont-size: 0.6em;\n\tpadding: 0.2em;\n\tmargin: 1px;\n\tbackground: none;\n\tborder: 1px solid <<colour tiddler-controls-foreground>>;\n\tborder-radius: 0.25em;\n}\n\nhtml body.tc-body .tc-btn-boxed svg {\n\tfont-size: 1.6666em;\n}\n\n.tc-btn-boxed:hover {\n\tbackground: <<colour muted-foreground>>;\n\tcolor: <<colour background>>;\n}\n\nhtml body.tc-body .tc-btn-boxed:hover svg {\n\tfill: <<colour background>>;\n}\n\n.tc-btn-rounded {\n\tfont-size: 0.5em;\n\tline-height: 2;\n\tpadding: 0em 0.3em 0.2em 0.4em;\n\tmargin: 1px;\n\tborder: 1px solid <<colour muted-foreground>>;\n\tbackground: <<colour muted-foreground>>;\n\tcolor: <<colour background>>;\n\tborder-radius: 2em;\n}\n\nhtml body.tc-body .tc-btn-rounded svg {\n\tfont-size: 1.6666em;\n\tfill: <<colour background>>;\n}\n\n.tc-btn-rounded:hover {\n\tborder: 1px solid <<colour muted-foreground>>;\n\tbackground: <<colour background>>;\n\tcolor: <<colour muted-foreground>>;\n}\n\nhtml body.tc-body .tc-btn-rounded:hover svg {\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-btn-icon svg {\n\theight: 1em;\n\twidth: 1em;\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-btn-text {\n\tpadding: 0;\n\tmargin: 0;\n}\n\n.tc-btn-big-green {\n\tdisplay: inline-block;\n\tpadding: 8px;\n\tmargin: 4px 8px 4px 8px;\n\tbackground: <<colour download-background>>;\n\tcolor: <<colour download-foreground>>;\n\tfill: <<colour download-foreground>>;\n\tborder: none;\n\tfont-size: 1.2em;\n\tline-height: 1.4em;\n\ttext-decoration: none;\n}\n\n.tc-btn-big-green svg,\n.tc-btn-big-green img {\n\theight: 2em;\n\twidth: 2em;\n\tvertical-align: middle;\n\tfill: <<colour download-foreground>>;\n}\n\n.tc-sidebar-lists input {\n\tcolor: <<colour foreground>>;\n}\n\n.tc-sidebar-lists button {\n\tcolor: <<colour sidebar-button-foreground>>;\n\tfill: <<colour sidebar-button-foreground>>;\n}\n\n.tc-sidebar-lists button.tc-btn-mini {\n\tcolor: <<colour sidebar-muted-foreground>>;\n}\n\n.tc-sidebar-lists button.tc-btn-mini:hover {\n\tcolor: <<colour sidebar-muted-foreground-hover>>;\n}\n\nbutton svg.tc-image-button, button .tc-image-button img {\n\theight: 1em;\n\twidth: 1em;\n}\n\n.tc-unfold-banner {\n\tposition: absolute;\n\tpadding: 0;\n\tmargin: 0;\n\tbackground: none;\n\tborder: none;\n\twidth: 100%;\n\twidth: calc(100% + 2px);\n\tmargin-left: -43px;\n\ttext-align: center;\n\tborder-top: 2px solid <<colour tiddler-info-background>>;\n\tmargin-top: 4px;\n}\n\n.tc-unfold-banner:hover {\n\tbackground: <<colour tiddler-info-background>>;\n\tborder-top: 2px solid <<colour tiddler-info-border>>;\n}\n\n.tc-unfold-banner svg, .tc-fold-banner svg {\n\theight: 0.75em;\n\tfill: <<colour tiddler-controls-foreground>>;\n}\n\n.tc-unfold-banner:hover svg, .tc-fold-banner:hover svg {\n\tfill: <<colour tiddler-controls-foreground-hover>>;\n}\n\n.tc-fold-banner {\n\tposition: absolute;\n\tpadding: 0;\n\tmargin: 0;\n\tbackground: none;\n\tborder: none;\n\twidth: 23px;\n\ttext-align: center;\n\tmargin-left: -35px;\n\ttop: 6px;\n\tbottom: 6px;\n}\n\n.tc-fold-banner:hover {\n\tbackground: <<colour tiddler-info-background>>;\n}\n\n@media (max-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-unfold-banner {\n\t\tposition: static;\n\t\twidth: calc(100% + 59px);\n\t}\n\n\t.tc-fold-banner {\n\t\twidth: 16px;\n\t\tmargin-left: -16px;\n\t\tfont-size: 0.75em;\n\t}\n\n}\n\n/*\n** Tags and missing tiddlers\n*/\n\n.tc-tag-list-item {\n\tposition: relative;\n\tdisplay: inline-block;\n\tmargin-right: 7px;\n}\n\n.tc-tags-wrapper {\n\tmargin: 4px 0 14px 0;\n}\n\n.tc-missing-tiddler-label {\n\tfont-style: italic;\n\tfont-weight: normal;\n\tdisplay: inline-block;\n\tfont-size: 11.844px;\n\tline-height: 14px;\n\twhite-space: nowrap;\n\tvertical-align: baseline;\n}\n\nbutton.tc-tag-label, span.tc-tag-label {\n\tdisplay: inline-block;\n\tpadding: 0.16em 0.7em;\n\tfont-size: 0.9em;\n\tfont-weight: 400;\n\tline-height: 1.2em;\n\tcolor: <<colour tag-foreground>>;\n\twhite-space: nowrap;\n\tvertical-align: baseline;\n\tbackground-color: <<colour tag-background>>;\n\tborder-radius: 1em;\n}\n\n.tc-untagged-separator {\n\twidth: 10em;\n\tleft: 0;\n\tmargin-left: 0;\n\tborder: 0;\n\theight: 1px;\n\tbackground: <<colour tab-divider>>;\n}\n\nbutton.tc-untagged-label {\n\tbackground-color: <<colour untagged-background>>;\n}\n\n.tc-tag-label svg, .tc-tag-label img {\n\theight: 1em;\n\twidth: 1em;\n\tfill: <<colour tag-foreground>>;\n\tvertical-align: text-bottom;\n}\n\n.tc-tag-manager-table .tc-tag-label {\n\twhite-space: normal;\n}\n\n.tc-tag-manager-tag {\n\twidth: 100%;\n}\n\n/*\n** Page layout\n*/\n\n.tc-topbar {\n\tposition: fixed;\n\tz-index: 1200;\n}\n\n.tc-topbar-left {\n\tleft: 29px;\n\ttop: 5px;\n}\n\n.tc-topbar-right {\n\ttop: 5px;\n\tright: 29px;\n}\n\n.tc-topbar button {\n\tpadding: 8px;\n}\n\n.tc-topbar svg {\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-topbar button:hover svg {\n\tfill: <<colour foreground>>;\n}\n\n.tc-sidebar-header {\n\tcolor: <<colour sidebar-foreground>>;\n\tfill: <<colour sidebar-foreground>>;\n}\n\n.tc-sidebar-header .tc-title a.tc-tiddlylink-resolves {\n\tfont-weight: 300;\n}\n\n.tc-sidebar-header .tc-sidebar-lists p {\n\tmargin-top: 3px;\n\tmargin-bottom: 3px;\n}\n\n.tc-sidebar-header .tc-missing-tiddler-label {\n\tcolor: <<colour sidebar-foreground>>;\n}\n\n.tc-advanced-search input {\n\twidth: 60%;\n}\n\n.tc-search a svg {\n\twidth: 1.2em;\n\theight: 1.2em;\n\tvertical-align: middle;\n}\n\n.tc-page-controls {\n\tmargin-top: 14px;\n\tfont-size: 1.5em;\n}\n\n.tc-page-controls button {\n\tmargin-right: 0.5em;\n}\n\n.tc-page-controls a.tc-tiddlylink:hover {\n\ttext-decoration: none;\n}\n\n.tc-page-controls img {\n\twidth: 1em;\n}\n\n.tc-page-controls svg {\n\tfill: <<colour sidebar-controls-foreground>>;\n}\n\n.tc-page-controls button:hover svg, .tc-page-controls a:hover svg {\n\tfill: <<colour sidebar-controls-foreground-hover>>;\n}\n\n.tc-menu-list-item {\n\twhite-space: nowrap;\n}\n\n.tc-menu-list-count {\n\tfont-weight: bold;\n}\n\n.tc-menu-list-subitem {\n\tpadding-left: 7px;\n}\n\n.tc-story-river {\n\tposition: relative;\n}\n\n@media (max-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-sidebar-header {\n\t\tpadding: 14px;\n\t\tmin-height: 32px;\n\t\tmargin-top: {{$:/themes/tiddlywiki/vanilla/metrics/storytop}};\n\t}\n\n\t.tc-story-river {\n\t\tposition: relative;\n\t\tpadding: 0;\n\t}\n}\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-message-box {\n\t\tmargin: 21px -21px 21px -21px;\n\t}\n\n\t.tc-sidebar-scrollable {\n\t\tposition: fixed;\n\t\ttop: {{$:/themes/tiddlywiki/vanilla/metrics/storytop}};\n\t\tleft: {{$:/themes/tiddlywiki/vanilla/metrics/storyright}};\n\t\tbottom: 0;\n\t\tright: 0;\n\t\toverflow-y: auto;\n\t\toverflow-x: auto;\n\t\t-webkit-overflow-scrolling: touch;\n\t\tmargin: 0 0 0 -42px;\n\t\tpadding: 71px 0 28px 42px;\n\t}\n\n\thtml[dir=\"rtl\"] .tc-sidebar-scrollable {\n\t\tleft: auto;\n\t\tright: {{$:/themes/tiddlywiki/vanilla/metrics/storyright}};\n\t}\n\n\t.tc-story-river {\n\t\tposition: relative;\n\t\tleft: {{$:/themes/tiddlywiki/vanilla/metrics/storyleft}};\n\t\ttop: {{$:/themes/tiddlywiki/vanilla/metrics/storytop}};\n\t\twidth: {{$:/themes/tiddlywiki/vanilla/metrics/storywidth}};\n\t\tpadding: 42px 42px 42px 42px;\n\t}\n\n<<if-no-sidebar \"\n\n\t.tc-story-river {\n\t\twidth: calc(100% - {{$:/themes/tiddlywiki/vanilla/metrics/storyleft}});\n\t}\n\n\">>\n\n}\n\n@media print {\n\n\tbody.tc-body {\n\t\tbackground-color: transparent;\n\t}\n\n\t.tc-sidebar-header, .tc-topbar {\n\t\tdisplay: none;\n\t}\n\n\t.tc-story-river {\n\t\tmargin: 0;\n\t\tpadding: 0;\n\t}\n\n\t.tc-story-river .tc-tiddler-frame {\n\t\tmargin: 0;\n\t\tborder: none;\n\t\tpadding: 0;\n\t}\n}\n\n/*\n** Tiddler styles\n*/\n\n.tc-tiddler-frame {\n\tposition: relative;\n\tmargin-bottom: 28px;\n\tbackground-color: <<colour tiddler-background>>;\n\tborder: 1px solid <<colour tiddler-border>>;\n}\n\n{{$:/themes/tiddlywiki/vanilla/sticky}}\n\n.tc-tiddler-info {\n\tpadding: 14px 42px 14px 42px;\n\tbackground-color: <<colour tiddler-info-background>>;\n\tborder-top: 1px solid <<colour tiddler-info-border>>;\n\tborder-bottom: 1px solid <<colour tiddler-info-border>>;\n}\n\n.tc-tiddler-info p {\n\tmargin-top: 3px;\n\tmargin-bottom: 3px;\n}\n\n.tc-tiddler-info .tc-tab-buttons button.tc-tab-selected {\n\tbackground-color: <<colour tiddler-info-tab-background>>;\n\tborder-bottom: 1px solid <<colour tiddler-info-tab-background>>;\n}\n\n.tc-view-field-table {\n\twidth: 100%;\n}\n\n.tc-view-field-name {\n\twidth: 1%; /* Makes this column be as narrow as possible */\n\ttext-align: right;\n\tfont-style: italic;\n\tfont-weight: 200;\n}\n\n.tc-view-field-value {\n}\n\n@media (max-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\t.tc-tiddler-frame {\n\t\tpadding: 14px 14px 14px 14px;\n\t}\n\n\t.tc-tiddler-info {\n\t\tmargin: 0 -14px 0 -14px;\n\t}\n}\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\t.tc-tiddler-frame {\n\t\tpadding: 28px 42px 42px 42px;\n\t\twidth: {{$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth}};\n\t\tborder-radius: 2px;\n\t}\n\n<<if-no-sidebar \"\n\n\t.tc-tiddler-frame {\n\t\twidth: 100%;\n\t}\n\n\">>\n\n\t.tc-tiddler-info {\n\t\tmargin: 0 -42px 0 -42px;\n\t}\n}\n\n.tc-site-title,\n.tc-titlebar {\n\tfont-weight: 300;\n\tfont-size: 2.35em;\n\tline-height: 1.2em;\n\tcolor: <<colour tiddler-title-foreground>>;\n\tmargin: 0;\n}\n\n.tc-site-title {\n\tcolor: <<colour site-title-foreground>>;\n}\n\n.tc-tiddler-title-icon {\n\tvertical-align: middle;\n}\n\n.tc-system-title-prefix {\n\tcolor: <<colour muted-foreground>>;\n}\n\n.tc-titlebar h2 {\n\tfont-size: 1em;\n\tdisplay: inline;\n}\n\n.tc-titlebar img {\n\theight: 1em;\n}\n\n.tc-subtitle {\n\tfont-size: 0.9em;\n\tcolor: <<colour tiddler-subtitle-foreground>>;\n\tfont-weight: 300;\n}\n\n.tc-tiddler-missing .tc-title {\n font-style: italic;\n font-weight: normal;\n}\n\n.tc-tiddler-frame .tc-tiddler-controls {\n\tfloat: right;\n}\n\n.tc-tiddler-controls .tc-drop-down {\n\tfont-size: 0.6em;\n}\n\n.tc-tiddler-controls .tc-drop-down .tc-drop-down {\n\tfont-size: 1em;\n}\n\n.tc-tiddler-controls > span > button {\n\tvertical-align: baseline;\n\tmargin-left:5px;\n}\n\n.tc-tiddler-controls button svg, .tc-tiddler-controls button img,\n.tc-search button svg, .tc-search a svg {\n\tfill: <<colour tiddler-controls-foreground>>;\n}\n\n.tc-tiddler-controls button svg, .tc-tiddler-controls button img {\n\theight: 0.75em;\n}\n\n.tc-search button svg, .tc-search a svg {\n height: 1.2em;\n width: 1.2em;\n margin: 0 0.25em;\n}\n\n.tc-tiddler-controls button.tc-selected svg,\n.tc-page-controls button.tc-selected svg {\n\tfill: <<colour tiddler-controls-foreground-selected>>;\n}\n\n.tc-tiddler-controls button.tc-btn-invisible:hover svg,\n.tc-search button:hover svg, .tc-search a:hover svg {\n\tfill: <<colour tiddler-controls-foreground-hover>>;\n}\n\n@media print {\n\t.tc-tiddler-controls {\n\t\tdisplay: none;\n\t}\n}\n\n.tc-tiddler-help { /* Help prompts within tiddler template */\n\tcolor: <<colour muted-foreground>>;\n\tmargin-top: 14px;\n}\n\n.tc-tiddler-help a.tc-tiddlylink {\n\tcolor: <<colour very-muted-foreground>>;\n}\n\n.tc-tiddler-frame .tc-edit-texteditor {\n\twidth: 100%;\n\tmargin: 4px 0 4px 0;\n}\n\n.tc-tiddler-frame input.tc-edit-texteditor,\n.tc-tiddler-frame textarea.tc-edit-texteditor,\n.tc-tiddler-frame iframe.tc-edit-texteditor {\n\tpadding: 3px 3px 3px 3px;\n\tborder: 1px solid <<colour tiddler-editor-border>>;\n\tbackground-color: <<colour tiddler-editor-background>>;\n\tline-height: 1.3em;\n\t-webkit-appearance: none;\n}\n\n.tc-tiddler-frame .tc-binary-warning {\n\twidth: 100%;\n\theight: 5em;\n\ttext-align: center;\n\tpadding: 3em 3em 6em 3em;\n\tbackground: <<colour alert-background>>;\n\tborder: 1px solid <<colour alert-border>>;\n}\n\ncanvas.tc-edit-bitmapeditor {\n\tborder: 6px solid <<colour tiddler-editor-border-image>>;\n\tcursor: crosshair;\n\t-moz-user-select: none;\n\t-webkit-user-select: none;\n\t-ms-user-select: none;\n\tmargin-top: 6px;\n\tmargin-bottom: 6px;\n}\n\n.tc-edit-bitmapeditor-width {\n\tdisplay: block;\n}\n\n.tc-edit-bitmapeditor-height {\n\tdisplay: block;\n}\n\n.tc-tiddler-body {\n\tclear: both;\n}\n\n.tc-tiddler-frame .tc-tiddler-body {\n\tfont-size: {{$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize}};\n\tline-height: {{$:/themes/tiddlywiki/vanilla/metrics/bodylineheight}};\n}\n\n.tc-titlebar, .tc-tiddler-edit-title {\n\toverflow: hidden; /* https://github.com/Jermolene/TiddlyWiki5/issues/282 */\n}\n\nhtml body.tc-body.tc-single-tiddler-window {\n\tmargin: 1em;\n\tbackground: <<colour tiddler-background>>;\n}\n\n.tc-single-tiddler-window img,\n.tc-single-tiddler-window svg,\n.tc-single-tiddler-window canvas,\n.tc-single-tiddler-window embed,\n.tc-single-tiddler-window iframe {\n\tmax-width: 100%;\n}\n\n/*\n** Editor\n*/\n\n.tc-editor-toolbar {\n\tmargin-top: 8px;\n}\n\n.tc-editor-toolbar button {\n\tvertical-align: middle;\n\tbackground-color: <<colour tiddler-controls-foreground>>;\n\tfill: <<colour tiddler-controls-foreground-selected>>;\n\tborder-radius: 4px;\n\tpadding: 3px;\n\tmargin: 2px 0 2px 4px;\n}\n\n.tc-editor-toolbar button.tc-text-editor-toolbar-item-adjunct {\n\tmargin-left: 1px;\n\twidth: 1em;\n\tborder-radius: 8px;\n}\n\n.tc-editor-toolbar button.tc-text-editor-toolbar-item-start-group {\n\tmargin-left: 11px;\n}\n\n.tc-editor-toolbar button.tc-selected {\n\tbackground-color: <<colour primary>>;\n}\n\n.tc-editor-toolbar button svg {\n\twidth: 1.6em;\n\theight: 1.2em;\n}\n\n.tc-editor-toolbar button:hover {\n\tbackground-color: <<colour tiddler-controls-foreground-selected>>;\n\tfill: <<colour background>>;\n}\n\n.tc-editor-toolbar .tc-text-editor-toolbar-more {\n\twhite-space: normal;\n}\n\n.tc-editor-toolbar .tc-text-editor-toolbar-more button {\n\tdisplay: inline-block;\n\tpadding: 3px;\n\twidth: auto;\n}\n\n.tc-editor-toolbar .tc-search-results {\n\tpadding: 0;\n}\n\n/*\n** Adjustments for fluid-fixed mode\n*/\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n<<if-fluid-fixed text:\"\"\"\n\n\t.tc-story-river {\n\t\tpadding-right: 0;\n\t\tposition: relative;\n\t\twidth: auto;\n\t\tleft: 0;\n\t\tmargin-left: {{$:/themes/tiddlywiki/vanilla/metrics/storyleft}};\n\t\tmargin-right: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth}};\n\t}\n\n\t.tc-tiddler-frame {\n\t\twidth: 100%;\n\t}\n\n\t.tc-sidebar-scrollable {\n\t\tleft: auto;\n\t\tbottom: 0;\n\t\tright: 0;\n\t\twidth: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth}};\n\t}\n\n\tbody.tc-body .tc-storyview-zoomin-tiddler {\n\t\twidth: 100%;\n\t\twidth: calc(100% - 42px);\n\t}\n\n\"\"\" hiddenSidebarText:\"\"\"\n\n\t.tc-story-river {\n\t\tpadding-right: 3em;\n\t\tmargin-right: 0;\n\t}\n\n\tbody.tc-body .tc-storyview-zoomin-tiddler {\n\t\twidth: 100%;\n\t\twidth: calc(100% - 84px);\n\t}\n\n\"\"\">>\n\n}\n\n/*\n** Toolbar buttons\n*/\n\n.tc-page-controls svg.tc-image-new-button {\n fill: <<colour toolbar-new-button>>;\n}\n\n.tc-page-controls svg.tc-image-options-button {\n fill: <<colour toolbar-options-button>>;\n}\n\n.tc-page-controls svg.tc-image-save-button {\n fill: <<colour toolbar-save-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-info-button {\n fill: <<colour toolbar-info-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-edit-button {\n fill: <<colour toolbar-edit-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-close-button {\n fill: <<colour toolbar-close-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-delete-button {\n fill: <<colour toolbar-delete-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-cancel-button {\n fill: <<colour toolbar-cancel-button>>;\n}\n\n.tc-tiddler-controls button svg.tc-image-done-button {\n fill: <<colour toolbar-done-button>>;\n}\n\n/*\n** Tiddler edit mode\n*/\n\n.tc-tiddler-edit-frame em.tc-edit {\n\tcolor: <<colour muted-foreground>>;\n\tfont-style: normal;\n}\n\n.tc-edit-type-dropdown a.tc-tiddlylink-missing {\n\tfont-style: normal;\n}\n\n.tc-edit-tags {\n\tborder: 1px solid <<colour tiddler-editor-border>>;\n\tpadding: 4px 8px 4px 8px;\n}\n\n.tc-edit-add-tag {\n\tdisplay: inline-block;\n}\n\n.tc-edit-add-tag .tc-add-tag-name input {\n\twidth: 50%;\n}\n\n.tc-edit-add-tag .tc-keyboard {\n\tdisplay:inline;\n}\n\n.tc-edit-tags .tc-tag-label {\n\tdisplay: inline-block;\n}\n\n.tc-edit-tags-list {\n\tmargin: 14px 0 14px 0;\n}\n\n.tc-remove-tag-button {\n\tpadding-left: 4px;\n}\n\n.tc-tiddler-preview {\n\toverflow: auto;\n}\n\n.tc-tiddler-preview-preview {\n\tfloat: right;\n\twidth: 49%;\n\tborder: 1px solid <<colour tiddler-editor-border>>;\n\tmargin: 4px 0 3px 3px;\n\tpadding: 3px 3px 3px 3px;\n}\n\n<<if-editor-height-fixed then:\"\"\"\n\n.tc-tiddler-preview-preview {\n\toverflow-y: scroll;\n\theight: {{$:/config/TextEditor/EditorHeight/Height}};\n}\n\n\"\"\">>\n\n.tc-tiddler-frame .tc-tiddler-preview .tc-edit-texteditor {\n\twidth: 49%;\n}\n\n.tc-tiddler-frame .tc-tiddler-preview canvas.tc-edit-bitmapeditor {\n\tmax-width: 49%;\n}\n\n.tc-edit-fields {\n\twidth: 100%;\n}\n\n\n.tc-edit-fields table, .tc-edit-fields tr, .tc-edit-fields td {\n\tborder: none;\n\tpadding: 4px;\n}\n\n.tc-edit-fields > tbody > .tc-edit-field:nth-child(odd) {\n\tbackground-color: <<colour tiddler-editor-fields-odd>>;\n}\n\n.tc-edit-fields > tbody > .tc-edit-field:nth-child(even) {\n\tbackground-color: <<colour tiddler-editor-fields-even>>;\n}\n\n.tc-edit-field-name {\n\ttext-align: right;\n}\n\n.tc-edit-field-value input {\n\twidth: 100%;\n}\n\n.tc-edit-field-remove {\n}\n\n.tc-edit-field-remove svg {\n\theight: 1em;\n\twidth: 1em;\n\tfill: <<colour muted-foreground>>;\n\tvertical-align: middle;\n}\n\n.tc-edit-field-add-name {\n\tdisplay: inline-block;\n\twidth: 15%;\n}\n\n.tc-edit-field-add-value {\n\tdisplay: inline-block;\n\twidth: 40%;\n}\n\n.tc-edit-field-add-button {\n\tdisplay: inline-block;\n\twidth: 10%;\n}\n\n/*\n** Storyview Classes\n*/\n\n.tc-storyview-zoomin-tiddler {\n\tposition: absolute;\n\tdisplay: block;\n\twidth: 100%;\n}\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-storyview-zoomin-tiddler {\n\t\twidth: calc(100% - 84px);\n\t}\n\n}\n\n/*\n** Dropdowns\n*/\n\n.tc-btn-dropdown {\n\ttext-align: left;\n}\n\n.tc-btn-dropdown svg, .tc-btn-dropdown img {\n\theight: 1em;\n\twidth: 1em;\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-drop-down-wrapper {\n\tposition: relative;\n}\n\n.tc-drop-down {\n\tmin-width: 380px;\n\tborder: 1px solid <<colour dropdown-border>>;\n\tbackground-color: <<colour dropdown-background>>;\n\tpadding: 7px 0 7px 0;\n\tmargin: 4px 0 0 0;\n\twhite-space: nowrap;\n\ttext-shadow: none;\n\tline-height: 1.4;\n}\n\n.tc-drop-down .tc-drop-down {\n\tmargin-left: 14px;\n}\n\n.tc-drop-down button svg, .tc-drop-down a svg {\n\tfill: <<colour foreground>>;\n}\n\n.tc-drop-down button.tc-btn-invisible:hover svg {\n\tfill: <<colour foreground>>;\n}\n\n.tc-drop-down p {\n\tpadding: 0 14px 0 14px;\n}\n\n.tc-drop-down svg {\n\twidth: 1em;\n\theight: 1em;\n}\n\n.tc-drop-down img {\n\twidth: 1em;\n}\n\n.tc-drop-down-language-chooser img {\n\twidth: 2em;\n\tvertical-align: baseline;\n}\n\n.tc-drop-down a, .tc-drop-down button {\n\tdisplay: block;\n\tpadding: 0 14px 0 14px;\n\twidth: 100%;\n\ttext-align: left;\n\tcolor: <<colour foreground>>;\n\tline-height: 1.4;\n}\n\n.tc-drop-down .tc-tab-set .tc-tab-buttons button {\n\tdisplay: inline-block;\n width: auto;\n margin-bottom: 0px;\n border-bottom-left-radius: 0;\n border-bottom-right-radius: 0;\n}\n\n.tc-drop-down .tc-prompt {\n\tpadding: 0 14px;\n}\n\n.tc-drop-down .tc-chooser {\n\tborder: none;\n}\n\n.tc-drop-down .tc-chooser .tc-swatches-horiz {\n\tfont-size: 0.4em;\n\tpadding-left: 1.2em;\n}\n\n.tc-drop-down .tc-file-input-wrapper {\n\twidth: 100%;\n}\n\n.tc-drop-down .tc-file-input-wrapper button {\n\tcolor: <<colour foreground>>;\n}\n\n.tc-drop-down a:hover, .tc-drop-down button:hover, .tc-drop-down .tc-file-input-wrapper:hover button {\n\tcolor: <<colour tiddler-link-background>>;\n\tbackground-color: <<colour tiddler-link-foreground>>;\n\ttext-decoration: none;\n}\n\n.tc-drop-down .tc-tab-buttons button {\n\tbackground-color: <<colour dropdown-tab-background>>;\n}\n\n.tc-drop-down .tc-tab-buttons button.tc-tab-selected {\n\tbackground-color: <<colour dropdown-tab-background-selected>>;\n\tborder-bottom: 1px solid <<colour dropdown-tab-background-selected>>;\n}\n\n.tc-drop-down-bullet {\n\tdisplay: inline-block;\n\twidth: 0.5em;\n}\n\n.tc-drop-down .tc-tab-contents a {\n\tpadding: 0 0.5em 0 0.5em;\n}\n\n.tc-block-dropdown-wrapper {\n\tposition: relative;\n}\n\n.tc-block-dropdown {\n\tposition: absolute;\n\tmin-width: 220px;\n\tborder: 1px solid <<colour dropdown-border>>;\n\tbackground-color: <<colour dropdown-background>>;\n\tpadding: 7px 0;\n\tmargin: 4px 0 0 0;\n\twhite-space: nowrap;\n\tz-index: 1000;\n\ttext-shadow: none;\n}\n\n.tc-block-dropdown.tc-search-drop-down {\n\tmargin-left: -12px;\n}\n\n.tc-block-dropdown a {\n\tdisplay: block;\n\tpadding: 4px 14px 4px 14px;\n}\n\n.tc-block-dropdown.tc-search-drop-down a {\n\tdisplay: block;\n\tpadding: 0px 10px 0px 10px;\n}\n\n.tc-drop-down .tc-dropdown-item-plain,\n.tc-block-dropdown .tc-dropdown-item-plain {\n\tpadding: 4px 14px 4px 7px;\n}\n\n.tc-drop-down .tc-dropdown-item,\n.tc-block-dropdown .tc-dropdown-item {\n\tpadding: 4px 14px 4px 7px;\n\tcolor: <<colour muted-foreground>>;\n}\n\n.tc-block-dropdown a:hover {\n\tcolor: <<colour tiddler-link-background>>;\n\tbackground-color: <<colour tiddler-link-foreground>>;\n\ttext-decoration: none;\n}\n\n.tc-search-results {\n\tpadding: 0 7px 0 7px;\n}\n\n.tc-image-chooser, .tc-colour-chooser {\n\twhite-space: normal;\n}\n\n.tc-image-chooser a,\n.tc-colour-chooser a {\n\tdisplay: inline-block;\n\tvertical-align: top;\n\ttext-align: center;\n\tposition: relative;\n}\n\n.tc-image-chooser a {\n\tborder: 1px solid <<colour muted-foreground>>;\n\tpadding: 2px;\n\tmargin: 2px;\n\twidth: 4em;\n\theight: 4em;\n}\n\n.tc-colour-chooser a {\n\tpadding: 3px;\n\twidth: 2em;\n\theight: 2em;\n\tvertical-align: middle;\n}\n\n.tc-image-chooser a:hover,\n.tc-colour-chooser a:hover {\n\tbackground: <<colour primary>>;\n\tpadding: 0px;\n\tborder: 3px solid <<colour primary>>;\n}\n\n.tc-image-chooser a svg,\n.tc-image-chooser a img {\n\tdisplay: inline-block;\n\twidth: auto;\n\theight: auto;\n\tmax-width: 3.5em;\n\tmax-height: 3.5em;\n\tposition: absolute;\n\ttop: 0;\n\tbottom: 0;\n\tleft: 0;\n\tright: 0;\n\tmargin: auto;\n}\n\n/*\n** Modals\n*/\n\n.tc-modal-wrapper {\n\tposition: fixed;\n\toverflow: auto;\n\toverflow-y: scroll;\n\ttop: 0;\n\tright: 0;\n\tbottom: 0;\n\tleft: 0;\n\tz-index: 900;\n}\n\n.tc-modal-backdrop {\n\tposition: fixed;\n\ttop: 0;\n\tright: 0;\n\tbottom: 0;\n\tleft: 0;\n\tz-index: 1000;\n\tbackground-color: <<colour modal-backdrop>>;\n}\n\n.tc-modal {\n\tz-index: 1100;\n\tbackground-color: <<colour modal-background>>;\n\tborder: 1px solid <<colour modal-border>>;\n}\n\n@media (max-width: 55em) {\n\t.tc-modal {\n\t\tposition: fixed;\n\t\ttop: 1em;\n\t\tleft: 1em;\n\t\tright: 1em;\n\t}\n\n\t.tc-modal-body {\n\t\toverflow-y: auto;\n\t\tmax-height: 400px;\n\t\tmax-height: 60vh;\n\t}\n}\n\n@media (min-width: 55em) {\n\t.tc-modal {\n\t\tposition: fixed;\n\t\ttop: 2em;\n\t\tleft: 25%;\n\t\twidth: 50%;\n\t}\n\n\t.tc-modal-body {\n\t\toverflow-y: auto;\n\t\tmax-height: 400px;\n\t\tmax-height: 60vh;\n\t}\n}\n\n.tc-modal-header {\n\tpadding: 9px 15px;\n\tborder-bottom: 1px solid <<colour modal-header-border>>;\n}\n\n.tc-modal-header h3 {\n\tmargin: 0;\n\tline-height: 30px;\n}\n\n.tc-modal-header img, .tc-modal-header svg {\n\twidth: 1em;\n\theight: 1em;\n}\n\n.tc-modal-body {\n\tpadding: 15px;\n}\n\n.tc-modal-footer {\n\tpadding: 14px 15px 15px;\n\tmargin-bottom: 0;\n\ttext-align: right;\n\tbackground-color: <<colour modal-footer-background>>;\n\tborder-top: 1px solid <<colour modal-footer-border>>;\n}\n\n/*\n** Notifications\n*/\n\n.tc-notification {\n\tposition: fixed;\n\ttop: 14px;\n\tright: 42px;\n\tz-index: 1300;\n\tmax-width: 280px;\n\tpadding: 0 14px 0 14px;\n\tbackground-color: <<colour notification-background>>;\n\tborder: 1px solid <<colour notification-border>>;\n}\n\n/*\n** Tabs\n*/\n\n.tc-tab-set.tc-vertical {\n\tdisplay: -webkit-flex;\n\tdisplay: flex;\n}\n\n.tc-tab-buttons {\n\tfont-size: 0.85em;\n\tpadding-top: 1em;\n\tmargin-bottom: -2px;\n}\n\n.tc-tab-buttons.tc-vertical {\n\tz-index: 100;\n\tdisplay: block;\n\tpadding-top: 14px;\n\tvertical-align: top;\n\ttext-align: right;\n\tmargin-bottom: inherit;\n\tmargin-right: -1px;\n\tmax-width: 33%;\n\t-webkit-flex: 0 0 auto;\n\tflex: 0 0 auto;\n}\n\n.tc-tab-buttons button.tc-tab-selected {\n\tcolor: <<colour tab-foreground-selected>>;\n\tbackground-color: <<colour tab-background-selected>>;\n\tborder-left: 1px solid <<colour tab-border-selected>>;\n\tborder-top: 1px solid <<colour tab-border-selected>>;\n\tborder-right: 1px solid <<colour tab-border-selected>>;\n}\n\n.tc-tab-buttons button {\n\tcolor: <<colour tab-foreground>>;\n\tpadding: 3px 5px 3px 5px;\n\tmargin-right: 0.3em;\n\tfont-weight: 300;\n\tborder: none;\n\tbackground: inherit;\n\tbackground-color: <<colour tab-background>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-top: 1px solid <<colour tab-border>>;\n\tborder-right: 1px solid <<colour tab-border>>;\n\tborder-top-left-radius: 2px;\n\tborder-top-right-radius: 2px;\n}\n\n.tc-tab-buttons.tc-vertical button {\n\tdisplay: block;\n\twidth: 100%;\n\tmargin-top: 3px;\n\tmargin-right: 0;\n\ttext-align: right;\n\tbackground-color: <<colour tab-background>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-bottom: 1px solid <<colour tab-border>>;\n\tborder-right: none;\n\tborder-top-left-radius: 2px;\n\tborder-bottom-left-radius: 2px;\n}\n\n.tc-tab-buttons.tc-vertical button.tc-tab-selected {\n\tbackground-color: <<colour tab-background-selected>>;\n\tborder-right: 1px solid <<colour tab-background-selected>>;\n}\n\n.tc-tab-divider {\n\tborder-top: 1px solid <<colour tab-divider>>;\n}\n\n.tc-tab-divider.tc-vertical {\n\tdisplay: none;\n}\n\n.tc-tab-content {\n\tmargin-top: 14px;\n}\n\n.tc-tab-content.tc-vertical {\n\tdisplay: inline-block;\n\tvertical-align: top;\n\tpadding-top: 0;\n\tpadding-left: 14px;\n\tborder-left: 1px solid <<colour tab-border>>;\n\t-webkit-flex: 1 0 70%;\n\tflex: 1 0 70%;\n}\n\n.tc-sidebar-lists .tc-tab-buttons {\n\tmargin-bottom: -1px;\n}\n\n.tc-sidebar-lists .tc-tab-buttons button.tc-tab-selected {\n\tbackground-color: <<colour sidebar-tab-background-selected>>;\n\tcolor: <<colour sidebar-tab-foreground-selected>>;\n\tborder-left: 1px solid <<colour sidebar-tab-border-selected>>;\n\tborder-top: 1px solid <<colour sidebar-tab-border-selected>>;\n\tborder-right: 1px solid <<colour sidebar-tab-border-selected>>;\n}\n\n.tc-sidebar-lists .tc-tab-buttons button {\n\tbackground-color: <<colour sidebar-tab-background>>;\n\tcolor: <<colour sidebar-tab-foreground>>;\n\tborder-left: 1px solid <<colour sidebar-tab-border>>;\n\tborder-top: 1px solid <<colour sidebar-tab-border>>;\n\tborder-right: 1px solid <<colour sidebar-tab-border>>;\n}\n\n.tc-sidebar-lists .tc-tab-divider {\n\tborder-top: 1px solid <<colour sidebar-tab-divider>>;\n}\n\n.tc-more-sidebar > .tc-tab-set > .tc-tab-buttons > button {\n\tdisplay: block;\n\twidth: 100%;\n\tbackground-color: <<colour sidebar-tab-background>>;\n\tborder-top: none;\n\tborder-left: none;\n\tborder-bottom: none;\n\tborder-right: 1px solid #ccc;\n\tmargin-bottom: inherit;\n}\n\n.tc-more-sidebar > .tc-tab-set > .tc-tab-buttons > button.tc-tab-selected {\n\tbackground-color: <<colour sidebar-tab-background-selected>>;\n\tborder: none;\n}\n\n/*\n** Manager\n*/\n\n.tc-manager-wrapper {\n\t\n}\n\n.tc-manager-controls {\n\t\n}\n\n.tc-manager-control {\n\tmargin: 0.5em 0;\n}\n\n.tc-manager-list {\n\twidth: 100%;\n\tborder-top: 1px solid <<colour muted-foreground>>;\n\tborder-left: 1px solid <<colour muted-foreground>>;\n\tborder-right: 1px solid <<colour muted-foreground>>;\n}\n\n.tc-manager-list-item {\n\n}\n\n.tc-manager-list-item-heading {\n display: block;\n width: 100%;\n text-align: left;\t\n\tborder-bottom: 1px solid <<colour muted-foreground>>;\n\tpadding: 3px;\n}\n\n.tc-manager-list-item-heading-selected {\n\tfont-weight: bold;\n\tcolor: <<colour background>>;\n\tfill: <<colour background>>;\n\tbackground-color: <<colour foreground>>;\n}\n\n.tc-manager-list-item-heading:hover {\n\tbackground: <<colour primary>>;\n\tcolor: <<colour background>>;\n}\n\n.tc-manager-list-item-content {\n\tdisplay: flex;\n}\n\n.tc-manager-list-item-content-sidebar {\n flex: 1 0;\n background: <<colour tiddler-editor-background>>;\n border-right: 0.5em solid <<colour muted-foreground>>;\n border-bottom: 0.5em solid <<colour muted-foreground>>;\n white-space: nowrap;\n}\n\n.tc-manager-list-item-content-item-heading {\n\tdisplay: block;\n\twidth: 100%;\n\ttext-align: left;\n background: <<colour muted-foreground>>;\n\ttext-transform: uppercase;\n\tfont-size: 0.6em;\n\tfont-weight: bold;\n padding: 0.5em 0 0.5em 0;\n}\n\n.tc-manager-list-item-content-item-body {\n\tpadding: 0 0.5em 0 0.5em;\n}\n\n.tc-manager-list-item-content-item-body > pre {\n\tmargin: 0.5em 0 0.5em 0;\n\tborder: none;\n\tbackground: inherit;\n}\n\n.tc-manager-list-item-content-tiddler {\n flex: 3 1;\n border-left: 0.5em solid <<colour muted-foreground>>;\n border-right: 0.5em solid <<colour muted-foreground>>;\n border-bottom: 0.5em solid <<colour muted-foreground>>;\n}\n\n.tc-manager-list-item-content-item-body > table {\n\tborder: none;\n\tpadding: 0;\n\tmargin: 0;\n}\n\n.tc-manager-list-item-content-item-body > table td {\n\tborder: none;\n}\n\n.tc-manager-icon-editor > button {\n\twidth: 100%;\n}\n\n.tc-manager-icon-editor > button > svg,\n.tc-manager-icon-editor > button > button {\n\twidth: 100%;\n\theight: auto;\n}\n\n/*\n** Alerts\n*/\n\n.tc-alerts {\n\tposition: fixed;\n\ttop: 0;\n\tleft: 0;\n\tmax-width: 500px;\n\tz-index: 20000;\n}\n\n.tc-alert {\n\tposition: relative;\n\tmargin: 28px;\n\tpadding: 14px 14px 14px 14px;\n\tborder: 2px solid <<colour alert-border>>;\n\tbackground-color: <<colour alert-background>>;\n}\n\n.tc-alert-toolbar {\n\tposition: absolute;\n\ttop: 14px;\n\tright: 14px;\n}\n\n.tc-alert-toolbar svg {\n\tfill: <<colour alert-muted-foreground>>;\n}\n\n.tc-alert-subtitle {\n\tcolor: <<colour alert-muted-foreground>>;\n\tfont-weight: bold;\n}\n\n.tc-alert-highlight {\n\tcolor: <<colour alert-highlight>>;\n}\n\n@media (min-width: {{$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint}}) {\n\n\t.tc-static-alert {\n\t\tposition: relative;\n\t}\n\n\t.tc-static-alert-inner {\n\t\tposition: absolute;\n\t\tz-index: 100;\n\t}\n\n}\n\n.tc-static-alert-inner {\n\tpadding: 0 2px 2px 42px;\n\tcolor: <<colour static-alert-foreground>>;\n}\n\n/*\n** Control panel\n*/\n\n.tc-control-panel td {\n\tpadding: 4px;\n}\n\n.tc-control-panel table, .tc-control-panel table input, .tc-control-panel table textarea {\n\twidth: 100%;\n}\n\n.tc-plugin-info {\n\tdisplay: block;\n\tborder: 1px solid <<colour muted-foreground>>;\n\tbackground-colour: <<colour background>>;\n\tmargin: 0.5em 0 0.5em 0;\n\tpadding: 4px;\n}\n\n.tc-plugin-info-disabled {\n\tbackground: -webkit-repeating-linear-gradient(45deg, #ff0, #ff0 10px, #eee 10px, #eee 20px);\n\tbackground: repeating-linear-gradient(45deg, #ff0, #ff0 10px, #eee 10px, #eee 20px);\n}\n\n.tc-plugin-info-disabled:hover {\n\tbackground: -webkit-repeating-linear-gradient(45deg, #aa0, #aa0 10px, #888 10px, #888 20px);\n\tbackground: repeating-linear-gradient(45deg, #aa0, #aa0 10px, #888 10px, #888 20px);\n}\n\na.tc-tiddlylink.tc-plugin-info:hover {\n\ttext-decoration: none;\n\tbackground-color: <<colour primary>>;\n\tcolor: <<colour background>>;\n\tfill: <<colour foreground>>;\n}\n\na.tc-tiddlylink.tc-plugin-info:hover .tc-plugin-info > .tc-plugin-info-chunk > svg {\n\tfill: <<colour foreground>>;\n}\n\n.tc-plugin-info-chunk {\n\tdisplay: inline-block;\n\tvertical-align: middle;\n}\n\n.tc-plugin-info-chunk h1 {\n\tfont-size: 1em;\n\tmargin: 2px 0 2px 0;\n}\n\n.tc-plugin-info-chunk h2 {\n\tfont-size: 0.8em;\n\tmargin: 2px 0 2px 0;\n}\n\n.tc-plugin-info-chunk div {\n\tfont-size: 0.7em;\n\tmargin: 2px 0 2px 0;\n}\n\n.tc-plugin-info:hover > .tc-plugin-info-chunk > img, .tc-plugin-info:hover > .tc-plugin-info-chunk > svg {\n\twidth: 2em;\n\theight: 2em;\n\tfill: <<colour foreground>>;\n}\n\n.tc-plugin-info > .tc-plugin-info-chunk > img, .tc-plugin-info > .tc-plugin-info-chunk > svg {\n\twidth: 2em;\n\theight: 2em;\n\tfill: <<colour muted-foreground>>;\n}\n\n.tc-plugin-info.tc-small-icon > .tc-plugin-info-chunk > img, .tc-plugin-info.tc-small-icon > .tc-plugin-info-chunk > svg {\n\twidth: 1em;\n\theight: 1em;\n}\n\n.tc-plugin-info-dropdown {\n\tborder: 1px solid <<colour muted-foreground>>;\n\tmargin-top: -8px;\n}\n\n.tc-plugin-info-dropdown-message {\n\tbackground: <<colour message-background>>;\n\tpadding: 0.5em 1em 0.5em 1em;\n\tfont-weight: bold;\n\tfont-size: 0.8em;\n}\n\n.tc-plugin-info-dropdown-body {\n\tpadding: 1em 1em 1em 1em;\n}\n\n/*\n** Message boxes\n*/\n\n.tc-message-box {\n\tborder: 1px solid <<colour message-border>>;\n\tbackground: <<colour message-background>>;\n\tpadding: 0px 21px 0px 21px;\n\tfont-size: 12px;\n\tline-height: 18px;\n\tcolor: <<colour message-foreground>>;\n}\n\n.tc-message-box svg {\n\twidth: 1em;\n\theight: 1em;\n vertical-align: text-bottom;\n}\n\n/*\n** Pictures\n*/\n\n.tc-bordered-image {\n\tborder: 1px solid <<colour muted-foreground>>;\n\tpadding: 5px;\n\tmargin: 5px;\n}\n\n/*\n** Floats\n*/\n\n.tc-float-right {\n\tfloat: right;\n}\n\n/*\n** Chooser\n*/\n\n.tc-chooser {\n\tborder: 1px solid <<colour table-border>>;\n}\n\n.tc-chooser-item {\n\tborder: 8px;\n\tpadding: 2px 4px;\n}\n\n.tc-chooser-item a.tc-tiddlylink {\n\tdisplay: block;\n\ttext-decoration: none;\n\tcolor: <<colour tiddler-link-foreground>>;\n\tbackground-color: <<colour tiddler-link-background>>;\n}\n\n.tc-chooser-item a.tc-tiddlylink:hover {\n\ttext-decoration: none;\n\tcolor: <<colour tiddler-link-background>>;\n\tbackground-color: <<colour tiddler-link-foreground>>;\n}\n\n/*\n** Palette swatches\n*/\n\n.tc-swatches-horiz {\n}\n\n.tc-swatches-horiz .tc-swatch {\n\tdisplay: inline-block;\n}\n\n.tc-swatch {\n\twidth: 2em;\n\theight: 2em;\n\tmargin: 0.4em;\n\tborder: 1px solid #888;\n}\n\n/*\n** Table of contents\n*/\n\n.tc-sidebar-lists .tc-table-of-contents {\n\twhite-space: nowrap;\n}\n\n.tc-table-of-contents button {\n\tcolor: <<colour sidebar-foreground>>;\n}\n\n.tc-table-of-contents svg {\n\twidth: 0.7em;\n\theight: 0.7em;\n\tvertical-align: middle;\n\tfill: <<colour sidebar-foreground>>;\n}\n\n.tc-table-of-contents ol {\n\tlist-style-type: none;\n\tpadding-left: 0;\n}\n\n.tc-table-of-contents ol ol {\n\tpadding-left: 1em;\n}\n\n.tc-table-of-contents li {\n\tfont-size: 1.0em;\n\tfont-weight: bold;\n}\n\n.tc-table-of-contents li a {\n\tfont-weight: bold;\n}\n\n.tc-table-of-contents li li {\n\tfont-size: 0.95em;\n\tfont-weight: normal;\n\tline-height: 1.4;\n}\n\n.tc-table-of-contents li li a {\n\tfont-weight: normal;\n}\n\n.tc-table-of-contents li li li {\n\tfont-size: 0.95em;\n\tfont-weight: 200;\n\tline-height: 1.5;\n}\n\n.tc-table-of-contents li li li a {\n\tfont-weight: bold;\n}\n\n.tc-table-of-contents li li li li {\n\tfont-size: 0.95em;\n\tfont-weight: 200;\n}\n\n.tc-tabbed-table-of-contents {\n\tdisplay: -webkit-flex;\n\tdisplay: flex;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents {\n\tz-index: 100;\n\tdisplay: inline-block;\n\tpadding-left: 1em;\n\tmax-width: 50%;\n\t-webkit-flex: 0 0 auto;\n\tflex: 0 0 auto;\n\tbackground: <<colour tab-background>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-top: 1px solid <<colour tab-border>>;\n\tborder-bottom: 1px solid <<colour tab-border>>;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a,\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a {\n\tdisplay: block;\n\tpadding: 0.12em 1em 0.12em 0.25em;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a {\n\tborder-top: 1px solid <<colour tab-background>>;\n\tborder-left: 1px solid <<colour tab-background>>;\n\tborder-bottom: 1px solid <<colour tab-background>>;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item > a:hover {\n\ttext-decoration: none;\n\tborder-top: 1px solid <<colour tab-border>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-bottom: 1px solid <<colour tab-border>>;\n\tbackground: <<colour tab-border>>;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a {\n\tborder-top: 1px solid <<colour tab-border>>;\n\tborder-left: 1px solid <<colour tab-border>>;\n\tborder-bottom: 1px solid <<colour tab-border>>;\n\tbackground: <<colour background>>;\n\tmargin-right: -1px;\n}\n\n.tc-tabbed-table-of-contents .tc-table-of-contents .toc-item-selected > a:hover {\n\ttext-decoration: none;\n}\n\n.tc-tabbed-table-of-contents .tc-tabbed-table-of-contents-content {\n\tdisplay: inline-block;\n\tvertical-align: top;\n\tpadding-left: 1.5em;\n\tpadding-right: 1.5em;\n\tborder: 1px solid <<colour tab-border>>;\n\t-webkit-flex: 1 0 50%;\n\tflex: 1 0 50%;\n}\n\n/*\n** Dirty indicator\n*/\n\nbody.tc-dirty span.tc-dirty-indicator, body.tc-dirty span.tc-dirty-indicator svg {\n\tfill: <<colour dirty-indicator>>;\n\tcolor: <<colour dirty-indicator>>;\n}\n\n/*\n** File inputs\n*/\n\n.tc-file-input-wrapper {\n\tposition: relative;\n\toverflow: hidden;\n\tdisplay: inline-block;\n\tvertical-align: middle;\n}\n\n.tc-file-input-wrapper input[type=file] {\n\tposition: absolute;\n\ttop: 0;\n\tleft: 0;\n\tright: 0;\n\tbottom: 0;\n\tfont-size: 999px;\n\tmax-width: 100%;\n\tmax-height: 100%;\n\tfilter: alpha(opacity=0);\n\topacity: 0;\n\toutline: none;\n\tbackground: white;\n\tcursor: pointer;\n\tdisplay: inline-block;\n}\n\n/*\n** Thumbnail macros\n*/\n\n.tc-thumbnail-wrapper {\n\tposition: relative;\n\tdisplay: inline-block;\n\tmargin: 6px;\n\tvertical-align: top;\n}\n\n.tc-thumbnail-right-wrapper {\n\tfloat:right;\n\tmargin: 0.5em 0 0.5em 0.5em;\n}\n\n.tc-thumbnail-image {\n\ttext-align: center;\n\toverflow: hidden;\n\tborder-radius: 3px;\n}\n\n.tc-thumbnail-image svg,\n.tc-thumbnail-image img {\n\tfilter: alpha(opacity=1);\n\topacity: 1;\n\tmin-width: 100%;\n\tmin-height: 100%;\n\tmax-width: 100%;\n}\n\n.tc-thumbnail-wrapper:hover .tc-thumbnail-image svg,\n.tc-thumbnail-wrapper:hover .tc-thumbnail-image img {\n\tfilter: alpha(opacity=0.8);\n\topacity: 0.8;\n}\n\n.tc-thumbnail-background {\n\tposition: absolute;\n\tborder-radius: 3px;\n}\n\n.tc-thumbnail-icon svg,\n.tc-thumbnail-icon img {\n\twidth: 3em;\n\theight: 3em;\n\t<<filter \"drop-shadow(2px 2px 4px rgba(0,0,0,0.3))\">>\n}\n\n.tc-thumbnail-wrapper:hover .tc-thumbnail-icon svg,\n.tc-thumbnail-wrapper:hover .tc-thumbnail-icon img {\n\tfill: #fff;\n\t<<filter \"drop-shadow(3px 3px 4px rgba(0,0,0,0.6))\">>\n}\n\n.tc-thumbnail-icon {\n\tposition: absolute;\n\ttop: 0;\n\tleft: 0;\n\tright: 0;\n\tbottom: 0;\n\tdisplay: -webkit-flex;\n\t-webkit-align-items: center;\n\t-webkit-justify-content: center;\n\tdisplay: flex;\n\talign-items: center;\n\tjustify-content: center;\n}\n\n.tc-thumbnail-caption {\n\tposition: absolute;\n\tbackground-color: #777;\n\tcolor: #fff;\n\ttext-align: center;\n\tbottom: 0;\n\twidth: 100%;\n\tfilter: alpha(opacity=0.9);\n\topacity: 0.9;\n\tline-height: 1.4;\n\tborder-bottom-left-radius: 3px;\n\tborder-bottom-right-radius: 3px;\n}\n\n.tc-thumbnail-wrapper:hover .tc-thumbnail-caption {\n\tfilter: alpha(opacity=1);\n\topacity: 1;\n}\n\n/*\n** Errors\n*/\n\n.tc-error {\n\tbackground: #f00;\n\tcolor: #fff;\n}"
},
"$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/bodyfontsize",
"text": "15px"
},
"$:/themes/tiddlywiki/vanilla/metrics/bodylineheight": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/bodylineheight",
"text": "22px"
},
"$:/themes/tiddlywiki/vanilla/metrics/fontsize": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/fontsize",
"text": "14px"
},
"$:/themes/tiddlywiki/vanilla/metrics/lineheight": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/lineheight",
"text": "20px"
},
"$:/themes/tiddlywiki/vanilla/metrics/storyleft": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/storyleft",
"text": "0px"
},
"$:/themes/tiddlywiki/vanilla/metrics/storytop": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/storytop",
"text": "0px"
},
"$:/themes/tiddlywiki/vanilla/metrics/storyright": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/storyright",
"text": "770px"
},
"$:/themes/tiddlywiki/vanilla/metrics/storywidth": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/storywidth",
"text": "770px"
},
"$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/tiddlerwidth",
"text": "686px"
},
"$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/sidebarbreakpoint",
"text": "960px"
},
"$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth": {
"title": "$:/themes/tiddlywiki/vanilla/metrics/sidebarwidth",
"text": "350px"
},
"$:/themes/tiddlywiki/vanilla/options/stickytitles": {
"title": "$:/themes/tiddlywiki/vanilla/options/stickytitles",
"text": "no"
},
"$:/themes/tiddlywiki/vanilla/options/sidebarlayout": {
"title": "$:/themes/tiddlywiki/vanilla/options/sidebarlayout",
"text": "fixed-fluid"
},
"$:/themes/tiddlywiki/vanilla/options/codewrapping": {
"title": "$:/themes/tiddlywiki/vanilla/options/codewrapping",
"text": "pre-wrap"
},
"$:/themes/tiddlywiki/vanilla/reset": {
"title": "$:/themes/tiddlywiki/vanilla/reset",
"type": "text/plain",
"text": "/*! normalize.css v3.0.0 | MIT License | git.io/normalize */\n\n/**\n * 1. Set default font family to sans-serif.\n * 2. Prevent iOS text size adjust after orientation change, without disabling\n * user zoom.\n */\n\nhtml {\n font-family: sans-serif; /* 1 */\n -ms-text-size-adjust: 100%; /* 2 */\n -webkit-text-size-adjust: 100%; /* 2 */\n}\n\n/**\n * Remove default margin.\n */\n\nbody {\n margin: 0;\n}\n\n/* HTML5 display definitions\n ========================================================================== */\n\n/**\n * Correct `block` display not defined in IE 8/9.\n */\n\narticle,\naside,\ndetails,\nfigcaption,\nfigure,\nfooter,\nheader,\nhgroup,\nmain,\nnav,\nsection,\nsummary {\n display: block;\n}\n\n/**\n * 1. Correct `inline-block` display not defined in IE 8/9.\n * 2. Normalize vertical alignment of `progress` in Chrome, Firefox, and Opera.\n */\n\naudio,\ncanvas,\nprogress,\nvideo {\n display: inline-block; /* 1 */\n vertical-align: baseline; /* 2 */\n}\n\n/**\n * Prevent modern browsers from displaying `audio` without controls.\n * Remove excess height in iOS 5 devices.\n */\n\naudio:not([controls]) {\n display: none;\n height: 0;\n}\n\n/**\n * Address `[hidden]` styling not present in IE 8/9.\n * Hide the `template` element in IE, Safari, and Firefox < 22.\n */\n\n[hidden],\ntemplate {\n display: none;\n}\n\n/* Links\n ========================================================================== */\n\n/**\n * Remove the gray background color from active links in IE 10.\n */\n\na {\n background: transparent;\n}\n\n/**\n * Improve readability when focused and also mouse hovered in all browsers.\n */\n\na:active,\na:hover {\n outline: 0;\n}\n\n/* Text-level semantics\n ========================================================================== */\n\n/**\n * Address styling not present in IE 8/9, Safari 5, and Chrome.\n */\n\nabbr[title] {\n border-bottom: 1px dotted;\n}\n\n/**\n * Address style set to `bolder` in Firefox 4+, Safari 5, and Chrome.\n */\n\nb,\nstrong {\n font-weight: bold;\n}\n\n/**\n * Address styling not present in Safari 5 and Chrome.\n */\n\ndfn {\n font-style: italic;\n}\n\n/**\n * Address variable `h1` font-size and margin within `section` and `article`\n * contexts in Firefox 4+, Safari 5, and Chrome.\n */\n\nh1 {\n font-size: 2em;\n margin: 0.67em 0;\n}\n\n/**\n * Address styling not present in IE 8/9.\n */\n\nmark {\n background: #ff0;\n color: #000;\n}\n\n/**\n * Address inconsistent and variable font size in all browsers.\n */\n\nsmall {\n font-size: 80%;\n}\n\n/**\n * Prevent `sub` and `sup` affecting `line-height` in all browsers.\n */\n\nsub,\nsup {\n font-size: 75%;\n line-height: 0;\n position: relative;\n vertical-align: baseline;\n}\n\nsup {\n top: -0.5em;\n}\n\nsub {\n bottom: -0.25em;\n}\n\n/* Embedded content\n ========================================================================== */\n\n/**\n * Remove border when inside `a` element in IE 8/9.\n */\n\nimg {\n border: 0;\n}\n\n/**\n * Correct overflow displayed oddly in IE 9.\n */\n\nsvg:not(:root) {\n overflow: hidden;\n}\n\n/* Grouping content\n ========================================================================== */\n\n/**\n * Address margin not present in IE 8/9 and Safari 5.\n */\n\nfigure {\n margin: 1em 40px;\n}\n\n/**\n * Address differences between Firefox and other browsers.\n */\n\nhr {\n -moz-box-sizing: content-box;\n box-sizing: content-box;\n height: 0;\n}\n\n/**\n * Contain overflow in all browsers.\n */\n\npre {\n overflow: auto;\n}\n\n/**\n * Address odd `em`-unit font size rendering in all browsers.\n */\n\ncode,\nkbd,\npre,\nsamp {\n font-family: monospace, monospace;\n font-size: 1em;\n}\n\n/* Forms\n ========================================================================== */\n\n/**\n * Known limitation: by default, Chrome and Safari on OS X allow very limited\n * styling of `select`, unless a `border` property is set.\n */\n\n/**\n * 1. Correct color not being inherited.\n * Known issue: affects color of disabled elements.\n * 2. Correct font properties not being inherited.\n * 3. Address margins set differently in Firefox 4+, Safari 5, and Chrome.\n */\n\nbutton,\ninput,\noptgroup,\nselect,\ntextarea {\n color: inherit; /* 1 */\n font: inherit; /* 2 */\n margin: 0; /* 3 */\n}\n\n/**\n * Address `overflow` set to `hidden` in IE 8/9/10.\n */\n\nbutton {\n overflow: visible;\n}\n\n/**\n * Address inconsistent `text-transform` inheritance for `button` and `select`.\n * All other form control elements do not inherit `text-transform` values.\n * Correct `button` style inheritance in Firefox, IE 8+, and Opera\n * Correct `select` style inheritance in Firefox.\n */\n\nbutton,\nselect {\n text-transform: none;\n}\n\n/**\n * 1. Avoid the WebKit bug in Android 4.0.* where (2) destroys native `audio`\n * and `video` controls.\n * 2. Correct inability to style clickable `input` types in iOS.\n * 3. Improve usability and consistency of cursor style between image-type\n * `input` and others.\n */\n\nbutton,\nhtml input[type=\"button\"], /* 1 */\ninput[type=\"reset\"],\ninput[type=\"submit\"] {\n -webkit-appearance: button; /* 2 */\n cursor: pointer; /* 3 */\n}\n\n/**\n * Re-set default cursor for disabled elements.\n */\n\nbutton[disabled],\nhtml input[disabled] {\n cursor: default;\n}\n\n/**\n * Remove inner padding and border in Firefox 4+.\n */\n\nbutton::-moz-focus-inner,\ninput::-moz-focus-inner {\n border: 0;\n padding: 0;\n}\n\n/**\n * Address Firefox 4+ setting `line-height` on `input` using `!important` in\n * the UA stylesheet.\n */\n\ninput {\n line-height: normal;\n}\n\n/**\n * It's recommended that you don't attempt to style these elements.\n * Firefox's implementation doesn't respect box-sizing, padding, or width.\n *\n * 1. Address box sizing set to `content-box` in IE 8/9/10.\n * 2. Remove excess padding in IE 8/9/10.\n */\n\ninput[type=\"checkbox\"],\ninput[type=\"radio\"] {\n box-sizing: border-box; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Fix the cursor style for Chrome's increment/decrement buttons. For certain\n * `font-size` values of the `input`, it causes the cursor style of the\n * decrement button to change from `default` to `text`.\n */\n\ninput[type=\"number\"]::-webkit-inner-spin-button,\ninput[type=\"number\"]::-webkit-outer-spin-button {\n height: auto;\n}\n\n/**\n * 1. Address `appearance` set to `searchfield` in Safari 5 and Chrome.\n * 2. Address `box-sizing` set to `border-box` in Safari 5 and Chrome\n * (include `-moz` to future-proof).\n */\n\ninput[type=\"search\"] {\n -webkit-appearance: textfield; /* 1 */\n -moz-box-sizing: content-box;\n -webkit-box-sizing: content-box; /* 2 */\n box-sizing: content-box;\n}\n\n/**\n * Remove inner padding and search cancel button in Safari and Chrome on OS X.\n * Safari (but not Chrome) clips the cancel button when the search input has\n * padding (and `textfield` appearance).\n */\n\ninput[type=\"search\"]::-webkit-search-cancel-button,\ninput[type=\"search\"]::-webkit-search-decoration {\n -webkit-appearance: none;\n}\n\n/**\n * Define consistent border, margin, and padding.\n */\n\nfieldset {\n border: 1px solid #c0c0c0;\n margin: 0 2px;\n padding: 0.35em 0.625em 0.75em;\n}\n\n/**\n * 1. Correct `color` not being inherited in IE 8/9.\n * 2. Remove padding so people aren't caught out if they zero out fieldsets.\n */\n\nlegend {\n border: 0; /* 1 */\n padding: 0; /* 2 */\n}\n\n/**\n * Remove default vertical scrollbar in IE 8/9.\n */\n\ntextarea {\n overflow: auto;\n}\n\n/**\n * Don't inherit the `font-weight` (applied by a rule above).\n * NOTE: the default cannot safely be changed in Chrome and Safari on OS X.\n */\n\noptgroup {\n font-weight: bold;\n}\n\n/* Tables\n ========================================================================== */\n\n/**\n * Remove most spacing between table cells.\n */\n\ntable {\n border-collapse: collapse;\n border-spacing: 0;\n}\n\ntd,\nth {\n padding: 0;\n}\n"
},
"$:/themes/tiddlywiki/vanilla/settings/fontfamily": {
"title": "$:/themes/tiddlywiki/vanilla/settings/fontfamily",
"text": "\"Helvetica Neue\", Helvetica, Arial, \"Lucida Grande\", \"DejaVu Sans\", sans-serif"
},
"$:/themes/tiddlywiki/vanilla/settings/codefontfamily": {
"title": "$:/themes/tiddlywiki/vanilla/settings/codefontfamily",
"text": "Monaco, Consolas, \"Lucida Console\", \"DejaVu Sans Mono\", monospace"
},
"$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment": {
"title": "$:/themes/tiddlywiki/vanilla/settings/backgroundimageattachment",
"text": "fixed"
},
"$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize": {
"title": "$:/themes/tiddlywiki/vanilla/settings/backgroundimagesize",
"text": "auto"
},
"$:/themes/tiddlywiki/vanilla/sticky": {
"title": "$:/themes/tiddlywiki/vanilla/sticky",
"text": "<$reveal state=\"$:/themes/tiddlywiki/vanilla/options/stickytitles\" type=\"match\" text=\"yes\">\n``\n.tc-tiddler-title {\n\tposition: -webkit-sticky;\n\tposition: -moz-sticky;\n\tposition: -o-sticky;\n\tposition: -ms-sticky;\n\tposition: sticky;\n\ttop: 0px;\n\tbackground: ``<<colour tiddler-background>>``;\n\tz-index: 500;\n}\n``\n</$reveal>\n"
}
}
}
14px
1500px
0px
1500px
1500px
1400px
fluid-fixed
yanjiamao.tiddlyspot.com/
classic
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
Thm 2.4.8 a special case of the [[Rao-Blackwell Theorem]]
[[link|https://www.4clojure.com/problems]] I am using the offline-4clojure solution provided by thattommyhall. To run problem #30 by ``` lein run -m offline-4clojure.p30 ``` ! Progress [img[4clojure.png]]
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
Given an improper prior $\pi$, a sample $(x_1, \dots, x_n)$ is a training sample if the corresponding posterior $\pi(\cdot\mid x_1, \dots, x_n)$ is proper and is a minimal ''training sample'' if no subsample is a training sample.
The idea is then to use a minimal training sample. $x_{(l)}$ say, to "properize" the improper prior $\pi$ into $\pi(\cdot\mid x_{(l)})$, and then to use the posterior distribution as if it were a regular proper prior for the remainder of the sample, $x_{(-l)}$ say, in order to avoid the data twice, as in the proposal of Aitkin (1991). When facing a hypothesis $H_0$ with a prior distribution $\pi_0$, with a broader alternative $H_1$ with a prior distribution $\pi_1$, if the minimal training sample under $H_1$ is such that $\pi_0(\cdot\mid x_{(l)})$ is also proer, the pseudo-Bayes factor
$$
B_{10}^{(l)} = \frac{\int_{\Theta_1}f_1(x\mid\theta_1)\pi_1(\theta_1)d\theta_1}{\int_{\Theta_0}f_0(x\mid\theta_0)\pi_0(\theta_0)d\theta_0}
$$
is then independent from the normalizaing constants used in both $\pi_0$ and $\pi_1$.
There are enough difficulties to make us question their use in testing and model choice problems:
# Most pseudo-Bayes factors do not, even though the fractional Bayes factor satisfies $B_{01}^F = 1/B_{10}$.
# When the pseudo-Bayes factors can be expressed as true Bayes factors, the corresponding intrinsic priors are not necessarily appealing and these priors do depend on the choice of the improper reference priors $\pi_0$ and $\pi_1$, hence are hardly intrinsic.
# The pseudo-Bayes factors may also exhibit a bias in favor of one of the hypothesis, in the sence that they can be expressed as a true Bayes factor multiplied by a certain factor. in such cases, it can be seen as modifying the probability of both hypothesis from the reference value $1/2$, a feature we will also encounter for [[least favorables bounds in Section 2.5.2|2.5.2 The absolute error loss]].
# Most often, however, the pseudo-Bayes factors do not correspond to any true Bayes factor, and they may give strongly biased solutions.
# Pseudo-Bayes factors may simply not exist for a whole class of models.
# There are many ways of defining pseudo-Bayes factors and, while most are arguably logical, there is no coherent way of ordering them. Pseudo-Bayes factors, as defined here, do agree with the Likelihood Principle, but the multiplication of possible answers, even if those are close, is not a good signal to users.
# The issue of computing pseudo-Bayes factors.
'' Definition 5.3.1'' The power of a testing procedure $\varphi$ is the probability of rejecting $H_0$ under the alternative hypothesis, that is, $\beta(\theta) = 1-\mathbb E_\theta[\varphi(x)]$ when $\theta\in\Theta_1$. The quantity $1-\beta(\theta)$ is called ''type-two error'', while the ''type-one error'' is $E_\theta[\varphi(x)]$ when $\theta\in\Theta_0$.
'' Definition 5.3.2'' If $\alpha\in[0,1]$ and $\mathcal C_\alpha$ is the class of the procedures $\varphi$ satisfiying the following constraint on the type I error:
$$
\sup_{\theta\in\Theta_0}\mathbb E_\theta[L(\theta, \varphi(x))]=\sup_{\theta\in\Theta_0}P_\theta(\varphi(x)=0)\le\alpha,
$$
a test procedure $\varphi$ is said to be uniformly most powerful at level $\alpha$ (''UMP'') if it minimizes the risk $\mathbb E_\theta[L(\theta, \varphi(x))]$ uniformly on $\Theta_1$ in $\mathcal C_\alpha$.
This optimality is much weaker than the notion of admissibility developed in [[Section 2.4|2.4 Two optimalities: minimaxity and admissibility]].
''Proposition 5.3.3'' Consider $f(x|\theta)$ with a monotone likelihood ratio in $T(x)$. For $H_0:\theta\le\theta_0$ and $H_1:\theta>\theta_0$ there exists a UMP test such that
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \mbox{if } T(x) < c,\\
\gamma & \mbox{if } T(x) = c,\\
0 & \mbox{otherwise}.
\end{array}\right.
$$
<<<
A major difficulty with the Neyman-Pearson approach, namely, that arbitrary significance levels are not necessarily attainable unless one calls for randomization. For discrete cases, $\varphi(x)=\gamma$ means that $\varphi(x) = 1$ with probability $\gamma$. Such procedures are incompatible with the [[Likelihood Principle]].
<<<
''Proposition 5.3.5'' Consider an exponential family
$$
f(x|\theta) = e^{\theta T(x)-\psi(\theta)}h(x)
$$
and $H_0:\theta\le\theta_1$ or $\theta\ge\theta_2$, $H_1:\theta_1<\theta<\theta_2$. There exists a UMP test such that
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
0 & \mbox{if } c_1 < T(x) < c_2,\\
\gamma_i & \mbox{if } T(x) = c_i \qquad (i=1,2),\\
1 & \mbox{otherwise}.
\end{array}\right.
$$
with $(i = 1, 2)$
$$
\mathbb E_{theta_i}[\varphi(x)]=\alpha.
$$
<<<
There is no corresponding UMP test for the opposite case, i.e., $H_0:\theta_1<\theta<\theta_2$
<<<
The Neyman-Pearson solution is to propose an additional reduction of the class of test procedures by considering unbiased tests, i.e., those also satisfying
$$
\sup_{\Theta_0}P_\theta(\varphi(x) = 0)\le\inf_{\Theta_1}P_\theta(\varphi(x) = 0).
$$
In other words, $\varphi$ must also satisfy
$$
\inf_{\Theta_0}\mathbb E_\theta[\varphi(x)]\ge\sup_{\Theta_1}\mathbb E_\theta[\varphi(x)].
$$
The notion of ''uniformly most powerful unbiased'' tests (UMPU) then follows.
Consider $H_0:\theta\in\Theta_0$ to be tested against a point alternative $H_1:\theta\in\Theta_1$ with $\pi$ a prior distribution on $\Theta_0$. From a Bayesian point of view, the test problem can be represented as the test of $H_\pi: x\sim m_\pi$ versus $H_1:x\sim f(x|\theta_1)$, where $m$ is the marginal distribution under $H_0$
$$
m_\pi(x)=\int_{\Theta_0}f(x|\theta)\pi(\theta)d\theta.
$$
Since both hypothesis ($H-\pi$ and $H_1$) are point hypotheses, the [[Neyman-Pearson lemma]] ensures the existence of a UMP test $\varphi_\pi$, at significance level $\alpha$, with power $\beta_\pi = P_{\theta_1}(\varphi_\pi(x) = 0)$. This test is of the form
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \mbox{if } m_\pi(x)>kf(x|\theta_1),\\
0 & \mbox{otherwise}.
\end{array}\right.
$$
'' Definition 5.3.7'' A least favorable distribution is any prior distribution $\pi$ which maximizes the power $\beta_\pi$.
''Theorem 5.3.8'' If the UMP test $\varphi_\pi$ at level $\alpha$ for $H_\pi$ versus $H_1$ satisfies
$$
\underset{\theta\in\Theta_0}{\sup}\mathbb E_\theta[L(\theta,\varphi_\pi)]\le\alpha,
$$
then
# $\varphi_\pi$ is UMP at level $\alpha$;
# if $\varphi_\pi$ is the unique $\alpha$-level test of $H_\pi$ versus $H_1$, $\varphi_\pi$ is the unique UMP test at level $\alpha$ to test $H_0$ versus $H_1$; and
# $\pi$ is a least favorable distribution.
<<<
The constraint in the above theorem may seem unnecessary, but notice that $\varphi_\pi$ is defined by
$$
\int_{\{m_\pi(x)>kf(x|\theta_1)\}}m_\pi(x)dx=\alpha.
$$
<<<
Given a loss function $L$ and a prior distribution $\pi$, the Bayes estimate associated with an observation $x$ is the (usually unique) decision $d$ minimizing the posterior loss $$ L(\pi, d|x) = \int_\Theta L(\theta, d)\pi(\theta|x)d\theta. $$ Its minimization can be hindered by two difficulties in practice: # the explicit computation of the posterior distribution, $\pi(\theta|x)$, may be impossible; and # even if $\pi(\theta|x)$ is known, this does not necessarily imply that minimizing it is an easy task; indeed, when analytic integration is impossible, numerical minimization sometimes calls for a formidable amount of computing time, especially when $\Theta$ and $\mathcal D$ have large dimensions.
! Definition
Define sets $\Theta$, $\mathcal{X}$ and $\mathcal{A}$, where $\Theta$ are the states of nature, $\mathcal{X}$ the possible observations, and $\mathcal{A}$ the actions that may be taken. An observation $x \in \mathcal{X}$ is distributed as $F(x\mid\theta)$ and therefore provides evidence about the state of nature $\theta\in\Theta$. A decision rule is a function $\delta:{\mathcal{X}}\rightarrow {\mathcal{A}}$, where upon observing $x\in \mathcal{X}$, we choose to take action $\delta(x)\in \mathcal{A}$.
Also define a loss function $L: \Theta \times \mathcal{A} \rightarrow \mathbb{R}$, which specifies the loss we would incur by taking action $a \in \mathcal{A}$ when the true state of nature is $\theta \in \Theta$. Usually we will take this action after observing data $x \in \mathcal{X}$, so that the loss will be $L(\theta,\delta(x))$. (It is possible though unconventional to recast the following definitions in terms of a utility function, which is the negative of the loss.)
Define the risk function as the expectation
$$
R(\theta,\delta)=\operatorname{E}_{F(x\mid\theta)}[{L(\theta,\delta(x))]}.\,\!
$$
Whether a decision rule $\delta$, has low risk depends on the true state of nature $\theta$. A decision rule $\delta^*$ dominates a decision rule $\delta$ if and only if $R(\theta,\delta^*)\le R(\theta,\delta)$ for all $\theta$, and the inequality is strict for some $\theta$.
A decision rule is ''admissible'' (with respect to the loss function) if and only if no other rule dominates it; otherwise it is inadmissible. Thus an admissible decision rule is a maximal element with respect to the above partial order. An inadmissible rule is not preferred (except for reasons of simplicity or computational efficiency), since by definition there is some other rule that will achieve equal or lower risk for all $\theta$. But just because a rule $\delta$ is admissible does not mean it is a good rule to use. Being admissible means there is no other single rule that is always better - but other admissible rules might achieve lower risk for most $\theta$ that occur in practice. (The Bayes risk discussed below is a way of explicitly considering which $\theta$ occur in practice.)
! Intro A CNN architecture with multiple convolution layers, positing latent, dense and low-dimensional word vectors (initialized to random values) as inputs. One must represent a sentence in terms of features that depend on the words and short //n//-grams that are frequently observed. @@color:#859900; In our case, new (and irregular) words are often to appear so perhaps char is a good entity. @@ The net consists of 1-dim conv and dynamic //k//-max pooling, which pools //k// values (k is dynamically determined). There are multiple feature maps.
[[link|http://mlg.eng.cam.ac.uk/zoubin/papers/lds.pdf]] * Hinton et al. (1995) note that factor analysis and PCA are closely related * Digalakis et al. (1993) relate the forware-backward algorithm for HMMs to Kalman filtering From this paper: * factor analysis and mixtures of Gaussians can be implemented using autoencoder neural networks * ICA can be seen as a nonlinear version of factor analysis
! Model Selection Approaches [[Contrasting AIC and BIC]] ! Outlier and Novelty [[Outlier Detection]] ! KL-Based [[KL-Based Changepoint]]
! Google * [[A Polynomial-Time Dynamic Programming Algorithm for Phrase-Based Decoding with a Fixed Distortion Limit|https://www.transacl.org/ojs/index.php/tacl/article/view/1020]] * [[Cross-Sentence N-ary Relation Extraction with Graph LSTMs|https://www.transacl.org/ojs/index.php/tacl/article/view/1028]] * [[Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision|https://arxiv.org/abs/1611.00020]] * [[Coarse-to-Fine Question Answering for Long Documents|https://homes.cs.washington.edu/~eunsol/papers/acl17eunsol.pdf]] * [[Automatic Compositor Attribution in the First Folio of Shakespeare|https://arxiv.org/abs/1704.07875]] * [[A Nested Attention Neural Hybrid Model for Grammatical Error Correction|https://arxiv.org/abs/1707.02026]] * [[Get To The Point: Summarization with Pointer-Generator Networks|https://arxiv.org/abs/1704.04368]] * [[Identifying 1950s American Jazz Composers: Fine-Grained IsA Extraction via Modifier Composition|https://www.seas.upenn.edu/~epavlick/papers/finegrained-isa.pdf]] * [[Learning to Skim Text|https://arxiv.org/abs/1704.06877]] ! Facebook * [[Automatically Generating Rhythmic Verse with Neural Networks|https://research.fb.com/publications/automatically-generating-rhythmic-verse-with-neural-networks/]] * [[Enriching Word Vectors with Subword Information|https://research.fb.com/publications/enriching-word-vectors-with-subword-information-2]] * Reading Wikipedia to Answer Open-Domain Questions
Don Knuth opened for this panel!!! Computer science is similar to math in that we can create our own problems. Knuth don't believe in letting an algorithm to compose music. He thinks computers should help people to reduce the search space. He does believe in automatic theorem proving. He mentioned literate programming many times. I think we should refer to it when considering program induction. Jokes # I don't know what a //deep theorem// is, but I think it's similar to what found by deep learning. # By the time I won Turing award, the prize is 1M$ less than today. But I do got a silver plate that my wife and I used to use it to serve strawberries all the time. Strawberries actually taste much better. Barbara Liskov introduces the impact of Turing Recipients work. John McCarthy's work on non-monotonic reasoning is mentioned. Maybe related to reasoning the correctness of code.
This is derived from Policy Gradient Theorem. We want to optimize a expected discounted reward
$$
\eta(\pi_\theta) = \mathbb E_{\pi_\theta}\left[\sum_{t=1}^\infty\gamma^{t-1}r_t|s_0\right]
$$
It has 2 networks (or one net with two heads):
* Actor: predict action from state
* Critic: evaluate state (can also evaluate state and action)
The update iteration is:
# take action $a\sim\pi_\theta(a|s)$, get $(s, a, s', r)$
# update $\hat V_\phi^\pi$ using target $r+\gamma\hat V_\phi^\pi(s')$
# evaluate $\hat A^\pi(s, a)=r(s, a)+\gamma V_\phi^\pi(s')-\hat V_\phi^\pi(s)$
# $\nabla_\theta J(\theta)\approx\nabla_\theta\log\pi_\theta(a|s)\hat A^\pi(s, a)$
# $\theta\leftarrow\theta+\alpha\nabla_\theta J(\theta)$
Value and policy update work best with a batch. We can design better estimators of advantage: [[Generalized advantage estimation|https://arxiv.org/abs/1506.02438]]
! Bibs
* Classic papers
** Policy gradient methods for reinforcement learning with function approximation: actor-critic algorithms with value function approximation
* Deep RL
** Asynchronous methods for deep reinforcement learning: A3C -- parallel online actor-critic
** High-dimensional continuous control using generalized advantage estimation: batch-mode actor-critic with blended Monte Carlo and function approximator returns
** Q-Prop: sample-efficient policy-gradient with an off-policy critic: policy gradient with Q-function control variate
The benefits of Adadelta are as follows: * No manual setting of a learning rate. * Insensitive to hyperparameters. * Separate dynamic learning rate per-dimension. * Minimal computation over gradient descent. * Robust to large gradients, noise and architecture choice. * Applicable in both local or distributed environments.
! Reading list The talk generalizes following works: * “Intriguing Properties of Neural Networks” Szegedy et al, 2013 * “Explaining and Harnessing Adversarial Examples” Goodfellow et al 2014 * “Adversarial Perturbations of Deep Neural Networks” Warde-Farley and Goodfellow, 2016 * “Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples” Papernot et al 2016 * “Practical Black-Box Attacks against Deep Learning Systems using Adversarial Examples” Papernot et al 2016 * “Adversarial Perturbations Against Deep Neural Networks for Malware Classification” Grosse et al 2016 (not my own work) * “Distributional Smoothing with Virtual Adversarial Training” Miyato et al 2015 (not my own work) * “Virtual Adversarial Training for Semi-Supervised Text Classification” Miyato et al 2016 * “Adversarial Examples in the Physical World” Kurakin et al 2016 * [[The Space of Transferable Adversarial Examples|https://arxiv.org/abs/1704.03453]]: some conditions * [[Universal adversarial perturbations|https://arxiv.org/abs/1610.08401]], CVPR 2017 ** One mask to fool them all ** Analyzed model robustness Neural networks (esp. softmax regressions) are easily fooled. We can add noise-like filters to attack a neural net, misleading it to wrong prediction while making no perceptional difference to human. For example, we can perform gradient ascent on the input to change its prediction. ! Explaining the Weakness First guess is the model is overfitted. Rather than randomly misclassification, there is a systematic behavior. This can be explained better, if we imagine the decision surface being far too linear, rather than too complex. Actually, modern deep nets are indeed very piecewise linear. By moving the input along the adversarial direction, images always tend to be mistaken as another fixed class, defending this linear assumption. Deep neural nets cannot make intentional decisions like human, they learn in a wrong way. They don't learn the contour of the input space, but just some general directions. Adversarial examples unveil its limitations. ! Fast Gradient Sign Method Maximize $$ J(x, \theta)+(\tilde x-x)^\top\nabla_xJ(x) $$ subject to $$ \|\tilde x-x\|_\infty \le \epsilon $$ ! Library [[cleverhans|https://github.com/openai/cleverhans]]
We want to avoid log-density computation of $\log q(z|x, w)$. From the objective:
$$
\begin{align}
&\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta)]-D_{KL}(q(z|x, w)\|p(z))\\
=&\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta) + \log p(z)-\log q(z|x, w)]
\end{align}
$$
We introduce a real-valued discriminator function $T(x, z)$ such that
$$
T(x, z)\approx-\log p(z)+\log q(z|x, w)
$$
Now we optimize $T$ with a similar form of $D$ from GAN:
$$
\max_{T\in\mathcal T}\mathbb E_{x\sim p_D}[\mathbb E_{z\sim q(z|x, w)}[\log\sigma(T(x, z))]+\mathbb E_{z\sim p(z)}[\log(1-\sigma(T(x, z)))]]
$$
And the ELBO with
$$
\begin{align}
&\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta) + \log p(z)-\log q(z|x, w)]\\
=&\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta) - T^*(x, z)]
\end{align}
$$
! Researchers * Christoph. Treude * David Lo ! Topics and related fields * ''Program Synthesis'': natural language to program ** @@color:#859900;Good place to start because there are high quality datasets and excellent examples of previous work.@@ ** @@color:#ec5300;Generation may get high evaluation score but cannot run or give wrong answers.@@ ** @@color:#ec5300;We have to look for applications of translating from NL (or code) to (not accurate) code.@@ * ''Program Induction'': sample to sample ** @@color:#859900;Powerful models that can simulate modern computer architecture and learn programming paradigms like recursion.@@ ** @@color:#ec5300;Now limited to simple tasks.@@ * [[Program Analysis]]: gives proof of correctness in formal language ** we may find related techniques to design metrics for code generation evaluation * ''Grammar Inference'': traditional program analysis problem of extraction FSA from data ** can be reformulated in the language of machine learning, very close to language modelling * [[Software Analysis]] ! Algorithms and models * Domain-Specific Language based models ** Can we learn DSL with rule extraction? ** Learning from LM or NTM unless it can be done end2end. An idea is to use NTM for [[SimplePrograms|Program Synthesis for Character Level Language Modeling]] * [[Natual language processing|NLP]] ** seq2seq model and many [[Neural Machine Translation]] works can be used directly in program analysis * [[Sequential Models]] ** advanced architecture e.g. [[Memory Networks]] and [[Neural Abstract Machines]] are strong and effective * [[Reinforcement Learning]] ** automatic discovery of program context, better training framework and many other applications * [[Probabilistic Models]] ** training with unlabeled data. * [[Deep Generative Models]] ** Adversarial training in unsupervised setting * [[Autoregressive Models]] ** another sequence to sequence translation technique. ! Software applications ||!formal logic way|!deep learning way| |Code migration|pattern matching|[[Neural Machine Translation]]| |Code fixing|compiler theory|[[Language Modelling]]| |Code generation|DSL-based Models|[[Source Code Generating Models]]| |Security|program analysis|sequence classification and [[Reinforcement Learning]]| Other applications I can think of * testing * debugging ** bug localization: given a bug report, find the program elements related to the bug. * documentation ** * distributed system analysis * code optimization ! [[SE Data]]
[[Model Selection]]
AIC estimates the relative K-L distance of the likelihood function specified by a fitted candidate model, from the unknown true likelihood function that generated the data. The fitted model closest to the truth in the KL sense would not necessarily be the model which best fits the observed sample, since the observed sample can often be fit arbitary well by making the model more and more complex. The best KL model is the model that most accurately describes ''the population distribution and hence future samples from it''.
$$
AIC = -2\log(\mathcal L(\hat\theta|y))+2K.
$$
Let the "true" value of $\theta$ for model $g$ be $\theta_0$. If $g$ is the K-L best model, then the MLE $\hat\theta$ would estimate $\theta_0$.
Akaike showed that the critical issue for getting a applied K-L model selection criterion was to estimate
$$
E_yE_x[\log(g(x|\hat\theta(y)))],
$$
where $x$ and $y$ are independent random samples from the same distribution and both statistical expectations are taken w.r.t. truth ($f$), is the ''model selection target'' of all model selection approaches, based on K-L information.
Akaike (1973) showed that the maximized log-likelihood is biased upward as an estimator of it. He also found that under certain conditions, this bias is approximately equal to $K$, the number of estimatable parameters in the approximating model.
An approximately unbiased estimator of this target for large samples and "good" models is
$$
\log(\mathcal L(\hat\theta|data))-K.
$$
This result is equivalent to
$$
\log(\mathcal L(\hat\theta|data))-K = \text{constant}-\hat E_{\hat\theta}[I(f, \hat g)],
$$
where $\hat g=g(\cdot|\hat\theta)$.
An approximately unbiased estimate of $E_t(l(y))$ would be a constant plus $l-\text{tr}(\hat {\mathbf J}^{-1}\hat{\mathbf K})$. $\hat{\mathbf J}$ is an estimator for the corvatiance matrix of the parameters based on the ''second-derivatives matrix of $l$'' in the parameters and $\hat{\mathbf K}$ is an estimator based on the ''cross-products of first derivatives'' [[Claeskens & Hjort, 2008, pp. 26-7]]. They are asymptotically equal for the true model, so that the trace becomes approximately $p$.
!! Criteria related to AIC
It may be that the crucial AIC approximation is too optimistic and the resulting penalty for model complexity is too weak. AIC_c and AIC3 sometimes perform better. The Deviance Information Criterion has some relationship to AIC. Also, some criteria like Mallows' C_p, and leave-one-out cross-validation are asymptotically equivalent to AIC.
! Power metal * Orden Ogan: High rating. Plain melody, weak guitar sound, but iconic and quite epic
A class of distributions $F(\Theta)$ is called a ''polynomial family'' if
$$
\forall r, E_{X\in F(\Theta)}[X^r] = M_r(\Theta)
$$
where $M_r(\Theta)$ is a polynomial in $\Theta = (\theta_1, \theta_2, \dots, \theta_k)$.
This definition captures a broad class of distributions such as mixture models whose components are uniform, exponential, Poisson, Guassian or gamma functions.
If the ''moment generating function'' of $X$ defined as $\sum E[X^n]\frac{n!}{t^n}$ converges in a neighborhood of zero, it uniquely determines the probability distribution, i.e.
$$
\forall r, M_r(\Theta) = M_r(\hat\Theta)\Rightarrow F(\Theta)=F(\hat\Theta)
$$
Given a ring $R$, an ideal $I$ generated by $g_1, g_2, \dots, g_n\in R$ denoted by $I=\langle g_1, g_2, \dots, g_n\rangle$ is defined as
$$
I=\left\{\sum_i r_ig_i \mbox{ where } r_i\in R\right\}.
$$
A ''Noetherian ring'' is a ring such that for any sequence of ideals
$$
I_1\subseteq I_2\subseteq I_3\subseteq\dots,
$$
there is $N$ such that $I_N = I_{N+1}=I_{N+2}=\dots$.
''Hilbert's Basis Theorem'' If $R$ is a Noetherian ring, then $R[X]$ is also a Noetherian ring.
We will use this to prove that for any polynomial family, a finite number of moments suffice to uniquely identity any distribution in the family:
If the moment generating function convergences in a neighborhood of zero, there exists $N$ such that
$$
F(\Theta) = F(\hat\Theta)\Leftrightarrow M_r(\Theta) = M_r(\hat\Theta)\forall r\in 1, 2, \dots, N
$$
''Proof:'' Let $Q_r(\Theta, \hat\Theta) = M_r(\Theta)-M_r(\hat\Theta)$. Let $I_1 = \langle Q_1\rangle, I_2 = \langle Q_1, Q_2\rangle, \dots$. This is our ascending chain of ideals in $R[\Theta, \hat\Theta]$. We can invoke Hilbert's basis theorem and conclude that $R[X]$ is a Noetherian ring and hence, there is $N$ such that $I_N = I_{N+1}=\dots$, So far all $N+j$, we have
$$
Q_{N+j}(\Theta, \hat\Theta) = \sum_{i=1}^Np_{ij}(\Theta, \hat\Theta)Q_i(\Theta, \hat\Theta)
$$
for some polynomial $p_{ij}\in R[\Theta, \hat\Theta]$. Thus, if $M_r(\Theta) = M_r(\hat\Theta) for all $r\in 1, 2,\dots, N$.
Details: * dataset of late games change slowly: 5% * Dirichlet Noise at the root * reflect/rotating the board * MCTS works as a policy improvement operator * When MCTS cannot improve the policy, training is done. MCTS is not needed in run time. ! Computing the policy gradient Computing the improvement gradient $\pi-p$ requires gradient w.r.t. policy $p_\theta$ and policy-value network parameters $\theta$. We instead minimize $$ \mathcal L(\theta) = KL(\pi_\theta\|p_\theta)=\pi^T\log p_\theta-c $$ <<< AlphaGo Zero uses a quite different approach to deep RL than typical (model-free) algorithms such as policy gradient or Q-learning. By using AlphaGo search we massively improve the policy and self-play outcomes - and then we apply simple, gradient based updates to train the next policy + value network. This appears to be much more stable than incremental, gradient-based policy improvements that can potentially forget previous improvements. <<< David Silver [[Reddit AMA|http://www.reddit.com/r/MachineLearning/comments/76xjb5/ama_we_are_david_silver_and_julian_schrittwieser/]] The stableness of self-play is likely a direct result of using tree search. An RL agent may train unstably for two reasons: * It may forget pertinent information about positions that it no longer visits (change in data distribution) * It learns to exploit a weak opponent (or a weakness of its own), rather than playing the optimal move. Tree search helps because: * AlphaGo Zero uses the tree policy in the first 30 moves to explore positions. In our work we use a NN trained to imitate that tree policy. Because MCTS should explore all plausible moves, an opponent that tries to play outside of the data distribution that the NN is trained on will usually have to play some moves that the MCTS has worked out strong responses to, so as you leave the training distribution, the AI will gain an unassailable lead. * To overfit to a policy weakness, a player needs to learn to visit a state s where the opponent is weak. However, because MCTS will direct resource to exploring towards s, it can discover improvements to the policy at s during search. MCTS finds these improvements will be found before the neural network is trained to try to play to s. In a method with no look-ahead, the neural network learns to reach s to exploit the weakness immediately. Only later does it realise that V^pi(s) is only large because the policy pi is poor at s, rather than because V*(s) is large. from [[Thinking Fast and Slow with Deep Learning and Tree Search|https://arxiv.org/abs/1705.08439]]
[[link|http://arxiv.org/abs/1410.3831]]
* [[Continuous approximations to arithmetic functions]] * [[Inequalities]] * [[Fisher information metric]] * [[Fourier Transform]] * [[Wavelet Transform]]
Most second order methods require a line-search and cannot be used in the stochastic mode. A carefully tuned SGD is hard to beat on large classification problems. CG offers best combination of speed, reliability and simplicity for smaller accurate real-valued outputs.
[[Kullback-Leibler divergence]] between two GMMs is not analytically tractable, nor does any efficient computational algorithm exist.
The only method that really can estimate $D(f||g)$ for large values of $d$ with arbitrary accuracy is Monte Carlo simulation. The idea is to draw a sample $x_i$ from the pdf $f$ such that $E_f\log(f(x_i)/g(x_i))=D(f||g)$. The variance of the estimation error is $\frac{1}{n}\text{Var}_f\log(f/g)$.
To compute $D_{\text{MC}}(f||g)$, we need to generate the i.i.d. samples from $f$ by drawing discrete samples from $\pi_a$ and then continuous samples from gaussian component $f_{a_i}(x)$.
!! Managing /etc/*.pac* files Using interactive script [[dotpac|https://wiki.archlinux.org/index.php/Dotpac]]. Usage: `sudo dotpac`. !! Downloading files from a web directory With empty preceding folders ``` wget --no-parent -ckr http://www.example.com/files ``` hopefully without ``` wget -np -nH --cut-dirs 5 -r http://www.example.com/files ``` !! Untar multiple files ``` for f in *.tar.gz; do tar -zxvf $f; done ``` !! AUR upgrade ``` yaourt -Su --aur ```
1st part of [[Theory and Algorithms for Forecasting Non-Stationary Time Series]]
! Definition
* Stationarity: distribution invariant to time
* Weak Stationairy:
** 1st moments: $\mathbb E[Z_t]$ is independent of $t$
** 2nd momments: $\mathbb E[Z_tZ_{t-j}] = f(j)$
* Lag operator $\mathfrak LY_t = Y_{t-1}$
! Model
* Autoregression: a linear combination of $p$ history
* Moving average
* ARMA: $Y_t = \sum_{i=1}^pa_iY_{t-i}+\epsilon_t+\sum_{j=1}^qb_j\epsilon_{t-j}$
where $\epsilon$ is the noise term. We can rewrite ARMA in terms of the lag operator:
$$
(1-\sum_{i=1}^pa_i\mathfrak L^i)Y_t = (1+\sum_{j=1}^q)b_j\mathfrak L^j)\epsilon_t
$$
L.H.S. is the characteristic polynomial $P(\mathfrak L)$.
<<<
''Theorem'' [Weak Stationarity of ARMA]<br>
An ARMA(p, q) process is weakly stationary if the roots of the characteristic polynomial $P(z)$ are outside the unit circle.
<<<
ARIMA(p, D, q) model is an ARMA(p, q) model for $(1-\mathfrak L)^DY_t$:
$$
(1-\sum_{i=1}^pa_i\mathfrak L^i)(1-\mathfrak L)^DY_t = (1+\sum_{j=1}^q)b_j\mathfrak L^j)\epsilon_t
$$
Other extensions
* seasonal components (SARIMA)
* side information (ARIMAX)
* long-memory (ARFIMA)
* multi-variate time series models (VAR)
* time-varying coefficients
* other non-linear models
! Estimation
* Maximum likelihood
** Requires further parametric assumptions on the noise distribution
* Method of moments
** Yule-Walker estimator
* Conditional/unconditional least square estimation
** Restricted to certain models.
!! Bounds
<<<
''Definition'' [Invertibility of ARMA]<br>
An ARMA(p, q) process is invertible if the roots of the polynomial
$$
Q(z) = 1+\sum_{j=1}^q b_jz^j
$$
are outside the unit circle.
<<<
If the ARMA model is weakly stationary and invertible, the least square estimate will converge
Submission Password: DvQMF9N8BK The programming assignment environment uses `python2`. So first activate [[Python virtualenv]] ``` source ~/virtualenvs/python2/bin/activate ``` Then enter `ipython2`, under interactive command: ``` In [1]: import A3Part1 In [2]: ``` Finally, submit with `python submitA3.py`.
!! Linearity
$$
DFT(ax_1[n]+bx_2[n]) = aX_1[k]+bX_2[k]
$$
!! Shift
$$
DFT(x[n-n_0]) = e^{-j2\pi kn_0/N}X[k]
$$
!! Symmetry
|!part|!$x[n]$ real|!$x[n]$ real and even|
|$\mathfrak R\{X[k]\}$ real part|even|even|
|$\mathfrak I\{X[k]\}$ imaginary part|odd|0|
|$\lvert X[k]\rvert$ magnitude|even|even|
|$<X[k]$ phase|odd|0|
!! Convolution
$$
DFT(x_1[n]*x_2[n]) = X_1[k]\times X_2[k]
$$
$$
DFT(x_1[n]\times x_2[n]) = X_1[k]*X_2[k]
$$
!! Energy conservation & decibels
The energy of a signal can be measured in time/frequency domain
$$
\sum_{n=-N/2}^{N/2-1}|x[n]|^2=\frac{1}{N}\sum_{k=-N/2}^{N/2-1}|X[k]|^2
$$
Decibel: $20\log(|X|)$
!! Phase unwrapping
For visualizing the phase spectrum, when reaching $2\pi$, let it grow.
!! Zero padding
Zero-padding a signal is done by adding zeros at the end of the signal.
By adding samples of zeros. The spectrum will be smooth. We will get more interpolated samples in between.
!! Fast Fourier Transform
Coley-Tukey algorithm: breaks down recursively the DFT of a power of 2 size into two pieces of size N/2.
!! FFT and zero-phase windowing
Split the signal and add zero to the center. We avoid getting shifting distortion would occur if we had not centered all the samples.
!! Analysis/synthesis
Fundamental challenges in RL:
* Exploration vs exploitation
* Dealing with delays
Functional approximation in RL with NN. (NeuroDynamicProgramming, Tdgammon). Combine DL and RL.
Atari game challenges:
* sequential decision making
* partial observability
* delayed reward (above handled by RL)
* high dimensional observations
* multiple interacting objects (above handled by DL)
Variety of:
* perspection
* rules
* dynamics
! Related work summary
!! Deep Q Network by DeepMind
CNN + Q-Learning.
The system is not in real-time. Game pauses after each frame, Use Monte-Carlo Tree Search (UCT) for computing approximate Q-values.
!!! UCT-based Planning Agent
Action selection after planning:
$$
\pi^{uct}(s) = \mbox{argmax}_aQ(s, a)
$$
To expand the search tree:
$$
\pi(s_1') = \mbox{argmax}_a Q(s_1', a) + c_{ucb}\sqrt{\log(n(s_1'))/n(s_1', a)}
$$
* $Q(s_1', a)$: estimated value of action a
* $n(s_1')$: number of times state $s_1'$ has been visited
* $n(s_1', a)$: number of times action a was selected under $s_1'$
* $c_{ucb}$: exploitation vs exploration
The complexity can be measured by depth (0.3k) and trajectories (10k).
!! Combining with deep learning
Compress UCT policy/value function into a CNN.Converting CNN output to:
* Q-value
* UCT result.
The problem is training player dist is different from real UCT states. Several CNNs are stacked to reduce the error.
!! DeepMind's Nature
With larger model and more training, best real-time performance right now.
! This work
Can we learn a transition model with such high dimensional observations?
With encoder-decoder. The
Multi-head attention allows the model to jointly attend to information from different representation subspaces at different positions. To prevent leftward information flow, all values in the input of the softmax are masked out. ! Remarks * TODO: this attention should work with segmentation. (Probably too much computation, how does this compared to CRF?) * TODO: attention in stock market? * Multi-level attention for detection and other large scale memories?
! Bibs
* [[Visual Attention]]
Sequential attention
* NIPS 2015 [[Grammar as a foreign language|https://arxiv.org/abs/1412.7449]]: parsing text
** where it allows the model to glance at words as it generates the parse tree
* 2015 [[A Neural Conversational Model|https://arxiv.org/abs/1506.05869]]
** it lets the model focus on previous parts of the conversation as it generates its response
* [[Using Fast Weights to Attend to the Recent Past]]
* [[Attentive Recurrent Comparators]]
* [[Online and Linear-Time Attention by Enforcing Monotonic Alignments|https://arxiv.org/abs/1704.00784]]: [[blog|http://colinraffel.com/blog/online-and-linear-time-attention-by-enforcing-monotonic-alignments.html]]
! Tutorials
* [[Attention and Augmented Recurrent Neural Networks|https://distill.pub/2016/augmented-rnns/#attentional-interfaces]]
! Computing
We need a weight for every column: this is an $|\mathbf f|$-length vector $\mathbf a_t$.
* Deterministic soft attention by weighted average: A simplified version of [[Bahdanau et al.|Neural Machine Translation by Jointly Learning to Align and Translate]]'s solution is with previous hidden states $\mathbf s_{t-1}$, compute the ''expected input embedding'' $\mathbf r_t = V\mathbf s_{t-1}$, then we take product with the source matrix to get the ''attention energy'', $\mathbf u_t = F^T\mathbf r_t$ and exponentiate and normalize to 1: $\mathbf a_t=\text{softmax}(\mathbf u_t)$. Finally the ''input source vector'' $\mathbf c_t=F\mathbf a_t$.
* Stochastic hard attention (Xu et al., 2015) by sampling a column: $\mathcal L=-\log p(\mathbf w|\mathbf x) = -\log\sum_{\mathbf s}p(\mathbf s|\mathbf x)p(\mathbf w|\mathbf x, \mathbf s)\le-\sum p(\mathbf s|\mathbf x)\log p(\mathbf w|\mathbf x, \mathbf s)$. With Jensen's inequality, we can minimize a upper bound. This can be sampled with MC:
** $-\sum_sp(s|x)\log p(w|x, s)\approx-\frac 1 N\sum_{i=1}^Np(s^i|x)\log p(w|x, s)$
** Sample $N$ sequences of attention decisions from the model
** The gradient is the probability of the gradient of the probability of this sequence scaled by the log probability of generating the target words using that sequence of attention decisions
** This is equivalent to using the [[REINFORCE]] algorithm (Williams, 1992) using the log probability of the observed words as a "reward function".
* Location based attention [[Bahdanau et al., 2014|Neural Machine Translation by Jointly Learning to Align and Translate]] 2k+ references. The inner product in attention energy part is replaced by a MLP: $\mathbf u_t = \mathbf v^T\tanh(WF+\mathbf r_t)$.
The method described above is called "early binding", means the attention is computed from previous state. An alternative is we comput hidden states without attention but only apply it at the decoding stage. "late binding" is too late but good for computation. At training time, gradient of attention is independent with the hidden states. Thus easier to parallel.
Attention also clipped the gradient thus help dealing with forgetting problem.
! Comments
Attention only cares about content
* No obvious bias in favor of diagonals, short jumps, ferfility, etc.
* Some work has begun to add other "structural" biases (Luong et al., 2015; Cohn et al., 2016), but there are lots more opportunities. @@color:#859900;Seq2tree works here, too. Putting linguistic features into neural networks@@
In practice, attention still can go wild, partly because of the misleading of BLEU score, especially when source sentence is short.
* [[Visual Attention]] ! Sequential Generative Models refs: * [[DRAW: A Recurrent Neural Network For Image Generation|https://arxiv.org/abs/1502.04623]] * [[Attend, Infer, Repeat: Fast Scene Understanding with Generative Models|https://deepmind.com/research/publications/attend-infer-repeat-fast-scene-understanding-generative-models/]] ** understanding 3D scenes ** where is the attention is yet to find
[[blog|https://medium.com/@sanyamagarwal/understanding-attentive-recurrent-comparators-ea1b741da5c3]] with an excellent implementation. ! Dynamic representations
* [[Julius Orion Smith III|https://ccrma.stanford.edu/~jos/]]
[[ASP Programming]]
!! Lecture 1
Frequency in radians is $2\pi k/N$.
!! Lecture 2
DFT of complex sinusoid
$$
x_1[n]=\exp(-j2\pi k_0n/N) \qquad n=0, \dots, N-1
$$
$$
X_1[k]=\sum_{n=0}^{N-1}x_1[n]\exp(-j2\pi kn/N)=\frac{1-\exp(-j2\pi(k-k_0))}{1-\exp(-j2\pi(k-k_0)/N)}
$$
$X_1[k]=N$ for $k=k_0$ and $X_1[k]=0$ for $k\neq k_0$
Inverse DFT
$$
x[n]=\frac 1 N\sum_{k=0}^{N-1}X[k]\exp(j2\pi kn/N) \qquad n=0,1,\dots,N-1
$$
Why the normalization factor?
!!! Quizes
# DFT of real sinusoids
#* $\frac{A_0}{2}$ for $k=\pm k_0$ and $0$ for rest.
# IDFT
#* Int values of $s_k[n]$ for $N=4$: $[1,1,1,1],[1,j,-1.-j],[1,-1,1,-1],[1,-j,-1,j]$.
!! Lecture 3
[[ASP Theory 3]]
!!! Quizes
# Symmetry
# phase unwrapping
# zero-phase windowing
# IDFT result: exactly the same.
* [[WaveNet]] * [[Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders]] * [[Pixel-CNN]] * [[SampleRNN]] * Video Pixel Network * [[Parallel Multiscale Autoregresssive Density Estimation]] * ByteNet * [[A Neural Parametric Singing Synthesizer|http://arxiv.org/abs/1704.03809v1]] ** What is the Causual conv input? ** How's the model trained ** How's the time sequence represented ** Hints for OCR context? * [[VQ-VAE]] Autoregressive models are trained and evaluated by teacher-forcing, meaning the model receives as input the true values of the variables being conditioned on. During generation, however, these variables are not known, and the model conditions on its own best predictions of their values. This difference between training/evaluation and generation is a known source of trouble: the model only ever learns one-step transitions, and never learns to deal with its own errors. At generation time it can easily run off the tracks.
Compiled awesome 4 on Ubuntu. Use `Win`+`s` for the help
! Stochastic vs batch learning According to chapter 1 of [[Neural Networks: Tricks of the Trade]], stochastic learning is faster and results better solution because less redundant data is used during the training. While the convergence conditions and other theoretical analyses are easier to conduct on batch learning.
A band-pass filter is a device that passes frequencies within a certain range and rejects (attenuates) frequencies outsides that range.
[[link|http://arxiv.org/abs/1502.03167]] ! Bib * [[Batch Renormalization]] * [[Recurrent Batch Normalization]] ! Problem ; Internal covariate shift : The distribution of each layer's input change as a function of previou layer parameters. [img[https://qph.is.quoracdn.net/main-qimg-89eac2a45ba389b24a0c29ad84209b8f?convert_to_webp=true]] This slows down training. To solve this problem, intermediate layers of deep nets should be constraint by whitening or use batch normalization. In long column analogy, you have two options for constrains:Fixed (Fig-c) and Pinned (Fig-d). If intermittent supports are fixed with column, support have to carry bending as well as lateral load, which usually are used in concrete column and beam monolithic configuration. If intermittent supports are pinned, such supports resist only lateral loads on compromise of column rotation at that location. Such connections are used in steel structure. Similarly, we have two options to apply whiten constrained in every hidden layers: Fixed and Pinned. For fixed whiten constrains, we have to apply such whitening in every hidden layer such that before proceed to the next layer, it will be whiten. But, zero mean, unit variance with decorrelation for every dimension for every layer and every gradient update with batch gradient descent and computation of correlation matrices is very storage and computation intensive. So, alternatively for pinned case similar to rotation flexibility of column section, we can introduce some mean (shift) and variance (scale) for every layer and every dimension, which will be trained as well with weights and bias parameters of the networks through backpropagation. Extra flexibility will increase learning capacity. But, computational complexity remains high. Batch Normalization solves such problem with some additional assumptions. Followings are the properties of Batch Normalization with mean and variance for a mini batch version: # Learning faster: Learning rate can be increased compare to non-batch-normalized version. # Increase Accuracy: Flexibility on mean and variance value for every dimension in every hidden layer provides better learning, hence accuracy of the network. # Normalization or Whitening of the inputs to each layer: Zero means, unit variances and or not decorrelated. # To remove the ill-effect of Internal Covariate shift:Transformation makes data to big or to small; change of the input distribution away from normalization due to successive transformation. # Not-Stuck in the saturation mode: Even if ReLU is not used. # Integrate Whitening within the gradient descent optimization: Decoupled Whitening between training steps, which modifies network directly, reduces the effort of optimization. So, model blows up when normalization parameters are computed outside the gradient descent step. # Whitening within gradient descent: Requires inverse square root of covariance matrix as well as derivatives for backpropagation # Normalization of Individual dimension: Individual dimension of hidden layers are normalized independently rather than joint covariances. So, features are not decorrelated. # Normalization of mini-batch: Estimation of mean and variance are computed after each mini-batch rather than entire training set. Even ignoring the joint covariance as it will create singular co-variance matrices for such small number of training sample per mini-batch compare to high dimension size of the hidden layer. # Learning of scale and shift for every dimension: Scaled and shifted values are passed to the next layer, whether mean and variances are calculated after getting all mini-batch activation of current layer. So, forward pass of all the samples within the mini-batch should pass layer wise. Backpropagation is required for getting gradient of weights as well as scaling (variance) and shift (mean). # Inference: During inference moving averaged mean and variance parameters during mini batch training are considered. # Convolution Neural Network: Whitening of intermediate layers, before or after the nonlinearity creates a lot of new innovation pathways ! Remarks Besides the reduction of internal covariate shift, in a Gaussian, linear case, the gradients flow through the network, do not explode or vanish.
BN drawbacks:
* not accurate for small minibatches
* non-i.i.d. minibatches can overfit (batch norm is not accurate?)
''remark'': suppose these can be reduced by `global_stats`
Using moving average may cause the model to blow up, because the bias or scale may increase without restrictions.
Batch Renormalization ensures the activations computed in the forward pass of the training step depend only on a single example and are identical to the activations computed in inference.
BR normalize like this:
$$
\frac{x_i-\mu}{\sigma} = \frac{x_i-\mu_B}{\sigma_B}\cdot r+d, \qquad r = \frac{\sigma_B}{\sigma}, d = \frac{\mu_B-\mu}{\sigma}
$$
Gradient does not propagate through $r$ and $d$, @@color:#859900;this correct for the fact that the minibatch statistics differ from the population ones.@@
! Setup
Let us consider discrete (categorical) HMMs of length $T$ (each observation sequence is $T$ observations long). Let the space of observations be $X = \{1, 2, \dots, N\}$, and let the space of underlying states be $Z = \{1, 2, \dots, M\}$. An HMM $\theta = (\pi, A, B)$ is parameterized by the initial state matrix $\pi$, the state transition matrix $A$, and the emission matrix $B$;
$\pi_i = P(z_1 = i)$, $A_{ij} = P(z_{t+1} = j|z_t = 1)$, and $B_i(j) = P(x_t = j|z_t = i)$. See [[PRML]] for a more detailed treatment of HMMs.
We study the probability of learning the parameterization of $\theta$ from a dataset of $D$ observations. Let $\mathcal X = (X^1, \dots, X^D)$, where each $X^i = (x_1^i, x_2^i, \dots, x_T^i)$. We assume each observation is drawn iid. The learning problem is nontrivial because we are not given the latent variables $Z^i$ for each $X^i$, otherwise we could directly compute $\theta^* = \text{argmax}_\theta P(\mathcal X, \mathcal Z;\theta)$. And there are too many values of $z$ to try.
! Baum-Welch
Baum-Welch can be described as repeating following steps until convergence:
# Compute $Q(\theta, \theta^s)=\sum_{z\in\mathcal Z}\log[P(\mathcal X,z;\theta)]P(z|\mathcal X;\theta^s)$.
# Set $\theta^{s+1} = \underset{\theta}{\text{argmax}}Q(\theta, \theta^s)$.
$$
\underset{\theta}{\text{argmax}}Q(\theta, \theta^s) = \underset{\theta}{\text{argmax}}\sum_{z\in\mathcal Z}\log[P(\mathcal X,z;\theta)]P(z, \mathcal X;\theta^s)
$$
since $P(\mathcal X)$ is not affected by the choice of $\theta$.
$$
P(z,\mathcal X;\theta) = \prod_{d = 1}^D\left(\pi_{z_1^d}B_{z_1^d}(x_1^d)\prod_{t=2}^TA_{z_{t-1}^dz_t^d}B_{z_t^d}(x_t^d)\right)
$$
We can optimize with Lagrange multipliers requiring that $\pi, A_i, B_i(\cdot)$ form valid probability distributions.
$$
\hat L(\theta, \theta^s) = \hat Q(\theta, \theta^s)-\lambda_\pi\left(\sum_{i=1}^M\pi_i-1\right)-\sum_i^M\lambda_{A_i}\left(\sum_{j=1}^MA_{ij}-1\right)-\sum_i^M\lambda_{B_i}\left(\sum_{j=1}^NB_{i}(j)-1\right)
$$
$$
\begin{align*}
\frac{\partial\hat L(\theta, \theta^s)}{\partial\pi_i}&=\\
&=
\end{align*}
$$
! Bib * [[Maximum likelihood approaches to variance component estimation and to related problems|http://www.lce.esalq.usp.br/arquivos/aulas/2012/LCE5872/Harville1977.pdf]]: The distinction between frequentist and Bayesian models is practically none !! Textbooks * [[Bayesian Choice]] ! Applications [[Bayesian information criterion]] ! Theory * [[Inference Methods]]
1.3.5 [[Maximum Likelihood Estimation]] [[5.2.6 Pseudo-Bayes factors]] [[5.3.2 Least favorable prior distributions]] [[6 Bayesian Calculations]] [[8.2 Admissibility of Bayes estimators]]
! History
First Bayesian NNs are training with Laplace's method. Neal uses HMC. In late 90s, VI is used.
Modern VI relied on a fully factorized approximation, assuming independence of each weight scalar in each layer from all other weights.
Expectation propagation is recently studied, relying on various forms of Renyi's $\alpha$-divergence. Seems to sacrifice their estimation of the dominant modes of the posterior.
! Introduction
Bayesian deep learning is
<<<
Posterior inference of layered representations of high-dimensional data
<<< Ranganath+, AISTATS 2015
Topics
* Deep exponential families: $z_{n,l,k}\sim \text{Exp-Fam}(g(w^\top_{l,k}z_{n,l+1}))$
Bayesian methods give RNNs another way to express their uncertainty. Using a prior to integrate out the parameters to average across many models acts as a regularizer. * [[Dropout as a Bayesian Approximation]] * weight decay as a variational inference scheme * [[Stochastic Gradient Langevin Dynamics]] to truncated backpropagation in time directly. * [[Stochastic Gradient Descent as Approximate Bayesian Inference]] * Bayes by Backprop ** with centralized Gaussian, KL term can be seen as a form of weight decay, tuned by the std fo the prior and posterior. * [[Bayesian Recurrent Neural Networks]]
Bayesian information criterion (BIC), also known as the Schwarz criterion is derived from an asymptotic expression of the Bayes factor. ! Applications * [[KL-Based Changepoint]]
[[paper|http://arxiv.org/abs/1704.02798]] remark: MCMC solution is given. What abount VI and Evolution Strategies
[[Bayesian information criterion]]
!!Model Selection
We can use Bayes' Theorem to find the posterior of each model. We assume equal prior probabilities for each model then the ratio of the odds becomes the Bayes factor $B_{ij} = P(M_i|y)/P(M_j|y)$. [[Schwarz 1978]] showed that in many kinds of models $B_{ij}$ can be roughly approximated by $\text{exp}(-\frac{1}{2}BIC_i+\frac{1}{2}BIC_j)$
The derivation of BIC holds both the model set and the data-generating model fixed as sample size goes to infinity. It is also clear that if the model set contains the true (generating) model, them BIC selection converges with probability 1 to that generating model as $n\rightarrow \infty$ (and the posterior probability of that model goes to 1). [[Cavanaugh and Neath (1999)]] make it clear that the derivation of BIC does not require the true model being in the set.
The difference between AIC and BIC is the $\log(n)$ in BIC, which is needed for idealized asymptotic consistency. BIC assumes equal prior probability for each model. The marginal probability is
$$
\int\left[\prod_{i=1}^ng(x_i|\theta)\right]\pi(\theta)d\theta
$$
Under ''general regularity conditions'', as sample size increases the likelihood function "near" the MLE, $\hat\theta$ can be approximated as
$$
\mathcal L(\theta|x, g) = \mathcal L(\hat\theta|x, g)\exp(-\frac{1}{2}(\theta-\hat\theta)'V(\hat\theta)^{-1}(\theta-\hat\theta)).
$$
where $V(\hat\theta)$ is the estimated $K\times K$ variance-covariance matrix of the MLE.
!!Segmentation
The BIC is probably the most extensively used segmentation and clustering metric due to its simplicity and effectiveness. It is likelihood criterion penalized by the model complexity (number of free parameters in the model) introduced by [[Schwarz (1971)]] and [[Schwarz (1978)]] as a model selection criterion. For a given acoustic segment $X_i$, the BIC value of a model $M_i$ applied to it indicates how well the model fits the data, and is determined by:
$$
BIC(M_i) = \log L(X_i, M_i)-\lambda\frac{1}{2}\#(M_i)\log(N_i)
$$
$\log L(X_i,M_i)$ is the log-likelihood of the data given the considered model. $\lambda$ is a free design parameter dependent on the data being modeled, estimated using development data; $N_i$ is the number of frames in the considered segment and $\#(M_i)$ the number of free parameters to estimate in model $M_i$. Such expression is an approximation of the Bayes Factor (BF) ([[Kass and Raftery (1995)]], [[Chickering and Heckerman (1997)]]) where the acoustic models are trained via ML methods and $N_i$ is considered big.
In order to use BIC to evaluate whether a change point occurs between both segments it evaluates the hypothesis that $X$ better models the data versus the hypothesis that $X_i+X_j$ does instead, like in the GLR ([[Generalized Likelihood Ratio]]), by computing:
$$
\Delta BIC(i,j) = -R(i, j)+\lambda P
$$
where $P$ is the penalty term, which is a function of the number of free parameters in the model. For a full covariance matrix it is
$$
P =\frac{1}{2}(p+\frac{1}{2}p(p+1)\log(N)
$$
The penalty term accounts for the likelihood increase of bigger models versus smaller ones. The term $R(i)$ can be written for the case of models composed on a single Gaussian as:
$$
R(i, j) =\frac{N}{2}\log|\sum_X|-\frac{N_i}{2}\log|\sum_{X_i}|-\frac{N_j}{2}\log|\sum_{x_j}|
$$
For cases where GMM models with multiple Gaussian mixures are used, $\Delta BIC$ is written as
$$
\Delta BIC(M_i) = \log L(X, M)-(\log L(X_i, M_i)+\log L(X_j, M_j))-\lambda\Delta\#(i, j)\log(N)
$$
where $\Delta\#(i, j)$ is the difference between two $BIC(i)$ criteria in the combined model versus the two individual models.
```
@inproceedings{chan1999training,
title={Training recurrent network with block-diagonal approximated Levenberg-Marquardt algorithm},
author={Chan, Lai-Wan and Szeto, Chi-Cheong},
booktitle={Neural Networks, 1999. IJCNN'99. International Joint Conference on},
volume={3},
pages={1521--1526},
year={1999},
organization={IEEE}
}
```
* [[Variational Inference for Generative Models]] * [[CNN for Video Classification]] * [[Generative Adversarial Network Talk]] * [[Guaranteed Non-convex Learning Algorithms through Tensor Factorization]] * [[On Optimization in Deep Learning]] * [[Scattering Representation]] * [[KL-Based Changepoint]] ! Planned * [[Real Time Change-Point Detection]] * [[Dropout as a Bayesian Approximation]]
* [[Bayesian Choice]] * [[Pattern Recognition and Machine Learning]] * [[Model selection and multimodel inference]] For deep learning: * [[Deep Learning|Deep Learning (textbook)]] * [[DNN for ASR]] * [[Neural Networks: Tricks of the Trade]]
[[Default Hotkeys|https://github.com/fuhsjr00/bug.n/blob/master/doc/Default_hotkeys.md]] |Decrease the gap between windows|`Win`+`Shift`+`Left`| |Set the previous-ly set layout|`Win`+`Tab`| |Set floating/monocle/tile layout|`Win`+`f`/`m`/`t`| |Toggle active window floating|`Win`+`Shift`+`f`| |Rotate the layout axis|`Win`+`Ctrl`+`t`| |Open the command GUI|`Win`+`y`|
! Workarounds !! Multilabel # As comparison, what would be the closest way to implement this functionality with existing layers? I presume a inner product that has num_output: N*K, re-size into (N,K) and then do a normal softmax that compares this to a (N,1) label. # And how is this PR better than that. # See [[this example|http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/pascal-multilabel-with-datalayer.ipynb]] for accuracy test !! Invalid Device Ordinal ! Variations * [[MS Caffe|https://github.com/Microsoft/caffe/tree/master]]: for Faster-RCNN and R-FCN * [[mem caffe|https://github.com/yjxiong/caffe/tree/mem#features]]: MPI !! Merging the 2 move layer files into /layers * net.hpp ** ForwardPrefilled: deprecated in ms caffe, replaced with Forward ** input_blob is set before calling Forward ** run memoryoptimize ** add learnable parameters * syncedmem.hpp ** add use_cuda flag to CaffeMallocHost ** cudaMallocHost improves performance and stability in multi GPU training * _caffe.cpp ** python_layer moved to layers * common.cpp ** cluster_seedgen(bool sync) * base_data_layer.cpp ** StartInternalThread replace CreatePrefetchThread
! Bibs * [[Dynamic Routing between Capsules|https://research.google.com/pubs/pub46351.html]] ! [[What is wrong with CNN|https://www.youtube.com/watch?v=Mqt8fs6ZbHk]]? * Too few levels of structure ** Neurons, layers, whole nets666 ** For example, we want to have a unit that can detect correlations between input ** Vector nonlinearity like softmax, analyse the covariance structure * We need to group the neurons in each layer into "capsules" that do a lot of internal computation and then output a compact result ** A capsule is inspired by a mini-column !! Capsule Connection binding problem done in sequence. Each capsule represents the presence and the instantiation parameters of a multi-dimensional entity of the type that the capsule detects. In the visual pathway, a capsule detects a particular type of object or object-part. It outputs 2 things: * The probability that an object of that type is present * The generalized pose of the object, includes position, orientation, scale, deformation, velocity, color etc. Convnet problem * feature extraction layers are interleaved with subsampling * relationships between objects not trained * the geometric view of vision is discarded Map the percept to the initial hidden state of a deep recurrent neural net and train the RNN to output captions (without retraining the convnet) ! Remarks * Capsule is like a convolutional Gaussian process? Can convolutional kernels capture covariance? * Attention may not be good for high dimensional data
There are several architectures in the field of Convolutional Networks that have a name. The most common are: # ''LeNet''. The first successful applications of Convolutional Networks were developed by Yann LeCun in 1990's. Of these, the best known is the LeNet architecture that was used to read zip codes, digits, etc. # [[AlexNet|Doubts on Caffenet]]. The first work that popularized Convolutional Networks in Computer Vision was the AlexNet, developed by Alex Krizhevsky, Ilya Sutskever and Geoff Hinton. The AlexNet was submitted to the ImageNet ILSVRC challenge in 2012 and significantly outperformed the second runner-up (top 5 error of 16% compared to runner-up with 26% error). The Network had a similar architecture basic as LeNet, but was deeper, bigger, and featured Convolutional Layers stacked on top of each other (previously it was common to only have a single CONV layer immediately followed by a POOL layer). # ''ZF Net''. The ILSVRC 2013 winner was a Convolutional Network from Matthew Zeiler and Rob Fergus. It became known as the ZF Net (short for Zeiler & Fergus Net). It was an improvement on AlexNet by tweaking the architecture hyperparameters, in particular by expanding the size of the middle convolutional layers. # [[GoogLeNet|GoogLeNet]]. The ILSVRC 2014 winner was a Convolutional Network from Szegedy et al. from Google. Its main contribution was the development of an Inception Module that dramatically reduced the number of parameters in the network (4M, compared to AlexNet with 60M). # [[VGGNet]]. The runner-up in ILSVRC 2014 was the network from Karen Simonyan and Andrew Zisserman that became known as the VGGNet. Its main contribution was in showing that the depth of the network is a critical component for good performance. Their final best network contains 16 CONV/FC layers and, appealingly, features an extremely homogeneous architecture that only performs 3x3 convolutions and 2x2 pooling from the beginning to the end. It was later found that despite its slightly weaker classification performance, the VGG ConvNet features outperform those of GoogLeNet in multiple transfer learning tasks. Hence, the VGG network is currently the most preferred choice in the community when extracting CNN features from images. In particular, their pretrained model is available for plug and play use in Caffe. A downside of the VGGNet is that it is more expensive to evaluate and uses a lot more memory and parameters (140M). # [[ResNet]]. The ILSVRC 2015 winner. # [[DiracNet]] Prototype networks: # Maxout # NIN # DropConnect # [[Densely Connected Convolutional Networks|https://github.com/liuzhuang13/DenseNet]] # [[Interleaved Group Convolution|https://arxiv.org/abs/1707.02725]]
@article{casella1996rao,
title={Rao-Blackwellisation of sampling schemes},
author={Casella, George and Robert, Christian P},
journal={Biometrika},
volume={83},
number={1},
pages={81--94},
year={1996},
publisher={Biometrika Trust}
}
Denote by $\mathbf x=\{x_j\in\mathbb R, j=1, \dots,n\}$ a multivariate time series of length $n$, and assume that some of its characteristics are changing at $k-1$ instants $t_1,t_2,\dots,t_{k-1}$, with the convention $t_0=0$ and $t_k=n$. To reflect this situation, we assume that the distribution of $\mathbf x$ is defined piecewise on the resulting $k$ segments by $\xi$. The model is defined as
$$
\mathcal M_\theta:\left\{ \begin{array}{l}
p(\mathbf x|\mathbf \xi) = \prod_{i=1}^kp(x_{t_{i-1}+1},\dots,x_{t_i}|\xi_i)\\
\pi(\mathbf \xi|\mu) = \prod_{i=1}^k\pi(\xi_i|\mu)
\end{array}\right.
$$
where $\pi(\cdot|\mu)$ denotes an [[informative prior distribution|Prior Classes]]. We follow a model selection strategy that involves choosing the model with the largest posterior probability:
$$
p(\mathcal M_\theta|\mathbf x)\propto\pi(\mathcal M_\theta)\int p(\mathbf x|\mathbf \xi)\pi(\mathbf \xi|\mu)d\mathbf \xi
$$
The Bayesian framework is useful for the general purpose of model selection because it offers the possibility of computing a posterior model probability on the set of all possible models, provided some prior probability can be established on this set. An alternative is to base model selection on the Bayes factor, which asymptotically leads to the same result.
!! [[BIC|Bayesian information criterion]]
[reynolds04] A penalized likelihood ratio test is used to compare whether the data within a fixed window are better modeled via a single Gaussian or two Gaussians. The window gradually grows at each test until a changepoint is inferred, at which point the window is reinitialized at the inferred changepoint.
[[BIC Changepoint]]
!! KL divergence
[siegler97] uses fixed length windows and computes the symmetric KL divergence between a pair of Gaussians each fit by the data in their respective windows. A post-processing step then sets the changepoints equal to the peaks of the computed KL that exceed a predetermined threshold.
[[KL Changepoint]]
!! Generalized Likelihood Ratio (GLR)
[[GLR Changepoint]]
@article{chen2014voice,
title={Voice conversion using deep neural networks with layer-wise generative training},
author={Chen, Ling-Hui and Ling, Zhen-Hua and Liu, Li-Juan and Dai, Li-Rong},
journal={IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)},
volume={22},
number={12},
pages={1859--1872},
year={2014},
publisher={IEEE Press}
}
Since 2008, have used 18 players in center. Dexter Fowler provides the ability to get on base from the leadoff position.
! Learning clojure [[Learn clojure in Y Minutes]] !! Websites [[4clojure]] [[braveclojure.com|http://www.braveclojure.com]] ! Properties [[Transducer]] [[clojure Snippets]] ! Examples * [[cljs quick start|https://github.com/clojure/clojurescript/wiki/Quick-Start]] * [[Compojure Address Book]] * [[Re-Frame]] ! Tools * [[Om]] * [[Datomic]] * [[Overtone]] * Figwheel ** Figwheel autoreloads and compiles your clojurescript code so that you can live code and see your changes update in the browser. It also comes with a repl for your clojurescript so you can evaluate your code there. This will come in handy. * [[Reagent]] ** Reagent wraps the React.js library and provides a neat Clojurescript interface to it. This is my first time using it and it’s been very simple to pick up and use. * Ring ** Ring is a simple abstraction for HTTP in Clojure. It allows you to create a webserver, define middleware etc. ! Engineering Projects [[Onyx|https://github.com/MichaelDrogalis/onyx]]
Empty seqeuence
```clojure
user=> (every? empty? ["" [] () '() {} #{} nil])
true
;example of recommended idiom for testing if not empty
user=> (every? seq ["1" [1] '(1) {:1 1} #{1}])
true
```
First read this post [[Understanding CNN for NLP|http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/]] And this course： [[Stanford CNN for NLP]] [[A Convolutional Neural Network for Modelling Sentences]]: A good paper to read. The model is kind of too complex. [[Convolutional Neural Networks for Sentence Classification]]: A one-layer CNN approach with simplicity and empirical performance. This has become the standard baseline for new text classification publications. ! NMT * [[Convolutional Sequence to Sequence Learning]] ! Practices * [[Daily News for Stock Market Prediction]]
To efficiently perform text spotting, the majority of methods follow the intuitive process of splitting the task in two: text detection followed by word recognition. ! Text Detection Detecting instances of words in noisy and cluttered images is a highly non-trivial task. !! Generative Models * [[Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data|https://arxiv.org/abs/1611.02788]] !! Character region methods Segments pixels into characters, and then group characters into words. Previous methods are: * finding images with constant stroke width (the distance between two parallel edges) with stroke width transform (SWT). Then clustering pixels together should form words. * Extreme Regions * Maximally Stable Extremal Regions with CNN classifier to prune Extreme Regions !! Sliding window methods * Random fern classifier on HoG features, grouped into words with a pictorial structures framework * CNNs trained for character classfication can be used as sliding window classifiers ! Papers # [[Reading Text in the Wild with CNN]]: best performance # [[Synthetic Data for Text Localisation in Natural Images]]: best precision and efficiency tradeoff # LSTM attention based models for various curvatures # [[Handwritten Chinese RNN|http://arxiv.org/abs/1606.06539v1]] # [[CTPN|https://github.com/tianzhi0549/CTPN]] ## Best line detection !! RNN Approach # [[Sequence Recognition Network]] # [[Seq2Seq|http://arxiv.org/abs/1607.06125]] # Learning Spatial-Semantic Context with Fully Convolutional Recurrent Network for Online handwritten Chinese Text Recognition: residual multilayerd BLSTM ! 自然图片文字检测预研 !! 数据集 传统英文文字识别公开数据集太小，不足以支持CNN模型训练。现常用的训练数据集为基于VGG组提出的合成算法生成的Synth90K。共九万张数据。一次类推，我们要解决的问题如果变化稍少、图片文字数量较多，根据中文单字结构复杂进行折中，预期需要二十万张高质量合成数据样本。 结合类似方法的数据集和我们的需求，数据集不会限制。 !! 加强问题 自然场景文字识别尚不存在完美的解决方案。尤其传统依赖MSER的方法对背景的限制很大。相比之下，基于CNN回归模型的目标检测的稳定性好很多。我们将数据限定为广告中印刷体合成文字。则根据难易程度可以依次将数据分为以下三类： # 如果约束背景简单、文字排列整齐、失真变化不大，基于传统分割方法可以达到100字整体正确率（即包含百字的图像没有单字漏判）85%以上。开发需要预期5人天的时间。不过可能适用性不强。 # 对于更加复杂的问题，复杂背景合成文字，基于目前的目标检测方法，预期至少可以达到90%的准确率和80%的召回率。我们需要做的是增加样本样式等比较基础的训练工作。 # 去掉图片文字朝向的约束，对于段落文字不应产生较大的影响。但现在开发过程遇到了问题，预期的工作量未知。 [[OCR with BiLSTM]]
! Applications * [[CNN for Video Classification]] * [[Video Object Segmentation]]
! Bib * [[Asynchronous Temporal Fields for Action Recognition|https://arxiv.org/abs/1612.06371]] ** fully-connected temporal CRF model for reasoning * Audio in video ** [[CNN Architectures for Large-Scale Audio Classification|http://arxiv.org/abs/1609.09430]] ! Video Classification Hand-crafted local space-time features like 3D-SIFT, ESURF, HOG3D, MBH, local trinary pattern have reasonable performance on simple datasets such as recognizing human actions, etc. They do not require algorithms to detect human body and are robust to background variations. [[Improved trajectories|https://hal.inria.fr/hal-00873267v2/document]] is the state-of-the-art low level trajectory extraction method. The main idea is to densely sample feature points in each frame, and track them in the video based on optical flow. Improved trajectories boost the recognition performance by taking camera motion into account, using SURF to complement optical flow matching. Then use RANSAC to estimate homography matrix. This technique achieves 91.2% on UCF-50, 87.9% on UCF-101 and 66.8% on HMDB-51, which is unsurpassed by many deep methods. These local features lack semantics and discriminative capacity. !! Datasets & Challenges * ''MED'14'': 20 events, 8k videos for training and 23k as dry-run validation (1.2k hr in total), 200k videos in test. * ''UCF-101 & THUMOS-2014'': 13,320 video clips with 101 annotated classes, THUMOS adds 5k+ videos. * ''Sports-1M'': 1m videos in 487 classes, annotation generated automaticly from YouTube, released by Stanford U. * ''HMDB-15'': 6,766 videos in 51 classes, more challenging because the context is complicated. * ''Columbia Consumer Videos'': 9,317 videos in 20 classes. !! Image-Based [[Zha et al. BMVC 2015|https://arxiv.org/abs/1503.04144]] gets mAP 38.74% on MED'14: * AlexNet and VGG19 as base model but features from hidden7 yield best performance * uses Spatio Temporal Pooling to extract features from 8 different regions * classification with kernel SVM * fusion with IDT Fisher Vectors improve results * claims uniform sampling yields same performance as keyframe detection. [[Xu et al. CVPR 2015|https://arxiv.org/abs/1411.4006]] uses VLAD encoding on VGG19 feature with SPP and gets 36.8% on MED'14. !! End-to-End CNN Architectures Due to the limited ability of capturing spatial-temporal patterns, using video directly as input has been considered. The most straight forward attempt is [[Ji et al. ICML 2010|http://www.cs.odu.edu/~sji/papers/pdf/Ji_ICML10.pdf]], where continous frames are stacked and processed with a 3-dim matrix as conv kernel. [[Karpathy et al. CVPR 2014|http://cs.stanford.edu/people/karpathy/deepvideo/]] experimented with different fusion and transfer learning schemes. Instead of stacking the frames all at once, they introduce a fusion technique called ''Slow Fusion'' to aggregate temperal information stepwisely. Highest accuracy on UCF-101 model is 65.4%, achieved by fine-tuning the top 3 layers of Sports-1M pretrained model. [img[slow_fusion.PNG]] [[Tran et al. ICCV 2015|https://arxiv.org/abs/1412.0767v4]] argues that previous models loss temporal information because they perform CONVs and POOLs only spatially. Slow Fusion can handle temporal information better because temporal information are grouped gradually. They propose C3D architecture that uses full frames as input, with 3x3x3 CONVs and 2x2x2 POOLs and achieve 72.26% on UCF101. ''Remark'': it is not CNN's strength to capture temporal information, spatial and temporal pooling works differently in human perception. And the 3D conv architecture is very memory and time consuming. [[Sun et al. ICCV 2015|https://arxiv.org/abs/1510.00562]] factorized 3D CONV into spatio and temporal operations and propose F,,ST,,CN: [img[FSTCN.PNG]] The score of video clips are fusioned with ''Sparsity Concentration Index'', which puts more weights on the score vectors that have higher degrees of sparsity. Predicts 88.1% on UCF-101 and 59.1% on HMDB-51. [[Simonyan & Zisserman, NIPS 2014|https://arxiv.org/abs/1406.2199]] propose a two-stream approach, static frames and optical flow stack are convolved separately: [img[2-stream cnn.PNG]] Authors argue information which hand-crafted local descriptors care about, i.e., histograms of orientations, kinematic features (divergence, curl and shear) and trajactory feature can all be captured by CONV. The scores of the two stream are fused with linear SVM. 88.0% on UCF-101 and 59.4% on HMDB-51. ''Remark'': Still no advantage over IDT. Optical flow probably can be replaced with deep methods. [[Wang et al. CVPR 2015|http://arxiv.org/abs/1505.04868]] propose Trajectory-Pooled Deep-Convolutional Descriptors. Replacing optical flows with improved trajectories as input of temporal stream. The so-called TDD is extracted by trajectory pooling trajectories and CONV feature maps. 91.5% on UCF-101 and 65.9% on HMDB-51 with early fusion of TDDs and iDT. [[Feichtenhofer et al. CVPR 2016|https://www.robots.ox.ac.uk/~vgg/publications/2016/Feichtenhofer16/feichtenhofer16.pdf]] use 3D Conv + 3D pooling to fuse spatial and temporal outputs and combine it with temporal outputs. Base model VGG16, 93.5% on UCF101 and 69.2% on HMDB51. [[Wang et al. ECCV 2016|https://arxiv.org/abs/1608.00859]] adds RGB difference and warped optical flow to input. Base model Inception-BN, 94.2% on UCF-101 and 69.4% on HMDB51. [[Wang et al. CVPR 2016|https://arxiv.org/abs/1512.00795]] learn feature representation by modeling an action as a transformation between an initial state (condition) to a new state (effect) with two Siamese CNNs: [img[Siamese.PNG]] 63.4% on HMDB51 and 92.4 on UCF101. The advantage is the action type is learned. [[Bilen et al. CVPR 2016|http://www.robots.ox.ac.uk/~vgg/publications/2016/Bilen16a/]] introduced the dynamic image to represent motions with rank pooling in videos, upon which a CNN model is trained for recognition. 65.2% on HMDB51 and 89.1% on UCF101. ''Remark'': the dynamic image is hard for human to recognize. !! Modeling Long-Term Temporal Dynamics [[Donahue et al. CVPR 2015|http://arxiv.org/abs/1411.4389]] trained a 2-layer LSTM with features from the two-stream approach. !! Incorporating Visual Attention # "Delving Deeper into Convolutional Networks for Learning Video Representations", Nicolas Ballas, Li Yao, Pal Chris, Aaron Courville, ICLR 2016. [[link|http://arxiv.org/pdf/1511.06432v4.pdf]] # "Deep Multi Scale Video Prediction Beyond Mean Square Error", Michael Mathieu, camille couprie, Yann Lecun, ICLR 2016. [[link|http://arxiv.org/pdf/1511.05440v6.pdf]] # Hierarchical Attention Network for Action Recognition in Videos (HAN) [[link|http://arxiv.org/abs/1607.06416]] ! Object Tracking # Online Tracking by Learning Discriminative Saliency Map with Convolutional Neural Network, Seunghoon Hong, Tackgeun You, Suha Kwak, Bohyung Han, arXiv:1502.06796. [[link|http://arxiv.org/pdf/1502.06796]] # DeepTrack: Learning Discriminative Feature Representations by Convolutional Neural Networks for Visual Tracking, Hanxi Li, Yi Li and Fatih Porikli, BMVC, 2014. [[link|http://www.bmva.org/bmvc/2014/files/paper028.pdf]] # Learning a Deep Compact Image Representation for Visual Tracking, N Wang, DY Yeung, NIPS, 2013. [[link|http://winsty.net/papers/dlt.pdf]] # Hierarchical Convolutional Features for Visual Tracking, Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, ICCV 2015 [[link|http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Ma_Hierarchical_Convolutional_Features_ICCV_2015_paper.pdf]] [[Code|https://github.com/jbhuang0604/CF2]] # Visual Tracking with fully Convolutional Networks, Lijun Wang, Wanli Ouyang, Xiaogang Wang, and Huchuan Lu, ICCV 2015 [[link|http://202.118.75.4/lu/Paper/ICCV2015/iccv15_lijun.pdf]] [[Code|https://github.com/scott89/FCNT]] # Learning Multi-Domain Convolutional Neural Networks for Visual Tracking, Hyeonseob Namand Bohyung Han, [[link|http://arxiv.org/pdf/1510.07945.pdf]] [[Code|https://github.com/HyeonseobNam/MDNet]] [[Project Page|http://cvlab.postech.ac.kr/research/mdnet/]] ! [[Video Captioning]]
[[blog|http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/]]
! Tweaks
* Learn embeddings from scratch, no pre-trained word2vec, and no static channel.
* L2 norm constraints left out
! Implementation
!! Inputs
```python
# Placeholders for input, output and dropout
self.input_x = tf.placeholder(tf.int32, [None, sequence_length], name="input_x")
self.input_y = tf.placeholder(tf.float32, [None, num_classes], name="input_y")
self.dropout_keep_prob = tf.placeholder(tf.float32, name="dropout_keep_prob")
```
* `sequence_length` - truncanated to 59.
!! Embedding layer
has input size `[vocabulary_size, embedding_size]`. To utilize `conv2d`, we have to add 1 dim of pure 1s to the embedding output.
```python
with tf.device('/cpu:0'), tf.name_scope("embedding"):
W = tf.Variable(
tf.random_uniform([vocab_size, embedding_size], -1.0, 1.0),
name="W")
self.embedded_chars = tf.nn.embedding_lookup(W, self.input_x)
self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)
```
`W` is learned during training.
This method is based on hard clustering and discrete mapping.
The converted feature vector $\hat y_t$ at frame $t$ is determined by quantizing the source feature vector $x_t$ to the nearest centroid vector of the source codebook and substituting it with a corresponding centroid vector $c_m^{(y)}$ of the mapping codebook. The large quantization error due to hard clustering is effectively reduced by adopting ''fuzzy vector quantization'' (VQ) that realizes ''soft clustering''. Continuous weights $w_{m,t}^{(x)}$ for individual clusters are determined at each frame according to the source feature vector.
$$
\hat y_t = \sum_{m=1}^Mw_{m,t}^{(x)}c_M^{(y)},
$$
where $M$ is the number of centroid vectors. Another way is
$$
\hat y_t = x_t + \sum_{m=1}^Mw_{m,t}^{(x)}(c_M^{(y)}-c_M^{(x)}).
$$
! TODO * Autocomplete * Vim bindings * Switch to markdown?
[[link|http://www.jarrodctaylor.com/posts/Compojure-Address-Book-Part-1/]] Tools involved: * Compojure * Ring * Midje: testing * hiccup: render html To run the project `lein ring server` Test/auto test `lein midje (:auto)` Start postgres: `systemctl start postgresql`
[[A Survey of Deep Learning Techniques Applied to Trading|https://www.linkedin.com/pulse/survey-deep-learning-techniques-applied-trading-james-melenkevitz-phd]] * [[Daily News for Stock Market Prediction]] * [[Daily Stock Forecast]] [[Portfolio Theory]]
! Backpropagating the Diagonal Hessian
Assume each layer has the functional form $o_i = f(y_i) = f(\sum_jw_{ij}x_j)$. The iteration is obtained using the Gaussian-Newton approximation (dropping the term that contain $d^2f(y)$). The cost of computing the diagonal second derivatives is essentially the same as the regular backpropagation. This technique is applied in the ''optimal brain damage'' pruning procedure.
!! Diagonal Bayesian Penalty
Chp4 of [[Neural Networks: Tricks of the Trade]]. The hyperparameters have to be updated to make sure the augmented Hessian is p.d.
The ''evidence for a network'' evaluated with cross-entropy error is as follows:
$$
\ln p(D|\alpha) = -\frac{1}{2}\sum_{i=1}^n\alpha_{[i]}w_i^2-E-\frac{1}{2}|H|+\sum_c\frac{n_c}{2}\ln\alpha_c-\frac N 2\ln(2\Pi)
$$
This algorithm is very sensitive to small eigenvalues of Hessian (which can be omitted). So I don't know what is going on here.
! Computing the Product of the Hessian and a Vector
The finite difference method:
$$
Hv\sim\frac{1}{\alpha}\left(\frac{\partial E}{\partial w}(w+\alpha v)-\frac{\partial E}{\partial w}(w)\right)
$$
! [[Hessian-Free Optimization]]
[[MCMC assisted by Belief Propagaion|https://arxiv.org/abs/1605.09042]] ! [[MCMC]] * pros: exact * cons: suffer from slow mixing time ! Belief Propagation Deterministic message-passing algorithm * pros: empirically fast, efficient * cons: lack of control over approximation quality
[[link|https://arxiv.org/abs/1603.05201]]
''Hypothesis'': the lower convolution layers learn redundant filters to extract both positive and negative phase information of an input signal.
''Fact'': The first few conv layers of a deep CNN manage to capture both negative and positive phase information through learning pairs or groups of negatively correlated filters. So there exists a redundancy among the filters from the lower conv layers.
''pairing filter'': $\bar\phi_i = \text{argmin}_{\phi_j}\langle\phi_i, \phi_j\rangle$ where $\phi_j\in\Phi$
; CReLU denoted by $\rho_c:\mathbb R\rightarrow \mathbb R^2$
: $\forall x\in \mathbb R, \rho_c(x) \triangleq ([x]_+, [-x]_+)$
Conv layers with CReLU are easier to reconstruct inputs.
* speech recognition * caption generation * [[Question Answering]] * [[Dialog]] * [[Neural Machine Translation]] * following instructions * summarisation: how to prepare parallel dataset ! Algorithmic challenges Maximum posterior is intractable. We therefore approximate it with: * a greedy search, generating words one by one. * a beam search, keep track of top b hypothesis, but not leading us very far * or with MC, not very common Improving search/inference is an open research question: * Search more effectively * Guaranteed bounds * Limit the model to make search easier ! [[Attention Mechanism]] ! Evaluation * Cross-entropy, perplexity: okay to implement, hard to interpret * Task-specific evaluation: BLEU, METEOR, WER, ROUGE, easy to implement, okay to interpret * Can we invent a code specific metric which emphasis on syntax and logic correctness
* [[ICML 2016]] * [[ICLR 2016]] * [[KDD 2016]] !! CVPR 2016 [[Learning Deep Features for Discriminative Localization]] !! ECCV 2016 [[Fully-Convolutional Siamese Networks for Object Tracking]] ! Misc * [[IFIP IIP]] ! [[Videos & Talk Notes|Talks]]
! Spacemacs
```lisp
(defun dotspacemacs/user-config ()
"Configuration function for user code.
This function is called at the very end of Spacemacs initialization after
layers configuration. You are free to put any user code."
;; Clojure
;; Pretty symbols for anonymous functions, set literals and partial functions
(setq clojure-enable-fancify-symbols t)
;; Use evil-paredit mode in clojure, still got some problems
;; (add-hook 'emacs-lisp-mode-hook 'evil-paredit-mode)
;; huaihai's configuration
;; Make evil-mode up/down operate in screen lines instead of logical lines
(define-key evil-motion-state-map "j" 'evil-next-visual-line)
(define-key evil-motion-state-map "k" 'evil-previous-visual-line)
;; Also in visual mode
(define-key evil-visual-state-map "j" 'evil-next-visual-line)
(define-key evil-visual-state-map "k" 'evil-previous-visual-line)
;; clojure mode config
;(require 'clojure-mode-extra-font-locking)
(add-hook 'clojure-mode-hook #'smartparens-strict-mode)
(add-hook 'clojure-mode-hook #'evil-smartparens-mode)
(add-hook 'clojure-mode-hook #'rainbow-delimiters-mode)
;; remove trailing whitespace when saving
(add-hook 'before-save-hook 'delete-trailing-whitespace)
;; toggle comments
(define-key evil-normal-state-map ",c " " cl")
;; match paredit.vim key-binding
(define-key evil-normal-state-map ",W" " kw") ; wrap with ()
(define-key evil-normal-state-map ",w[" ; wrap with []
(lambda (&optional arg) (interactive "P") (sp-wrap-with-pair "[")))
(define-key evil-normal-state-map ",w{" ; wrap with {}
(lambda (&optional arg) (interactive "P") (sp-wrap-with-pair "{")))
(define-key evil-normal-state-map ",S" " kW") ; splice, i.e unwrap an sexp
(define-key evil-normal-state-map ",J" " kJ") ; join two sexps
(define-key evil-normal-state-map ",O" 'sp-split-sexp) ; split an sexp
(define-key evil-normal-state-map ",I" " kr") ; raise current symbol
(define-key evil-normal-state-map (kbd ", <up>") " kE") ; splice kill backward
(define-key evil-normal-state-map (kbd ", <down>") " ke") ; forward
;; These are different from vim, here cursor should NOT be on delimits
(define-key evil-normal-state-map ",>" " ks") ; forward slurp
(define-key evil-normal-state-map ",<" " kS") ; backward slurp
;; jr0cket: keybindings for cycling buffers
(global-set-key [C-prior] 'previous-buffer)
(global-set-key [C-next] 'next-buffer)
;; Remap undo to overwrite the Emacs GUI window hide key
;; as the backslash character is used to escape in Emacs, we need to escape it with another backslash
(define-key global-map (kbd "C-\\") 'undo-tree-undo)
(define-key global-map (kbd "M-\\") 'undo-tree-redo)
)
;; Do not write anything past this comment. This is where Emacs will
;; auto-generate custom variable definitions.
```
!! The Idea
If the posterior distribution are in the same family as the prior probability distribution, the prior and posterior are then called ''conjugate distributions'', and the prior is called a conjugate prior for the likelihood funtion. The likelihood function is usually well-determined from a statement of the data-generating process. Different choices of the prior distribution may make teh integral more or less difficult to calculate, and the posterior may take one algebraic form or another. For certain choices of the prior, the posterior has the same algebraic for as the prior. Such a choice is a conjugate prior.
For example, the Gaussian family is conjugate to itself w.r.t. a Gaussian likelihood function. If the likelihood function is Gaussian, choosing a Gaussian prior over the mean will ensure that the posterior distribution is also Gaussian.
Another widely used example is [[Dirichlet-multinomial distribution]].
!! How to choose
Experimenters are often in the position of having had collected some data from which they want to make inferences about the process that produced those data. Bayes theorem,
$$
g(\theta|x_1, \dots, x_n)=\frac{\pi(\theta)L(\theta|x_1, \dots, x_n)}{\int \pi(\theta)L(\theta|x_1, \dots, x_n)d\theta}
$$
is can be used. However the integral is always not tractable.
We consider a likelihood funtion which can be factored as:
$$
L(\theta|x_1, \dots, x_n) = u(x_1,\dots,x_n)v(T(x_1, \dots, x_n), \theta),
$$
where the second term is known as the kernel function and $T(\cdot)$ is the sufficient statistics. The conjugate prior is defined propotional to the kernel function,
$$
g(\theta)\propto v(T(x_1, \dots, x_n), \theta).
$$
[[Exponential Family]] all have conjugate prior.
Baidu just released [[their code|https://github.com/baidu-research/warp-ctc]].
! Experiment !! Merging CTPN to ms * skipping LSTM for legacy issue. * add reverse and transpose !! RFCN-LSTM Base net list of stride 2 layers * conv1 * pool1 * res3a * res4a
! Criteria !! BIC [[BIC Changepoint]] !! A Heuristic Derivation of BIC !! AIC [[Akaike's information criterion]] Each of these simple criteria involves choosing the model with the best penalized log-likelihood (i.e., the highest value of $l−A_np$ where $l$ is the log-likelihood, $A_n$ is some constant or some function of the sample size $n$, and $p$ is the number of parameters in the model). For historical reasons, instead of finding the highest value of $l$ minus a penalty, this is sometimes expressed as finding the lowest value of $−2l$ plus a penalty, i.e., $$ -2l+A_np. $$ For AIC, $A_n = 2$ and for BIC, $A_n = \ln(n)$. ! Comparing In some simple cases, the comparison of two models using information criteria can be viewed as equivalent to a likelihood ratio test, with different models representing different [[alpha levels|Alpha level]] (i.e., different emphases on sensitivity or specificity; Lin & Dayton 1997). AIC or BIC could be preferable, depending on sample size and on the relative importance one assigns to sensitivity versus specificity. An alternative view the lack of consistent selection in AIC is that it attempts to find the model with good performance in some predictive sense. If $n$ is small, then we may have to choose a smaller model to get more reliable coeffecient estimates. However, if $n$ is large, then standard errors will be small and one can afford to use a rich model. Thus from an AIC perspective and for large $n$, Type II error (an underfit model) is considered worse than a Type I error (overfit model). In contrast, with BIC we are willing to take a higher risk of choosing too small a model, to improve our probability of choosing the true model. BIC considers Type I and Type II errors to be about equally undesirable while AIC considers Type II errors to be worse unless $n$ is very small. !! KL based comparison Both selection criteria can be derived, and applied, without assuming the true model is in the model set. However, the defining characteristic of BIC (i.e., what is it trying to do) is only evident asymptotically in relation to the concept of a ''quasi-true model''. In contrast AIC seeks to select only a best model at a given sample size; “best” is in relation to an expected estimated K-L criterion which serves to recognize a bias-variance trade-off in model selection. For AIC, “best” varies with $n$.For BIC, its “best,” i.e., the quasi-true model, does not depend on $n$. However, on average the BIC-selected model approaches its target “best” model from below in terms of the model ordering imposed here by the $I(f,g_r)$. How researchers assess AIC and BIC performance depends on the performance criteria they adopt (true model or best model), the assumptions they make (usually only implicitly, as in simulation studies) about the underlying K-L values, $I(f,g_r)$, $r=1,\dots,R$, and the sample sizes considered. Failure to properly recognize all of these factors and issues has led to much confusion in the model selection literature about AIC versus BIC. !! Discussion Most of the simulations shown here illustrated similar principles. AIC and similar criteria often risk choosing too large a model, while BIC and similar criteria often risk choosing too small a model. For small n, the most likely error is underfitting, so the criteria with lower underfitting rates, such as AIC, often seem better. For larger $n$, the most likely error is overfitting, so more parsimonious criteria, such as BIC, often seem better. Unfortunately, the point at which the $n$ becomes “large” depends on numerous aspects of the situation. In simulations, the relative performance of the ICs at a given $n$ depended on the nature of the “true model” (the distribution from which the data came). This finding is unhelpful for real data, where the truth is unknown. It may be more helpful to think about which aspects of performance (e.g. sensitivity or specificity) are most important in a given situation. Underfitting and overfitting could be defined as underestimating and overestimating the true number of classes or factors. For observed data for which models are only approximations to reality, more care is required in considering what it means for a model to be too small, correct, or too large (Burnham & Anderson, 2002, p. 32) Performance can be expressed in terms of a quantitative criterion such as MSE, avoiding the use of a “correct” size, but this may favor AIC-like over BIC-like criteria. There is no obvious conclusion about whether or when to use ICs, instead of some other approach. Kadane and Lazar (2004) suggested that ICs might be used to “deselect” very poor models (p. 279), leaving a few good ones for further study, rather than indicating a single best model. One could use the ICs to suggest a range of model sizes to consider; for example, one could use the BIC-preferred model as a minimum size and the AIC-preferred model as a maximum, and make further choices based on other kinds of fit criteria, on theory, or on subjective inspection of the results (Collins & Lanza 2010). If BIC indicates that a model is too small, it may well be too small (or else fit poorly for some other reason). If AIC indicates that a model is too large, it may well be too large for the data to warrant. Beyond this, theory and judgment are needed.
Here we use Caffenet (Alexnet) as an example ``` [INPUT-[CONV-RELU-POOL-NORM]*2-[CONV-RELU]*3-POOL-[FC-RELU-DROP]*2-FC-LOSS] ``` In more detail, [[and even more|ConvNet layer details]]: * INPUT [3x227x227] holds image of size [227x227] with 3 color channels (RGB). * CONV layer will compute the output of neurons that are connected to local regions in the input, each computing a dot product between their weights and the region they are connected to in the input volume. The first results in a [96x55x55] by a stride of 4, but later don't change the blob height and width, only depth (#filters). * RELU layer will apply an elementwise activation function, such as the $\max(0,x)$ thresholding at zero. This leaves the size of the volume unchanged. * POOL layer will perform a downsampling operation along the spatial dimensions (width, height). Each will downsample by 2 in height and width. * NORM layer is used to normalize the channel response. * FC (i.e. fully-connected) layer will compute the class scores, resulting in volume of size [1x1xC], where each of the C numbers correspond to a class score, such as among the 1000 categories of ImageNet. As with ordinary Neural Networks and as the name implies, each neuron in this layer will be connected to all the numbers in the previous volume.
!! [[Convolutional Layer]]
!! [[Pooling|Pooling Layer]]
!! [[Nonlinearity Layer]]
!! [[Normalization Layer]]
!! Local Response Normalization(LRN)
The local response normalization layer performs a kind of ''lateral inhibition'' by normalizing over local input regions. In `ACROSS_CHANNELS` mode, the local regions extend across nearby channels, but have no spatial extent (i.e., they have shape `local_size x 1 x 1`). In `WITHIN_CHANNEL mode`, the local regions extend spatially, but are in separate channels (i.e., they have shape `1 x local_size x local_size`). Each input value is divided by $(1+(\alpha/n)\sum_ix^2_i)^\beta$, where $n$ is the size of each local region, and the sum is taken over the region centered at that value (zero padding is added where necessary).
!! Fully-connected
This is what is used in traditional neural networks. It gives the network more flexibility to convert a FC to CONV, allowing us to 'slide' the original ConvNet across a larger image.
!! [[Dropout|Dropout Layer]]
!! Loss
We can use different loss functions for different tasks.
''Sigmoid cross-entropy loss'' is used for predicting K independent probability values in [0,1]. In logistic regression, our hypothesis took the form:
$$
h_\theta(z) = \frac{1}{1+\exp(-\theta\top z)},
$$
and the model parameters $\theta$ were trained to minimize the cost function:
$$
L(\theta) = -\left(\sum_{i=1}^my_i\ln\log h_\theta(z_i)+(1-y_i)\log(1-h_\theta(z_i))\right)
$$
Softmax loss is used for predicting a single class of $K$ mutually exclusive classes. Our hypothesis takes the form:
$$
h(\mathbf z, j) = \frac{e^{z_j}}{\sum_{k=1}^Ke^{z_k}},\qquad j = 1, \dots, K,
$$
and the loss function will be:
$$
J(z) = -\left(\sum_{i=1}^m\sum_{k=1}^K1_{y_i = k}\log h(z, k)\right).
$$
which is a generalized case of logistic regression loss. (When $K=2$, the softmax reduces to logistic regression).
Since the function maps a vector and a specific index i to a real value, the derivative needs to take the index into account. For the cross entropy cost, we have:
$$
\frac{\partial L}{\partial z_k} = -\sum_{i=1}^m(1_{y_i=k}-h(\mathbf z, k))
$$
Euclidean loss is used for regressing to real-valued labels [-inf,inf]
1d discrete convolution generally:
$$
(f * g)[n] = \sum_{m=-M}^M f[n-m]g[m]
$$
Convolution is great to extract features from images.
! RNN RNN is hard to train due to vanishing and exploding gradients. ! Moving average Moving average version is [[Quasi-RNN]] ! Autoregressive [[WaveNet]] and [[Pixel-CNN]] are examples of autoregressive models
[img height=250 [http://cs231n.github.io/assets/cnn/depthcol.jpeg]][img height=250 [http://cs231n.github.io/assets/nn1/neuron_model.jpeg]]
In Caffenet, the 96 neurons of the first conv layer has a reception field of $3\times3$. Since input volume is cropped beforehead, no padding is needed:
```css
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
/* learning rate and decay multipliers for the filters */
param { lr_mult: 1 decay_mult: 1 }
/* learning rate and decay multipliers for the biases */
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 96 /* learn 96 filters */
kernel_size: 11 /* each filter is 11x11 */
stride: 4 /* step 4 pixels between each filter application */
weight_filler {
type: "gaussian" /* initialize the filters from a Gaussian */
std: 0.01 /* distribution with stdev 0.01 (default mean: 0) */
}
bias_filler {
type: "constant" /* initialize the biases to zero (0) */
value: 0
}
}
}
```
This layer learns the Gabor filters which looks like this
[img height=250 [http://cs231n.github.io/assets/cnn/weights.jpeg]]
There are many convolution kernels in each layer, and each kernel is replicated over the entire image with the same parameters. The function of the convolution operators is to extract different features of the input. The capacity of a neural net varies, depending on the number of layers. The first convolution layers will obtain the low-level features, like edges, lines and corners. The more layers the network has, the higher-level features it will get.
! Backpropagation
The parameters of each convolution kernel are trained by the backpropagation algorithm. The backward pass for a convolution opteration (for both the data and the weights) is also a convolution (but with spatially-flipped filters).
$$
\frac{\partial L}{\partial a(p, q)} = \sum_{u, v}\frac{\partial L}{\partial z(p-u, q-v)}\frac{\partial z(p-u, q-v)}{\partial a(p, q)}=\frac{\partial L}{\partial z(u, v)}*\text{flip}(w)
$$
$$
\frac{\partial L}{\partial w(u, v)} = \sum_{p, q}\frac{\partial L}{\partial z(p, q)}\frac{\partial z(p, q)}{\partial w(u, v)}=a(u, v)*\frac{\partial L}{\partial z}
$$
Caffe implements convolution as matrix multiplication. We reorganize the input into $\mathbf A$ so that each column is the patch to be convoluted ($11\times11\times3$) in the previous case, and the total number of columns is the output size of each filter ($55\times55$). So the objective function becomes:
$$
L = f(\mathbf Z) = f(\mathbf W\mathbf A)
$$
by applying matrix derivation:
$$
\frac{\partial L}{\partial \mathbf W} = \frac{\partial L}{\partial \mathbf Z}\mathbf A^\top
$$
! Generalization
* [[Graph Convolution]]
* [[Dilated Convolution]]
* [[Deformable Convolution]]
* [[Deconvolution]]
Many of the materials borrowed form [[this page|http://cs231n.github.io/convolutional-networks/]], a very good place to start. Convolutional Networks are a specific class of neural networks which obtain invariant signal representations by cascading trainable filter banks with non-linearities and subsampling and pooling operators. They have been successfully applied to a variety of image and audio recognition tasks. Each layer of the network is connected to the previous one by establishing "connections" or filters, whose output is processed with a non-linearity such as sigmoids or rectifications. The spatial localization is progressively lost by successively "pooling", or subsampling, the resulting feature maps with local averages or general $L^p$ norms. Convolutional network architectures were originally learnt over a collection of labeled examples, using a backpropagation algorithm, which optimizes a classification error loss function using a gradient descent across the network. As opposed to general neural networks, convolutional networks incoporate a translation invariance prior, which greatly reduces the number of parameters to learn and hence its efficiency in a number of recognition tasks. It can be trained with unsupervised data with [[sparse autoencoders|Sparse Autoencoder]]. ! [[Convolution]] !! [[Architecture|ConvNet architecture]] * [[ConvNet Layers]] * [[Design|Designing a ConvNet]] * [[Visualization|Visualizing a ConvNet]] !! [[Case study|Case study of ConvNets]] ! Practices !! Data augmentation * ''Color'': multiply the projection of each patch pixel onto the principle components of the set of all pixels by a factor between 0.5 and 2 * ''Contrast'': raise saturation and value (S and V components of the HSV color representation) of all pixels to a power between 0.25 and 4. !!! Utilizing pretrained models Empirical results proved the efficiency of adopting a large pretrained network and training with new data for a new smaller task. Related ideas are these: * ''Fine-tuning''. In [[Flickr Style|https://github.com/BVLC/caffe/tree/master/models/finetune_flickr_style]] setting of finetuning caffenet, the last FC is replaced with a new one and is trained with a 10× larger learning rate, and the general learning rate starts smaller. Other settings are left unchanged. * ''Transfer learning''. In this setting, the first several layers of the network is kept unchanged while the later ones are retrained from scratch. It has been shown that transfer learning (even using the same data for the same task) can suffer from co-adaptation performance drops and (otherwise) representation specifity limits. While finetuning can deal all these problems smoothly. !! Fighting overfitting * [[Photometric distortions|http://arxiv.org/abs/1312.5402]], e.g. lighting, color etc. * Crops vary in size, aspect ratio (e.g. 140 per image) * Multiple models, 7 vs 1 [[ideas|Thoughts about ConvNets]]
[[link|http://arxiv.org/abs/1408.5882]]: A one-layer CNN approach with simplicity and empirical performance. This has become the standard baseline for new text classification publications. Here is a [[tensorflow implementation|cnn-text-classification]]. ! Structure * Input: pretrained word vectors, such as [[Word2Vec]] or [[GloVe|Global Vectors for Word Representation]]. * Components: 1 layer 1-d Conv, 1-Max pooling, Dropout, l2 regularization, softmax * Loss: categorical cross-entropy loss. ! Datasets [[Sentense Classification Datasets]] ! Empirical Results [[A Sensitivity Analysis of CNN for Sentence Classification]]
! Structure * WeightNorm after each Conv * Skip connection with each Conv * The encoder stack will receive gradients //twice// for each attention. ! Code Lua code is so unintuitive. It brings me headache. * EncoderStack: EncoderConv with skip connection * DecoderConvBlock ! Ideas * More general seq2seq usages * Cache mechanism useful? * Multilanguage
On going courses: * [[Audio|Audio Signal Processing for Music Applications]] * [[Deep Learning Courses]] * [[NLP Courses]]
[[Restricted Boltzmann Machines]]
! Algorithms
!! Convolutional deep belief networks
The difference with regular RBMs is that the weights between $H$ and $V$ are shared among all locations in the hidden layer.
[[Probabilistic max-pooling]]
!! Solving CRBM
Since all units in one layer are conditionally independent given the other layer, inference can be performed using block Gibbs sampling.
! Feature learning
First convert time-domain signals into spectrograms (160 channels). Apply [[PCA whitening|Whitening transformation]] to create lower dimensional representations. Feed in $n_c$ channels of one-dimensional vectors of length $n_V$, where $n_c$ is #PCA components.
For TIMIT, spectrogram had a 20ms window size with 10ms overlaps. PCA whitening reduces the dimension to 80. 300 first-layer bases with a ''filter length'' ($n_W$) of 6 and a max-pooling ratio (local neighborhood size) of 3 were trained.
! Code
error and sparsity_hid are criteria in batches.
!! `train_tirbm_audio_v1.m`
```matlab
1 function train_tirbm_audio_v1(ws, num_bases, spacing, pbias, pbias_lb, pbias_lambda, epsilon, l2reg, epsdecay, nPC, sigmaPC)
```
PCA is done with 1000 sampled data
```matlab
14 [X startframe_list] = concatenate_speech_data(Pall, randsample(length(Pall), min(length(Pall), 1000)));
```
Although it's a samping algorithm, it still needs momentum. (A network basic property?)
```matlab
111 initialmomentum = 0.5; % used for first 5 trials
112finalmomentum = 0.9; % used then
```
Contrastive Divergence?
```matlab
188 function [ferr dW_total dh_total dv_total poshidprobs poshidstates negdata stat] = ...
fobj_tirbm_CD_LB_sparse_audio(imdata, W, hbias_vec, vbias_vec, pars, CD_mode, bias_mode, spacing, l2reg)
```
! Automatic evaluation of software for deficiencies * buffer overflow * double-free * use-after-free ! Black Hat
! Bibs * [[Detecting and Explaining Causes From Text For a Time Series Event|https://arxiv.org/abs/1707.08852]] ! Introduction Data and task are posted [[here|https://www.kaggle.com/aaron7sun/stocknews]]. I am curious about how to take a whole sentence as an entity in a higher hierarchy, i.e., we have to find a representation for headlines to be the input of each day's stock prediction. Following is my take on this task: # Run TF-IDF+SVM baseline: running [[the code|https://www.kaggle.com/hsrobo/d/aaron7sun/stocknews/tf-idf-svm-baseline]] yields ROC-AUC .563. Actually better than bare guesses. # Replace TF-IDF with pretrained word2vec on Google News # Replace SVM with LSTM # Compare with a sequence prediction LSTM with no news input # If all of above works, things should get interesting # Use CNN with fixed word embedding for sentence feature extraction # Fine-tune the word embedding (deeply doubt if this works, too few words in dataset, should generalize poorly) # There's very little chance this small dataset can support a dilated convolution layer. That is what I am up to in the very end. ! Tasks * [[reddit headlines|https://www.kaggle.com/hsrobo/d/aaron7sun/stocknews/tf-idf-svm-baseline]]: data probabily not that relavant * [[SemEval-2017|http://alt.qcri.org/semeval2017/task5/]] ** [[Team UWaterloo report|https://arxiv.org/abs/1707.09448]] ! Results !! Simple tweaks Added called nltk tokenizer to preprocess the data. ROC-AUC dropped to .527. Too much new words. Should try fasttext, got memory error loading the word embeddings. !! Convnet moves ahead As in the baseline approach, we can join the 25 headlines, we can carry on classification with CNN. Here are two approaches in Tensorflow: * [[WildML one|http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/]] * [[The new one on Github with pretrained word embedding|https://github.com/mangate/ConvNetSent]] ! TODOs # run baseline on semeval # n-gram as embedding? # shallow feedforward or convnet
! Data RCquant ! Code * [[boosting sample|https://uqer.io/community/share/57163aa8228e5b82797f5561]] * [[Recurrent weighted average|https://github.com/jostmey/rwa]] * [[RCGAN|https://github.com/ratschlab/RGAN]] ! Research Directions * Statistics ** Good old quant algorithms ** Time Series *** Anomaly detection *** Non-stationary stochastic processes * Deep Learning ** Sequential Models *** Recurrent neural nets *** Autoregressive models: e.g. WaveNet ** Temporal Convolution: hierarchical feature extraction *** Dilated convolution *** ConvS2S ** Attention Mechanism: more clever way of associating history information *** Memory networks: key-value, end-to-end, incorporate various form of knowledge *** Differentiable memory: complex structured deduction ** [[Bayesian Deep Learning]]: measure the uncertainty ** Reinforcement Learning *** Trading agent *** Learning to learn * Natural Language Processing ** News to vector (Find informative news. I made this term up.) ** Question answering: reading comprehension and dialog agent to assist documentation understanding and archiving * Computing ** Processor level performace optimization: efficient computation of CONV, GEMM, etc. *** GPU *** FPGA ** Model Compression *** Quantization: ternary weight nets *** Sparsify: Trimming and vector level sparsity ** Parallel Computing *** Networking: e.g. infiniband *** Efficient training: async SGD, MPI and other related topics *** Serving: RPC, docker containers, etc. ! Bibs * [[Stochastic Portfolio Theory]] * [[Deep learning bank distress from news and numerical financial data|https://arxiv.org/abs/1706.09627]] * Learning to Generate Market Comments from Stock Prices: an ordinary seq2seq pointer network
[[Link|https://www.youtube.com/watch?v=ItDroviU_Vs&list=PL-tWvTpyd1VB928jOYmlK7S_A8UUsCJLW]]
! Classifier-Based Two-Sample Tests
Define distance to be the ''Integral Probability Metrics'': $\rho_{\mathcal F}(P, Q) = \sup_{f\in\mathcal F}[\mathbb E_{X\sim P}f(X)-\mathbb E_{Y\sim Q}f(Y)] = 2\text{ accuracy}-1$. Where $\mathcal F$ is a set of classifiers.
* If we make $\mathcal F$ be the family of 1-Lipschitz functions， we get [[WGAN]].
* If $\|f\|_\infty\le 1$, its total variance used in EBGAN (almost)
* If we use the mean error of auto-encoders: BEGAN
In this talk, $\|f\|_{\mathcal H}\le1$ from a unit ball, the metric is [[MMD|Maximum Mean Discrepancy]] used in GMMN.
A optimal classifier can be found with gradient descent of MMD. The theory seems to be broken in the optimization part
! GANs, Actor-Critic and Multilevel Optimization
Related paper [[Connecting Generative Adversarial Networks and Actor-Critic Methods|https://arxiv.org/abs/1610.01945]]
GAN is trained as a multilevel optimization:
$$
\phi^* = \arg\min_\phi F(\theta^*, \phi)
$$
where $F$ is a major goal
$$
\theta^*(\phi) = \arg\min_\theta f(\theta, \phi)
$$
and $f$ is a sub goal against it. This is NP-Hard even for bilevel linear programming.
! Generator-aware Discriminators & Discriminator-aware Generators
When the discriminator can look at the generator parameters, the training process becomes much stable. This take into account how discriminator will adjust, like [[Unrolled GAN]]s. The problem is @@color:#859900;how can we build a network whose inputs are the weights of another network?@@ Ideally we want a parameterization invariant representation.
We can use Hamiltonian Variational Inference. Use gradients of generator w.r.t. $z$ to adjust $z$. The idea is recently used in Bayesian RNNs "posterior sharpening".
A straight forward analog to GAN: generate multiple samples, choose one the discriminator likes best.
! Techniques * Brightness, saturation, contrast * [[Color augmentation|https://arxiv.org/abs/1312.5402]] * PCA Jittering: since AlexNet * Resize: Scale / Aspect Ratio / Random crop * Flip * Rotation * Affine translation: random image interpolation * Gaussian noise * Smoothing * Label Shuffle ! Implementations * [[Caffe Augmentation|https://github.com/kevinlin311tw/caffe-augmentation]] * [[FB ResNet Torch|https://github.com/facebook/fb.resnet.torch]] ! In Practice |Trick |ResNet Torch|Hikvision ImageNet | |Brightness, saturation, contrast | | | |[[Color augmentation|https://arxiv.org/abs/1312.5402]] | T | | |PCA Jittering | T | T | |Scale / Aspect Ratio / Random crop| T | T | |Flip| | | |Rotation| | | |Affine translation| | T | |Gaussian noise| | | |Smoothing| | | |Label Shuffle| | T | ! Bibs * [[mixup]]
Hope to relate this with [[An exact mapping between the Variational Renormalization Group and Deep Learning]]
[[Sentense Classification Datasets]] ! Images [[Open Images|https://github.com/openimages/dataset]] ! Deep Scene Understanding [[Places2|http://places2.csail.mit.edu/]] ! Video Classification Youtube-8M ! Audio * [[TIMIT]]
! Previous work * [[Stacked Hourglass Networks for Human Pose Estimation|https://arxiv.org/abs/1603.06937]] * Hypercolumns for Object Segmentation and Fine-grained Localization ** Above 2 use deconv as upsampling * [[Zoom Out-and-In Network with Recursive Training for Object Proposal|https://arxiv.org/abs/1702.05711]] * [[Find Tiny Faces]] ** 2 examples of using above technique in detection ! Hyper ROI Instead of upsampling the features, we use ROI Pooling layers of multiple scales. Should try crop and resize instead of roi, see tf-faster-rcnn code.
Checkerboard problem: No direct relationship exists among adjacent pixels. The up-sampled feature map generated by deconvolution can be considered as the result of periodical shuffling of multiple intermediate feature maps computed from the input feature map by independent convolutions. To overcome this, use Pixel Deconvolutional Networks
! Deep Convolutional Neural Network The overall architecture of a DCNN consists of one or more layers of convolution followed by pooling followed by densely connected hidden layers and a softmax classifier. The initial layers in a DCNN use convolutional layers in place of the standard fully-connected layers. [Baidu 15 Arxiv] use convolution and (max) pooling first layer architecture. The hyperparameters introduced are #convolutional layers, ''input region size'' for convolution and pooling layers and pooling fucntion. ! Deep Local United Neural Network DLUNN differs from a DCNN by using different weights at each location of the first hidden layer. A united neural neural network can be thought of as convolutional neural networks using locally connected computations and without weight-sharing.
! Generative modeling
In real life we always encounter problems where data generator are so complicated that its distribution cannot be evaluated. Probably drawn from super high dimensional space (image/audio). Suppose we have trainig examples $x\sim p_{data}(x)$ and want a model that can draw samples: $x\sim p_{model}(x)$ where $p_{model} = p_{data}$. Of course this is the idealized scenario.
Generally speaking, given high-dimensional data $X$, we want to estimate a low-dimensional model characterizing the population. Or consider [[Low-dimensionality manifold hypotheis]]
When given input of certain conditions, it can generate output with structure as rich as the input. This technique can be applied to speech synthesis, text translation etc. And it can generate data for other models, simulate the experiment environment and support reinforcement learning or simulate the reward of each action and use it in planning. We care this in:
* Simulation environments
* Prediction
* Inverse problems
* Transfer learning
Finally this might help us to leverage the unlabeled data problem. This possibility has not been demonstrated yet but we can assume the intermediate layers of GAN can be used as features for supervised tasks.
This task is challenging because we have to learn a representation from unlabeled data that captures regularity and complexity.
! Previous training methods
[[Survey of earlier work|http://yann.lecun.com/exdb/publis/pdf/lecun-06.pdf]]
People always train a generative model that represents the probability distribution. And fit this model using maximum likelihood.
$$
\theta^* = \underset{\theta}{max}\frac 1 m\sum_{i=1}^m\log p(x^{(i)};\theta)
$$
Prior can be encoded in a parametric generatvie model with density estimation. [[GMM]] is a shallow model that assumes density concentrates in a finite number of modes. If data is sequential, we can exploit temporal regularity with [[Word2Vec]].
Until a few years ago the most popular way is to use undirected graphical models. And the flagship under the context of neural network is ''Deep Boltzmann machines'', where we define an energy function over the visible units and several hidden layers. We define an unnormalized probability distribution by taking $e$ to the negative energy fucntion, which has no constraint on it. And we normalize this positive functions to obtain a probability distribution function:
$$
\begin{align}
p(h, x) &= \frac 1 Z \tilde p(h, x)\\
\tilde p(h, x) &= \exp(-E(h, x))\\
Z&=\sum_{h, x}\tilde p(h, x)
\end{align}
$$
Unfortunately, computing that normalizing constant is NP-complete and our ability to approximate it is limited. In particular, if we train a Boltzmann machine well enough to represent the sharp distribution of widely separated modes, then our ability to approximate it decays. Our best approximation to the partition function is based on drawing samples from the model using ''MCMC''. And this Monte Carlo method works well as long as it is able to move between different modes. As the modes become sharper and better separated, we have a more difficult time transitioning between them. There are a lot of strategies to increase the temperature of the distribution temperarily in order to force the Markov chain to mix. But this problem is not solved to our satisfaction.
Some have been studying directed graphical models, where a similar problem arises. To compute the partition function, we need to sum over all configuartions of hidden states. ''Variational learning'' has been used to solve this. Where we maximize $\log p(x)-\mathcal D_{KL}(q(x)||p(z|x))$. And one of the most popular deep directed model is the [[Variational Autoencoder]]. The basic idea is we have a distribution of the hidden units $q(z)$, and we can draw samples from this distribution with a neural network and some noise. This idea of a differentiable decoder, a.k.a. a generative net is very wide spread and serveral approaches to train the generative decoder nets.
Another popular model is generative stochastic networks. These networks learn the transition probability of the Markov chains. Which are very similar to GANs. The basic strategy is to give up on having an explicit formula for $p(x)$, just learn to sample incrementally. The drawback is it is difficult for Markov chains to mix from one example to another extremely dissimilar example.
! Fast Neural Style ! Image2Vec # Follow reddit posts on [[VAE vs GAN|https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/]] and [[VAE Tutorial|https://www.reddit.com/r/MachineLearning/comments/4paxkq/160605908_tutorial_on_variational_autoencoders/]] # [[Deformation Metric]]: Can this stability criterion be used as perceptual feature loss for neural style? # run on chinese char and face # try (S)GAN as base. @@color:#859900;DoctorTeeth/supergan repo is not there.@@ # Wait for Google Stlye transfer # GAN super resolution [[in torch|https://github.com/leehomyc/Photo-Realistic-Super-Resoluton]]
! Overview * [[Deep Generative Model Overview]] ! Models * Denoising Autoencoder ** [[Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space|https://arxiv.org/abs/1612.00005]] * [[Variational Autoencoder]] * [[Generative Adversarial Network]] * [[Input Convex Neural Network]] * [[Variational Generative Models]] * [[TCL|Time-Contrastive Learning]] * Inverse Autoregressive Flow ** [[paper|https://arxiv.org/abs/1606.04934]], [[tf implementation|https://github.com/openai/iaf]] * [[Real NVP|http://arxiv.org/abs/1605.08803]] * [[Attentive Generative Models]] * [[DISCO Net]] * [[Convolutional Gaussian Processes|https://arxiv.org/abs/1709.01894]] !! [[Autoregressive Models]] [[GANs vs VAEs]] ! Bibs * [[yingzhen's reading list|http://www.yingzhenli.net/home/blog/?p=566]] * [[Magenta's reviews|https://github.com/tensorflow/magenta/tree/master/magenta/reviews]] * [[What does it take to generate natural textures?]] on evaluation * [[On the Quantitative Analysis of Decoder-Based Generative Models|https://openreview.net/forum?id=B1M8JF9xx¬eId=B1M8JF9xx]] ! Applications * [[Neural Style]] * [[High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis|https://arxiv.org/abs/1611.09969]] ** Autoencoder with perceptual loss * [[Imitation Learning]]
<<tabs "[tag[Deep Learning]nsort[order]]" "Deep Learning Theory" "$:/state/tab" "tc-vertical">>
! Categories !! Vision # [[Object Detection With CNNs]] # [[CNN for Video]] # [[CNN for NLP]] # [[Visual Question Answering]] # [[Image Captioning]] # [[Deep Representation Learning]] # [[Deep Generative Models]] # [[Deep Super-Resolution]] # [[Deep Physical Simulation]] !! Speech * [[Deep Speech Recognition]] * [[Deep Speaker Adaptation]] !! Others * [[Reinforcement Learning]] * [[Deep Model Compression]] * Neural Message Passing for Quantum Chemistry ! Resources # [[Awesome Deep Learning|https://github.com/kjw0612/awesome-deep-vision#video-captioning]]
* [[Neural Net Functions]] * [[Deep Neural Networks Architectures]] * [[Back Propagation]] * [[Optimization Algorithms]] * [[Deep Learning Tricks]]
! Theory !! General * [[Neural Networks: Tricks of the Trade]] Second Edition, Editors: Grégoire Montavon, Geneviève B. Orr, Klaus-Robert Müller, 2012. !! RNN * Martens J, Sutskever I. Learning recurrent neural networks with hessian-free optimization[C]. Proceedings of the 28th International Conference on Machine Learning (ICML-11). 2011: 1033-1040. [[BibTeX|martens2011learning.txt]] * Hochreiter, Sepp, and Jürgen Schmidhuber. [[Long short-term memory]]. Neural computation 9.8 (1997): 1735-1780. * Sutskever, Ilya. [[Training recurrent neural networks|Thesis Training RNN]]. Diss. University of Toronto, 2013. * Chan, Lai-Wan, and Chi-Cheong Szeto. [[Training recurrent network with block-diagonal approximated Levenberg-Marquardt algorithm|Block-diagonal LM]]. Neural Networks, 1999. IJCNN'99. International Joint Conference on. Vol. 3. IEEE, 1999. ! Vision * [[Fast-RCNN]] ! Speech !! ASR * Dahl G E, Yu D, Deng L, et al. [[Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition|DNN ASR]][J]. Audio, Speech, and Language Processing, IEEE Transactions on, 2012, 20(1): 30-42. * Graves, Alex, and Jürgen Schmidhuber. [[Framewise phoneme classification with bidirectional LSTM and other neural network architectures|LSTM Phoneme Classification]] Neural Networks 18.5 (2005): 602-610. [[BibTeX|graves2005framewise.txt]] * Graves, Alan, Abdel-rahman Mohamed, and Geoffrey Hinton. [[Speech recognition with deep recurrent neural networks.|Transducer LSTM]] Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013. * Graves, Alan, Navdeep Jaitly, and Abdel-rahman Mohamed. [[Hybrid speech recognition with deep bidirectional LSTM." Automatic Speech Recognition and Understanding (ASRU)|Hybrid LSTM]], 2013 IEEE Workshop on. IEEE, 2013. !! Feature learning * Wang W, Arora R, Livescu K, et al. UNSUPERVISED LEARNING OF ACOUSTIC FEATURES VIA DEEP CANONICAL CORRELATION ANALYSIS[J]. * Lee H, Pham P, Largman Y, et al. [[Unsupervised feature learning for audio classification using convolutional deep belief networks|CRBM for audio]][C]. Advances in neural information processing systems. 2009: 1096-1104. ([[BibTeX|lee09unsupervised.txt]]) !! TTS * Ze H, Senior A, Schuster M. [[Statistical parametric speech synthesis using deep neural networks|Google DNN TTS]][C]. Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013: 7962-7966. ([[BibTeX|ze13statistical.txt]]) !! Voice conversion * Chen L H, Ling Z H, Liu L J, et al. [[Voice conversion using deep neural networks with layer-wise generative training|USTC DNN VC]][J]. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 2014, 22(12): 1859-1872. ([[BibTeX|chen14voice.txt]]) * Desai S, Black A W, Yegnanarayana B, et al. Spectral mapping using artificial neural networks for voice conversion[J]. Audio, Speech, and Language Processing, IEEE Transactions on, 2010, 18(5): 954-964. ([[BibTeX|desai10spectral.txt]]) * Nakashika T, Takashima R, Takiguchi T, et al. Voice conversion in high-order eigen space using deep belief nets[C]. INTERSPEECH. 2013: 369-372. ([[BibTeX|nakashika13voice.txt]]) ! [[TODO List|Paper Reading Group]] # [[top 5 arxiv papers|http://www.kdnuggets.com/2015/10/top-arxiv-deep-learning-papers-explained.html/2]]
[[UCB - Topics in Deep Learning]]
! Implementations [[GitXiv Projects]] * [[Deep Learning Tools]] * [[Low Precision Training]] * [[Deep Model Compression]] * [[Model Distillation]] * [[Visualizing a ConvNet]] ! Performance * [[CATERPILLAR: Coarse Grain Reconfigurable Architecture for Accelerating the Training of Deep Neural Networks|https://arxiv.org/abs/1706.00517]] * [[RNN Performance Optimization]] * [[Improving the speed of neural networks on CPUs|https://research.google.com/pubs/pub37631.html]] ! Training Techniques * [[Sobolev Training for Neural Networks]] * [[Distributed Training]] ! Hardware * [[Google TPU]] ! Serving Frameworks * [[TensorFlow Serving|https://tensorflow.github.io/serving/]]: Serving tf models * [[MXNet-Lambda|https://github.com/awslabs/mxnet-lambda]]: Deploy Apache MXNet with AWS Lambda * [[deepdetect|https://github.com/beniz/deepdetect]]: Deep Learning API and Server in C++11 with Python bindings and support for Caffe, Tensorflow, XGBoost and TSNE * [[tf-docker-demo|https://g.hz.netease.com/mi-demo/tf-docker-demo]]: Serving tf models within Docker as Flask application * [[rancher|https://github.com/rancher/rancher]]
[[link|https://www.youtube.com/watch?v=lKVIXI8Djv4]] [[slides|https://drive.google.com/file/d/0BwMz1xbo9yErdEI0UHh3YzRLNXM/view]]
The lecturer discussed how to relate human brain with deep learning optimization.
The central problem is credit assignment in hidden layers (or and through time). [This work|?] explains some relation between hidden layers' credit with brain.
Spike-timing Dependent Plasticity (STDP): there is a relationship between the timing of pre-synaptic spike with post spike on the weight change. Learning rule is modeled like STDP. ''Hypothesis 1'':
$$
\delta W_{i,j}\propto \dot{s_i}\rho(s_j)
$$
''Coincidence'': This simulation works
''Hypothesis 2'':
This matches SGD if the rate of change of neurons corresponds to the gradient of an objective function.
$$
\dot{s_i} = -\frac{\partial J}{\partial s_i}
$$
and
$$
s_i=a+b\sum_iW_{ij}\rho(s_j)
$$
Leaky integrator neurons with state (integrated voltage) s:
$$
\dot{s_i} = \epsilon(R(s)-s)=-\epsilon\frac{\partial E}{\partial s}
$$
where $R(s)$ is where the neuron's activation would converge
$$
R(s)\propto b+W\rho(s)
$$
''coincidence'': Denoising auto-encoders with reconstruction function $R(s)$ converge towards $R(s)-s$ = gradient of energy.
''Hypothesis #3'': neural computation = inference:
Neural activations tend to noisily move towards configurations making neurons' activations more compatible with each other according to some energy function.
We would like to choose an energy function that:
$$
\frac{\partial E(s)}{\partial s} = s-R(s)
$$
Lyapunov function has this property, and are some other pretty ones.
''coincidence'': auto-encoders tends to be symmetric
''coincidence'': early inference in continuous-variable energy-based models approximates back propagation.
Near a fixed point, this kind of network updates like back propagation.
SGD update gives a contrastive Hebbian learning step:
$$
\delta W_{ij}\propto \frac{\partial}{\partial \beta_y}\rho(s_i)\rho(s_j)\approx\rho(s_i^+)\rho(s_j^+)-\rho(s_i^-)\rho(s_j^-)
$$
* Making deep networks amenable to (stable!) online updates from weakly supervised data ** True lifelong learning and open up many applications * "Batch normalization" for GANs or Deep RL ** make everything wants to train by default * Moving away from supervised learning ** [[Capsules]] ** efficiently use data for more complex problems ** Come up with better exploration strategies as well as active learning approaches to acquire the relevant information while keeping training manageable * Build single machine learning system that can solve thousands or millions of tasks and can draw from the experience in solving these tasks to learn to automatically solve new tasks. ** [[Jeff's talk|https://www.matroid.com/scaledml/2017/jeff.pdf]] * [[New Directions for RNNs]] * Biologically plausible neural net systems * Exactly solvable DNN models (through symmetry & group theory) * New/better learning algorithms & design principles * Predictions on the organization of biological layered networks
! Probabilistic Models * [[David Duvenaud|https://www.cs.toronto.edu/~duvenaud/]]: PhD@Cambridge, Harvard, AP@TorontoU. Good PyTorch Coder. ** Talks: [[Generator-aware Discriminators & Discriminator-aware Generators|DALI 2017 GAN Workshop]] * [[David Pfau|http://davidpfau.com/]]: DeepMind ** Working on GAN, RL and Meta Learning * Bharath K. Sriperumbudur * Bernhard Schölkopf * Xi Chen ! Optimization * Sanjeev Arora * Rong Ge * Tengyu Ma ! Modelling * [[Oriol Vinyals|https://research.google.com/pubs/OriolVinyals.html]]: DeepMind, PhD@UCB ** Sequence modeling: Seq2Seq, Bayesian RNN, LSTM variants. ! RL * Xi Chen ! NLP * [[Graham Neubig|http://www.phontron.com/publications.php]]: neulab has some very cool implementations for NMT and NL2code
! Introduction
We will walk through Part II of the [[Deep Learning textbook|http://www.deeplearningbook.org/]] by Goodfellow et al. A pdf version can be found [[here|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/textbooks/deep_learning.pdf]]. Some of Goodfellow's most noticeable works are:
# ''Generative Adversarial Network'': ICML 2015 DL Workshop talk [[(transcript)|Generative Adversarial Network]] [[(video)|https://www.youtube.com/watch?v=LXDuuYSNtUY]]
# ''Adversarial Examples'': HORSE 2016 talk [[(slides)|https://goodfeli.github.io/slides/2016_09_19_horse.pdf]] [[(video)|https://www.youtube.com/watch?v=f95EhYFmsj0]] [[transcript|Adversarial Examples]]
Unless otherwise specified the course lectures and meeting times are:
//Tuesday 19:00-20:00//
!! Guidelines
# Suggested reading materials are listed to enhance understanding of the textbook. Extra reading materials are welcomed to be added to the list. The lecturer is not obliged to cover those papers/blog posts.
# The lecturer is suggested to upload the slides to [[this git repo|https://g.hz.netease.com/mi-tools/mi-course/tree/dev/seminar]] before seminar.
# The scribe should keep the notes of the seminar for later reference. A nice script format is `.pdf` or `.html`. Choosing between LaTeX/Markdown as the markup language according to the lecture content is strongly recommended. You may download the LaTeX template from [[here|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/template.zip]]. @@color:#859900;The materials already covered in the book is not necessary to be taken down, a reference of certain position in the book is enough.@@
!! Rotation
|!Lecturer |!Scribe |
|郭贺 |刘丽娟 |
|张晓博 |李一夫 |
|侯章军 |白海 |
|周立峰 |齐狄浩 |
|杨旭东 |曽杨 |
|陈昊 |张鹏 |
|刘丽娟 |郭贺 |
|李一夫 |张晓博 |
|白海 |侯章军 |
|齐狄浩 |周立峰 |
|曽杨 |杨旭东 |
|张鹏 |陈昊 |
! Schedule and Syllabus
|!No. |!Chapter |!Description |!Course Materials |!Lecturer |
|1 |6.{1-4} |XOR example, neural net architecture, loss, activations |Suggested Readings:<br><li>[[PReLU|https://arxiv.org/abs/1502.01852]]</li><li>[[Do Deep Convolutional Nets Really Need to be Deep and Convolutional?|https://arxiv.org/abs/1603.05691]]</li> |曽杨 |
|2 |6.{5-6} |Backprop |Suggested Readings:<br><li>[[Backprop with Tensorflow|http://blog.aloni.org/posts/backprop-with-tensorflow/]]</li> |刘丽娟 |
|3 |7.{1-3} |Parameter regularization |Suggested Readings:<br><li>[[Regularization insights|https://arxiv.org/abs/1207.0580]]</li> |郭贺 |
|4 |7.{4-11} |Data augmentation, label smoothing, early stopping, ensemble methods |Suggested Reading:<br>TBD |张晓博 |
|5 |7.{12-14} |Dropout, adverasrial training, learning on manifold |Suggested Readings:<br><li>[[Dropout|http://jmlr.org/papers/v15/srivastava14a.html]]</li><li>[[Adversarial training|https://arxiv.org/abs/1412.6572]]</li> |李一夫 |
|6 |8.{1-2} |[[On Optimization in Deep Learning]] |Suggested Readings:<br><li>[[Qualitatively characterizing neural network optimization problems|https://arxiv.org/abs/1412.6544]]</li><li>[[Reddit: deep learning without local minima|https://news.ycombinator.com/item?id=11765111]]</li><li>[[Why does Deep Learning work?|https://charlesmartin14.wordpress.com/2015/03/25/why-does-deep-learning-work/]]</li> |陈昊 |
|7 |8.{3-5} |SGD, momentum, adaptive learning rate |Suggested Readings:<br><li>[[Unit tests for stochastic optimization|https://arxiv.org/abs/1312.6055]]</li> |齐狄浩 |
|8 |8.{7} |Batch normalization, running average, other strategies. (skipping chapter 6) |Suggested Readings:<br><li>[[Thesis: 2nd Order RNN|http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf]]</li> |杨旭东 |
|9 |9.{1-5} |CONV, POOL, other strategies |Suggested Readings:<br><li>[[Fast Algorithms for Convolutional Neural Networks|https://arxiv.org/abs/1509.09308]]</li><li>[[cs231n lecture notes|http://cs231n.github.io/convolutional-networks/]]</li> |侯章军 |
|10 |9.{6-11} |Structured output, unsupervised training, relation with neuroscience |Suggested Readings:<br><li>[[Fully Convolutional Networks for Semantic Segmentation|https://arxiv.org/abs/1411.4038]]</li><li>[[Super resolution with CONV|https://arxiv.org/abs/1609.07009]]</li> |张鹏 |
|11 |10.{1-3} |RNN, Bi-RNN |Suggested Readings:<br><li>[[Speech recognition with RNN|http://www.cs.toronto.edu/~fritz/absps/RNN13.pdf]]</li><li>[[The Unreasonable Effectiveness of RNN|http://karpathy.github.io/2015/05/21/rnn-effectiveness/]]</li> |周立峰 |
|12 |10.{4-6} |Seq2seq, recursive NN, [[slides|https://docs.google.com/presentation/d/1fiS5J2a7IqgLZhVEoSC-XvreMILKiyZERWLTNzwL7Lo/edit?usp=sharing]] |Suggested Readings:<br><li>[[Sequence to Sequence Learning with Neural Networks|https://arxiv.org/abs/1409.3215]]</li><li>[[Tensorflow Tutorial: Sequence-to-Sequence Models|https://www.tensorflow.org/versions/r0.11/tutorials/seq2seq/index.html]]</li><li>[[Parsing Natural Scenes and Natural Language with Recursive NNs|http://www-nlp.stanford.edu/pubs/SocherLinNgManning_ICML2011.pdf]]</li> |陈昊 |
|13 |10.{7-12} |Long-term dependency, echo state networks, Gated RNNs, optimization, explicit memory |Suggested Readings:<br><li>[[MemNN|http://arxiv.org/pdf/1410.3916v8.pdf]]</li><li>[[LSTM: A Search Space Odyssey|http://arxiv.org/abs/1503.04069]]</li><li>[[Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks|https://arxiv.org/abs/1502.05698]]</li> |白海 |
|14 |11.{1-6} |Tuning model performance w.r.t. hyperparameters, debugging strategies, SVT example |Suggested Readings:<br><li>[[Google Street View paper|https://arxiv.org/abs/1312.6082v4]]</li> |杨旭东 |
! Dealing non-linearity
Nonlinearity of problems like XOR popularized [[non-linear hidden layers|http://dl.acm.org/citation.cfm?id=104293]] and kernel methods.
Another well-known example is [[the parity problem|https://en.wikipedia.org/wiki/Perceptrons_(book)]]: although a single non-linear hidden layer is sufficient to represent any function, it is not guaranteed to represent it efficiently, can even require exponentially many more parameters than a deeper model.
! Approximiation Theory
Deep Networks define a class of "universal approximators":
''Theorem'' [C'89, H'91] Let $\rho()$ be a bounded, non-constant continuous function. Let $I_m$ denote the $m$-dimensional hypercube, and $C(I_m)$ denote the space of continuous functions on $I_m$. Given any $f\in C(I_m)$ and $\epsilon > 0$, there exists $N > 0$ and $v_i, w_i, b_i, i=1, \dots, N$ such that
$$
F(x) = \sum_{i\le N}v_i\rho(w_i^\top x + b_i)\qquad\text{satisfies}
$$
$$
\underset{x\in I_m}{\sup} |f(x)-F(x)|<\epsilon
$$
This guarantees that even a single hidden-layer network can represent any classification problem in which the boundary is locally linear (smooth). But we do not know how to choose a good architecture, or how to optimize it.
! Estimation Theory
''Theorem'' [Barron'92] The mean integraded square error between the estimated network $\hat F$ and the target function $f$ is bounded by
$$
O\left(\frac{C_f^2}{N}\right) + O\left(\frac{Nm}{K}\log K\right),
$$
where $K$ is the number of training points, $N$ is the number of neurons, $m$ is the input dimension, and $C_f$ measures the global smoothness of $f$.
This formula combines approximation and estimation error. We cannot explain why @@color:#dc322f;online/stochastic optimization works better than batch normalization@@.
! Plausible Optimization
* [[On Optimization in Deep Learning]]
* [[Rethinking Generalization]]
! Statistical Learning Theory
The complexity or capacity of NNs can be computed by measuring how many configurations can be shattered (VC-dimension)
The capacity of the network, if measured by the number of pieces in a piecewise linear approximation, increases exponentially with depth. [Montufar, Pascanu et al, '14]
These results quantify an upper bound on the empirical risk of deep neural networks. But the bounds might be very pessimistic. We still have to figure out @@color:#dc322f;the superior generalization properties of CNNs versus models with similar capacity@@.
There is a functional equivalence between models of different depths at equal capacity [Ba and Caruana '14].
! Information Theory View
[[Information Theory for Deep Learning]]
! Understanding DNN
* [[Opening the Black Box of Deep Neural Networks via Information|https://arxiv.org/abs/1703.00810]]
* [[Energy Propagation in Deep Convolutional Neural Networks|https://arxiv.org/abs/1704.03636]]
* [[Exploring loss function topology with cyclical learning rates|https://arxiv.org/abs/1702.04283]]
! Interpretability
* [[Deep learning in biology]]
* DeepMind: [[Interpreting Deep Neural Networks using Cognitive Psychology|https://deepmind.com/blog/cognitive-psychology/]]
* Static ** [[Torch]] ** [[TensorFlow]] ** [[Caffe]] * Dynamic: write codes that computes predictions, symbolic representation of computation is written down implicitly (based on operator overloading) by toolkit. ** PyTorch *** [[The Incredible PyTorch|https://github.com/ritchieng/the-incredible-pytorch]] ** [[DyNet|https://github.com/clab/dynet]]: supports on-the-fly batching ! Others * [[Kaldi]] * docker-compose: save docker run settings [[Benchmarking State-of-the-Art Tools|https://github.com/hclhkbu/dlbench]] The problem with this setting is that when we have a very large model, e.g. MS'es 150-layer one, the unit has to be copied many many times.
! Choosing Hyperparameter * Random search ([[Bengio 2012|Neural Networks: Tricks of the Trade]]) * Bayesian optimization ([[Yogatama and Smith, 2015|http://arxiv.org/abs/1503.00693]]; [[Bergstra et al., 2013|http://www.jmlr.org/proceedings/papers/v28/bergstra13.pdf]]) !! Examples * [[Sentense classification|A Sensitivity Analysis of CNN for Sentence Classification]] * [[Unsupervised feature learning|http://www.jmlr.org/proceedings/papers/v15/coates11a/coates11a.pdf]] * [[Hyperparameter in SGD|http://arxiv.org/abs/1508.02788]] However, sophisticated search methods require previous knowledge about which hyperparameters are worth exploring to begin with. And Bayesian optimization is quite far from practical usage. ! Special Layers [[Batch Normalization]]: addressing the problem of the internal covariate shift ! Topics * [[Adversarial Examples]] * [[Data Augmentation]] * [[Deep Optimization Tricks]] * [[Deep Model Compression]] * [[Regularization for Deep Learning]] * Distributed training ** [[Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability|http://arxiv.org/abs/1609.06870]]
* [[blog|https://research.googleblog.com/2017/02/announcing-tensorflow-fold-deep.html]] * [[paper|https://arxiv.org/abs/1702.02181]] Implements binary Tree-LSTM and graph convolution model
! Bib !! Inference * [[Accelerating Deep Convolutional Networks using low-precision and sparsity|http://arxiv.org/abs/1610.00324v1]] * [[High-Dimensional Geometry of Binary Neural Networks]] !! Training * [[Training Quantized Nets: A Deeper Understanding]] * [[BitNet: Bit-Regularized Deep Neural Networks|https://arxiv.org/abs/1708.04788]] * [[Deep Binary Reconstruction for Cross-modal Hashing|https://arxiv.org/abs/1708.05127]]: for retrieval !! Pruning * [[The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning|http://openreview.net/pdf?id=BkV4VS9ll]] ** A rather theoretical study of pruning. 2-nd order Taylor without retraining.
! Old fashioned * [[RBF Nerwork]] * [[Restricted Boltzmann Machines]] ! State-of-the-Art * [[Convolutional Neural Network]] * [[Recurrent Neural Networks]] Neural Architecture Search: * generally a parameter search with RL * try some low complexity methods like random forest? * what those models indicate?
# learning with a curriculum (Bengio et al., 2009) # training with diffusion (Mobahi, 2016) - a form of continuation method # noisy activation functions improve performance on a wide variety of tasks (Gulcehre et al., 2016) # [[Mollifying Networks]] # [[Low Precision Training]] Impact of noise injection (Neelakantan et al., 2015) !! Using mini-batches Using mini-batchesof examples, as opposedto one example at a time, is helpful in several ways. First, the gradient of the loss over a mini-batch is an estimate of the gradient over the training set, whose quality improves as the batch size increases. Second, computation over a batch can be much more efficient than m computations for individual examples, due to the parallelism afforded by the modern computing platforms.
* [[A Compositional Object-Based Approach to Learning Physical Dynamics|https://arxiv.org/abs/1612.00341]] ** SGD to learn simple physical laws?
* classic: hierarchical clustering
* mainstream: autoencoders
* deep belief networks
! DNN for image retrieval
With a CNN learned on large labeled set, the output of the intermediate layers can be used as image descriptors. Global CNN descriptors lack geometric invariance [[link|http://arxiv.org/abs/1403.1840]].
[[Deep patches|https://hal.inria.fr/hal-01207966/document]] does not require supervision and utilizes outputs from different layers. Images are processed under the three-step pipeline:
# Interest point detection. Use Hessian-Affine detector to extract points at their characteristic scale and estimate for each point an affine-invariant local region (rotate to dominate gradient orientation).
# Interest point description. Map the affine region to a square and learn its representation.
# Patch matching. Given a clustering of the feature space consisting of $k$ centroids $\{c_1, \dots, c_k\}$, VLAD encodes a set of descriptors as the total shift with respect to their assigned centroid.
To extend CNNs for image retrieval, it is possible to use a unsupervicsed net such as convolutional kernel network. The feature representation of CKN is based on a kernel (feature) map and hence data-independent. Let $M$ and $M'$ be two patches of size $m\times m$, and $\Omega=\{1,\dots,m\}^2$ be a set of pixel locations. The sub-patch $p_z$ from $M$ is of fixed size.
!! Bib
* [[End-to-end Learning of Deep Visual Representations for Image Retrieval|https://arxiv.org/abs/1610.07940]]
! Generating natural images
''Non-parametric'': texture synthesis, super-resolution, in-painting
''parametric''
* variational sampling
* iterative forward diffution process
* GANs
** laplacian pyramid extension
* RNN
* deconvolution nets
! Papers
! Linear Transformations
Linear tarnsformations can be applied at
# input features
# activation of hidden layer
# input to the softmax layer
No matter where the linear transformation is applied, it is typically trained from an ''identity weight matrix'' and ''zero bias'' to optimize either the cross-entropy (CE) training criterion.
''Remark'': The activations inside the DNN are not linear, so maybe softmax is where the transformation is resonable.
!! Linear Input Networks
LIN claims that speaker-dependent features can be linearly transformed to match the average behavior which the speaker-independent DNN model described. LIN is impleneted by adding a linear layer $W^{LIN}$ to the input feature.
''Remark'': Lienar transformation (esp. fer frame) is too simple to handle speaker dependent feature.
!! Linear Output Networks
Since the last hidden layer can be considered as the transformed feature, it is (presumably) reasonable to apply a linear transformation on the last hidden layer for a specific speaker so that after the linear transformation it matches better to the average speaker.
This can be added before the last hidden layer or the output layer depending on which has a smaller number of parameters.
! Conservative Training
Surprisingly, adapting all parameters in DNN to optimize the adaptation criterion over the adaptation set can destroy previously learned information.
CT adds regularization to the adaptation criterion, e.g. to adapt only selected weights.
!! $L_2$ Regularization
The basic idea of the $L_2$ regularized CT is to add the $L_2$ norm of the model parameter difference
$$
R_2(W_{SI}-W) = \|vec(W_{SI}-W)\|^2_2
$$
Then the adapation criterion becomes
$$
J_{L_2}(w, b;S) = J(W, b;S)+\lambda R_2(W_{SI}, W),
$$
!! KL-Divergence Regularization
$$
R_{KLD}(W_{SI}, b_{SI}; W, b; S) = \frac 1 M\sum_{m=1}^M\sum_{i=1}^CP_{SI}(i|o_m;W_{SI}, b_{SI})\log P(i|o_m;W, b),
$$
$P_{SI}(i|o_m;W_{SI}, b_{SI})$ and $P(i|o_m;W, b)$ are the probability that the $m$th observation $o_m$ belongs to class $i$, estimated from the speaker-independent and the adapted DNNs,respectively.
KLD regularization constrains the output probabilities rather than the model parameters. The KLD regularized adaptation technique can be easily extended to the ''sequence-discriminative training''. To prevent overfitting the ''frame smoothing'' is often used in the sequence-discriminative training which leads to the interpolated training criterion
''TODO'': consider changing $P_{SI}$ to a distribution independent from speaker features.
!! Reducing Per-Speaker Footprint
Conservative training cannot solve the problem that a huge adapted model needs to be saved for each speaker (model is too large).
! Subspace Methods
!! PCA
PCA subspace construction aims to simplify the adapation weights to speed up the adaptation.
We extend the problem to a general condition, the adaptation aims to estimate a speaker-specific vector $a$ for each speaker. This approach assumes that new speakers can be represented as a point in the space spanned by the $S$ speakers. For each new speaker,
$$
a = \bar a+\tilde U\tilde g_a
$$
where $\tilde U$ and $\tilde g_a$ are reduced eigen matrix and reduced projection of the adaptation parameter vector and $\bar a$ is the mean of the adaptation parameters.
!! Tensor
The speaker and speech subspaces can also be estimated and combined using three-way connections. An example is disjoint factorized DNN. The speaker posterior probability $p(s|x_t)$ is estimated from the acoustic feature $x_t$ using a DNN. The class posterior probability $p(y_t = i|x_t)$ is estimated as
$$
p(y_t = i|x_t) = \sum_s\frac{\exp(s^\top W_iv_t^{L-1})}{\sum_j\exp(s^\top W_jv_t^{L-1})}p(s|x_t)
$$
where $W\in R^{N_L\times S\times N_{L-1}}$ is a tensor, $S$ is the numebr of output classes in the speaker identification DNN.
! Books * [[DNN for ASR]]
! Theory 2 ways of doing it with covn: # upsampling # deconv ! Bibs * [[Deep Depth Super-Resolution : Learning Depth Super-Resolution using Deep Convolutional Neural Network|https://arxiv.org/abs/1607.01977]] ** examples are fairly simple * [[Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network|https://arxiv.org/abs/1609.05158]] ** [[a supplement|https://arxiv.org/abs/1609.07009]] discussing deconvolution is also worth reading * [[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network]]
[[blog|https://medium.com/@phelixlau/notes-on-deformable-convolutional-networks-baaabbc11cf3]] [[paper|https://arxiv.org/abs/1703.06211]] [[mxnet|https://github.com/msracver/Deformable-ConvNets]] Deformable conv adds a new layer to learn 2D offset for each input. This can be seen as a learnable [[Dilated Convolution]].
We consider the following deformatin cost:
$$
\|\tau\|:=2^{-J}\|\tau\|_\infty+\|\nabla\tau\|_\infty.
$$
Scale $J$ controls how much we pay for absolute displacements. Stability criterion: $\forall \|x\|=1, \tau, \|\Phi(x)-\Phi(x_\tau)\|\le C\|\tau\|$. [[Think about 1|TODOs]]
We can define similar metrics for di↵eomorphisms associated with other transformation groups (e.g. rotation).
[[Fourier Transform vs Wavelet Transform]]
[[Learning in Implicit Generative Models|https://arxiv.org/abs/1610.03483]] We can test the hypothesis that the true data distribution $p^*(x)$ and our model distribution $q(x)$ are equal, using the density ratio function $r(x) = p^*(x)/q(x)$. We can compute this ratio with # class-probability estimation # [[divergence minimization|Divergence Classed GANs] # ratio matching # moment matching The original objective can be obtained by add a Bernoulli (logarithmic) loss over the discriminator $\mathcal D(x;\phi) = p(y=+1|x)$.
@article{desai2010spectral,
title={Spectral mapping using artificial neural networks for voice conversion},
author={Desai, Srinivas and Black, Alan W and Yegnanarayana, B and Prahallad, Kishore},
journal={Audio, Speech, and Language Processing, IEEE Transactions on},
volume={18},
number={5},
pages={954--964},
year={2010},
publisher={IEEE}
}
!! Prefer a stack of small filter CONV to one large receptive field CONV Suppose that you stack three 3x3 CONV layers on top of each other (with non-linearities in between, of course). In this arrangement, each neuron on the first CONV layer has a 3x3 view of the input volume. A neuron on the second CONV layer has a 3x3 view of the first CONV layer, and hence by extension a 5x5 view of the input volume. Suppose that instead of these three layers of 3x3 CONV, we only wanted to use a single CONV layer with 7x7 receptive fields. These neurons would have a receptive field size of the input volume that is identical in spatial extent (7x7), but with several disadvantages. # The neurons would be computing a linear function over the input, while the three stacks of CONV layers contain non-linearities that make their features more expressive. #If we suppose that all the volumes have $C$ channels, then it can be seen that the single 7x7 CONV layer would contain $C\times(7\times7\times C)=49C^2$ parameters, while the three 3x3 CONV layers would only contain $3\times(C\times(3\times3\times C))=27C^2$ parameters. Intuitively, stacking CONV layers with tiny filters as opposed to having one CONV layer with big filters allows us to express more powerful features of the input, and with fewer parameters. As a practical disadvantage, we might need more memory to hold all the intermediate CONV layer results if we plan to do backpropagation. !! Layer Sizings CONV layer should be using small filters (e.g. 3x3 or at most 5x5), using a stride of $S=1$. The most common setting of POOL is to use max-pooling with receptive fields 2x2 and with a stride of 2. ''Reducing sizing headaches.'' The scheme presented above is pleasing because all the CONV layers preserve the spatial size of their input, while the POOL layers alone are in charge of down-sampling the volumes spatially. In an alternative scheme where we use strides greater than 1 or don't zero-pad the input in CONV layers, we would have to very carefully keep track of the input volumes throughout the CNN architecture and make sure that all strides and filters "work out", and that the ConvNet architecture is nicely and symmetrically wired. ''Why use stride of 1 in CONV?'' Smaller strides work better in practice. Additionally, as already mentioned stride 1 allows us to leave all spatial down-sampling to the POOL layers, with the CONV layers only transforming the input volume depth-wise. ''Why use padding?'' In addition to the aforementioned benefit of keeping the spatial sizes constant after CONV, doing this actually improves performance. If the CONV layers were to not zero-pad the inputs and only perform valid convolutions, then the size of the volumes would reduce by a small amount after each CONV, and the information at the borders would be "washed away" too quickly. ''Compromising based on memory constraints.'' In some cases (especially early in the ConvNet architectures), the amount of memory can build up very quickly with the rules of thumb presented above. For example, filtering a 224x224x3 image with three 3x3 CONV layers with 64 filters each and padding 1 would create three activation volumes of size [224x224x64]. This amounts to a total of about 10 million activations, or 72MB of memory (per image, for both activations and gradients). Since GPUs are often bottlenecked by memory, it may be necessary to compromise. In practice, people prefer to make the compromise at only the first CONV layer of the network. For example, one compromise might be to use a first CONV layer with filter sizes of 7x7 and stride of 2 (as seen in a ZF net). As another example, an AlexNet uses filer sizes of 11x11 and stride of 4.
* [[Learning End-to-End Goal-Oriented Dialog|https://arxiv.org/abs/1605.07683]] test on (6) Dialog bAbI task. * Evaluation ** [[A Review of Evaluation Techniques for Social Dialogue Systems|https://arxiv.org/abs/1709.04409]]: turn-based metrics often ignore the context and do not account for the fact ** Adversarial examples for evaluating reading comprehension systems
! Paper
!! Components
* ''controller'': reads environment input $x$ and emits action (RL) or prediction (supervised) $y$, reads and writes ''memory'' with ''interface vectors'', implemented as a LSTM.
* ''memory'': Memory $M$ provides read vectors $r$. The memory is expandable because memory can be reused.
* ''heads'': read or write memory
* ''interface parameters'': is a set of keys $k$, strengths $\beta$, gates $f$ and erase and write vectors $e$ and $v$.
* ''links'': a ''precedence weight'' $p_t$ keeps track of which locations were most recently written to. Temporal order is necessary so iterate through memories in the order they were written. Temporal link matrix $L_t[i,j]$ represents teh degree to which location $i$ was the location written to after location $j$. Facilitates 2 congnitively important fucntions:
** Sequence chunking: don't write at every step
** Recording: iteratively reprocess a sequence, chunking each time
* ''free list'': tracks the usage of each memory location, usage is automatically increased after each write and optionally decreased after each read. The network can choose to write to the most free location.
!! Access
Weights are bounded by a simplex. $a, w, p\in\Delta_N$, where $N$ is the number of locations in memory.
# Update usage: Usage is automatically increased after each write ($w^W_t$) and optionally decreased after each read ($w^{r, i}_t$) by @@color:#859900;free gates@@ ($f^i_t$): $u_{t+1} = (u_t +w^W_t - u_t\circ w_t^W)\circ\prod_{i=1}^R(1-f^i_{t+1}w_t^{r,i})$. $R$ is the number of read vectors.
# Compute write weights: The controller then uses an @@color:#859900;allocation gate@@ ($g_t^a$) to interpolate between writing to a newly allocated ($a_t$) location, or an existing one found by content ($c^w_t$): $w_t^W = g_t^W[g_t^aa_t+(1-g_t^a)c_t^W]$
# Erase and write memory: $M_t = M_{t-1}\circ(E-w^W_te_t^\top)+w^W_tv_t^\top$
# Set linkage states
# Compute read weights: interpolates among backward weighting $b$, forward weighting $f$ and content read weighting $c$: $w_t^{r,i} = \pi_1b_t^i+\pi_2c_t^{r, i}+\pi_3f_t^i$
# Read words: $r_t^i = M_t^\top w_t^{r, i}$
!! Comparing to NTM
In NMT, NTM and [[Memory Networks]], a searching by ''content-based addressing'' memory mechanism is used, just a softmax.
* NTM could only 'allocate' memory in contiguous blocks, leading to memory management problems
* DNC defines a differentiable @@color:#859900;free list@@ tracking the @@color:#859900;usage@@ ($u_t$) of each memory location
!! Tasks DNC is capable of
The field can feel too open. Infer implicit graph structure. The graph traverse is done by associative key-value memory able to be queried by incomplete keys.
* Question answering
* Navigation
* Program learning
! Code
* `access` reads `controller`'s ouput and emits contents read from memory.
* `controller` is a feedforward or LSTM network.
What's the strength of using sonnet?
! Remarks
* DNC fails at bAbI basic induction task (16).
* A problem is how to scale it up.
Let $l$ be a dilated factor and let $*_l$ be defined as
$$
(F*_lk)(\mathbf p) = \sum_{\mathbf s+l\mathbf t=\mathbf p}F(\mathbf s)k(\mathbf t)
$$
The dilated convolution operator has been referred to in the past as “convolution with a dilated filter”. It plays a key role in the //algorithme à trous//, an algorithm for wavelet decomposition.
Dilated convolutions support exponential expanding receptive fields without losing resolution or coverage.
* [[paper|https://arxiv.org/abs/1706.00388]] * [[Implementation|https://github.com/szagoruyko/diracnets]] able to outperform ResNet-1000 with plain DiracNet with only 34 layers
Let $H$ be a measure on some parameter space $\Theta$, like the [[conjugate priors|Dirichlet-multinomial distribution]], a Dirichlet process, , is then a distribution over measures on $\Theta$, where the ''scalar concentration parameter'' $\gamma$ controls the similarity of samples $G\sim DP(\gamma, H)$ to the base measure $H$. Analogously to Gaussian processes, DPs may be characterized by the distribution they induce on finite, measurable partitions $(T_1,\dots, T_l)$ of $\Theta$. For such partitions, the random vector $(G(T_1), \dots, G(T_l))$ has a finite-dimensional Dirichlet distribution:
$$
(G(T_1),\dots,G(T_l))\sim Dir(\gamma H(T_1), \dots,\gamma H(T_l))
$$
Samples from DPs are discrete with probability one, a property highlighted by the following ''stick-breaking construction'':
$$
G(\theta)=\sum_{k=1}^\infty\beta_k\delta(\theta, \theta_k) \qquad \beta'_k\sim Beta(1, \gamma) \qquad \beta_k=\beta'_k\prod_{l=1}^{k-1}(1-\beta'_l)
$$
Each parameter $\theta_k\sim H$ is independently sampled from the base measure, while the weights $\beta=(\beta_1, \beta_2,\dots)$ use beta random variables to partition a unit-length "stick" of probability mass. We denote $\beta\sim GEM(\gamma)$ a sample from this stick-breaking process.
<<<
As $\gamma$ becomes large, $E[\beta'_k]=1/(1+\gamma)\rightarrow0$, and $G$ approaches $H$ by uniformly distributing probability mass among a densely sampled set of discrete parameters $\{\theta_k\}^\infty_{k=1}$.
<<<
Given $G\sim DP(\gamma, H)$, each observation $x_i$ is generated by first choosing a parameter $\bar\theta_i\sim G$, and then sampling $x_i\sim F(\bar\theta_i)$.
''Compute with CRP'': we let $z_i\sim\beta$ indicate the unique component of $G(\theta)$ associated with observation $x_i\sim F(\theta_{z_i})$. Marginalizing $G$, these assignments $z$ demonstrate an important clustering behavior. Letting $N_k$ denote the number of observations already assigned to $\theta_k$,
$$
p(z_i|z_{<i},\gamma) = \frac{1}{\gamma+i-1}\left[\sum_kN_k\delta(z_i, k)+\gamma\delta(z_i, \bar k)\right].
$$
Here, $\bar k$ indicates a previously unused mixture component.
! Sampling
The HDP follows an extension of the DP analogy known as the ''Chinese reataurant franchise''. Each object or group defines a separate restaurant in which customers (observed features) $(w_{ji}, v_{ji})$ sit at tables (clusters or parts) $t_{ji}$. Each table shares ''a single dish'' (parameter) $\tilde\theta_{lt}$, which is ordered from a menu $G_0$ shared among restaurants. Let $\mathbf k_l=\{k_{lt}\}$ denote the global parts assigned to all tables (local parts) of category $l$. We may then integrate over $G_0$ and $G_l$ to find the conditional distributions of these assignment variables:
$$
\begin{align*}
p(t_{ji}|t_{j\bar i}, \alpha)&\propto\sum_tN_{jt}\delta(t_{ji}, t) + \alpha\delta(t_{ji}, \bar t)\\
p(k_{lt}|\mathbf k_{\bar l}, k_{l\bar t}, \gamma)&\propto\sum_kM_{k}\delta(k_{lt}, k) + \gamma\delta(k_{lt}, \bar k)
\end{align*}
$$
Here, $M_k$ is the number of tables previously assigned to $\theta_k$, and $N_{jt}$ the number of customers already seated at the $t$th table in group $j$.
! Applications
[[HDP-HMM Diarization]]
It is also called the ''multivariate Polya distribution''.
! Multinomial distribution over category counts
For a random vector of category counts $\mathbf{x}=(n_1,\dots,n_K)$, distributed according to a multinomial distribution, the marginal distribution is obtained by integrating out $\mathbf p$:
$$
\Pr(\mathbf{x}\mid\boldsymbol{\alpha})=\int_{\mathbf{p}}\Pr(\mathbf{x}\mid \mathbf{p})\Pr(\mathbf{p}\mid\boldsymbol{\alpha})\textrm{d}\mathbf{p}
$$
which results in the following explicit formula:
$$
\Pr(\mathbf{x}\mid\boldsymbol{\alpha})=\frac{N!}
{\prod_{k}\left(n_{k}!\right)}\frac{\Gamma\left(A\right)}
{\Gamma\left(N+A\right)}\prod_{k}\frac{\Gamma(n_{k}+\alpha_{k})}{\Gamma(\alpha_{k})}
$$
where $A$ is defined as the sum $A = \sum \alpha_k$. Note that this differs crucially from the above formula in having an extra term at the front that looks like the factor at the front of a multinomial distribution. Another form for this same compound distribution, written more compactly in terms of the beta function, $B$, is as follows:
$$
\Pr(\mathbf{x}\mid\boldsymbol{\alpha})=\frac{N B\left(A,N\right)}
{\prod_{k:n_k>0} n_k B\left(\alpha_k,n_k \right)} .
$$
$$
\min_f\mathbb E_{(x, y)}\mathbb E_{\epsilon}[\mathcal l(y, f(x, \epsilon))-\frac{1}{2}\mathbb E_{\epsilon_1,\epsilon_2}[\mathcal l(f(x,\epsilon_1),f(x, \epsilon_2))]]
$$
[[An illustrated proof of CAP theorem|http://mwhittaker.github.io/2014/08/16/illustrated-proof-cap-theorem/]] Violations to strict serializability * stale reads: caused by network partitions. reads historical values. * dirty reads: caused by promoted update failure. read rolled back txns. * lost updates: caused by other nodes don't respond. write something but lost. ! In Action * [[Distributed Training]]
* In TensorFlow ** [[Deep Learning with Dynamic Computation Graphs]] * Parallel Framework ** [[A Framework for Parallel and Distributed Training of Neural Networks|https://arxiv.org/abs/1610.07448]] ** [[Distributed Training Large-Scale Deep Architectures|https://arxiv.org/abs/1709.06622]] ** [[Probabilistic Synchronous Parallel|https://arxiv.org/abs/1709.07772]] * SGD ** [[Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent]] ! Barrier methods * Bulk Synchronous Parallel ** deterministic ** often serializable * Stale Synchronous Parallel ** fastest worker waits if it is $s$ iterations ahead the slowest * Asynchronous Parallel ** no restrictions ** causes delayed updates * Probabilistic Synchronous Parallel
references:
* [[paper|https://arxiv.org/abs/1609.03126]]
* [[blog|http://www.inference.vc/are-energy-based-gans-actually-energy-based/]]
* [[A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models|https://arxiv.org/abs/1611.03852]]
* [[fGAN]]: generalize to $f$-divergence
! Relationship with mutual information
Original GAN objective itself can be derived from mutual information, and in fact, the discriminator $D$ can be thought of as a variational auxillary variable, exactly the same role as the recognition model $q(c|x)$ in the [[InfoGAN]] paper.
$$
\mathcal l_{GAN}(\theta) = I[x, y]
$$
The idea is simple, if $x_{real}$ and $x_{fake}$ come from the same distribution, it should be impossible to guess the value of $y$ better than chance, hence the mutual information would be 0.
! A unifying view on GAN-type algorithms
The loss for $D$, a discrepancy function $s(x)$ usually takes the following separable form:
$$
\mathcal L(s;P,Q)=\mathbb E_{x\sim P}\mathcal l_1(s(x))+E_{x\sim Q}\mathcal l_2(s(x)),
$$
In the original GAN:
$$
D(x) = \frac{1}{1+e^{-s(x)}}
$$
The training criterion for $s$ becomes:
$$
\begin{array}
\mathcal L(s) &= & \mathbb E_{x\sim P}\log D(x)+E_{x\sim Q}\log (1-D(x))\\
&=& \mathbb E_{x\sim P}\text{softminus}(s(x))+E_{x\sim Q}\text{softplus}(s(x))
\end{array}
$$
The optimal $s$ minimizes $KL[Q\|P]$. If we choose nonlinearity $f=\text{softplus}$ in the outer loop, we recover the GAN variant which minimizes [[Jensen-Shannon-divergence|https://en.wikipedia.org/wiki/Jensen%E2%80%93Shannon_divergence]], while choosing $f=\text{softminus}$ recovers the version the authors use in the original paper and in DCGAN.
It is also worth noticing that what teh original GAN objective trying to minimize is a lower bound of the GAN loss. Which may explain for the difficulty of training. (See [[this|http://www.inference.vc/infogan-variational-bound-on-mutual-information-twice/]])
And in the ''least-squares importance estimation'' case, we take loss:
$$
\mathcal L(s;P,Q)=\mathbb E_{x\sim P}s^2(x)-2\mathbb E_{x\sim Q}s(x),
$$
and the Bayes-optimal discrepancy function becomes
$$
s^*(x) = \frac{Q(s(x))}{P(x)}
$$
the same as logistic regression but without the logarithm, and this turns out to be a very important difference. For the very simple and intuitive derivation , see [[Kanamori et al, 2009|http://www.jmlr.org/papers/volume10/kanamori09a/kanamori09a.pdf]].
This is similar to the definition of the Rényi $\alpha$-divergence I wrote about last week, except for a missing logarithm. If you choose a nonlinearity $(x)=x^{\alpha−2}$, you can recover Rényi divergences for different alpha.
! Related works
Early hybrids of ANN and HMM have been used in ASR systems. Using only backpropagation to train the ANN makes it challenging to exploit more than two hidden layers and the ''context-dependent model'' described above does not take advantage of the numerous effective techniques developed for GMM-HMMs.
Neural networks for producing bottle-neck features are very similar architecturally to autoencoders since both typically have a small code layer. Deeper neural networks are known to be difficult to train with backpropagation alone.
! Problems to Concern
!! Network architecture
* size, #layers.
* increasing model size leads to overfitting
!! About Pretraining
Initializing DNN weights with unsupervised pre-training was initially thought to be important for good performance, but researchers later found that purely supervised training from random initial
weights yields nearly identical final system performance [19 of Baidu].
! Baidu 15 Arxiv
!! NN Acoustic Models
Hybrid HMM systems uses a neural network to approximate the probability of speech feature $x$ conditioned on HMM state label $y$, $p(x|y)$. We can view the neural networks as a classifier of senones given acoustic input.
$$
p(x|y) = \frac{p(y|x)p(x)}{p(y)}
$$
The prior of acoustic features $p(x)$ is usually not tractable, but fixed during decoding. The construction involves 5 steps:
# Label Set. context-dependent triphone senones.
# Forced Alignment. generate a forced alignment with HMM-GMMM system.
# Neural Net Architecture. CNN, RNN
# [[Neural Net Loss Function]]. cross entropy loss function is default for classification tasks, but it ignores ''the DNN as a component of the larger ASR system''.
# Optimization Algorithm. SGD, quasi-Newton
!! DNN Computations
Each layer has a weight matrix $W$ and bias vector $b$. We compute vector $h$ of first layer activations of a DNN using,
$$
h^{(i)}(x) = \sigma(W^{(i)\top}h^{(i-1)}+b^{(i)}).
$$
where $\sigma(z)$ is a point-wise nonlinearity function. Rectified linear units (ReLU) is shown to have better performance for classification tasks.
$$
\sigma(z) = \max(z, 0)
$$
The final layer is a softmax nonlinearity to output a properly formed probability distribution over the possible output categories,
$$
\hat y_j = \frac{\exp(W_j^{(L)\top}h^{(L-1)}+b_j^{(L)})}{\sum_{k=1}^N\exp(W_k^{(L)\top}h^{(L-1)}+b_k^{(L)})}.
$$
[[Deep Convolutional Neural Networks]]
!! Important refs
* Overview of HMM-based speech recognition systems [8-12]
* discriminative loss functions [38-41]
This is a book written by MS researchers. [[Multitask Learning for Phone label and State]] [[Connectionist Temporal Classification]] [[Speech Representation Learning]]
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! Background: Propagating Uncertainty Uncertainty information can guide output and speedup RL. Neal (1996) has shown that the prior distribution over nonlinear functions implied by the Bayesian neural network falls in a class of probability distributions known as Gaussian processes. Infinitely wide single hidden layer NNs with distributions placed over their weights converge to Gaussian processes. The Bayesian way to optimize it is to integrate these parameters out. This kind of NNs have been studied as [[Bayesian neural networks]]. One of the weakness of the model is if we take the hidden layer to infinity, we have to scale down the weight in the output. No single activation function will dominate. The square loss objective is the negative Gaussian likelihood. By adding the weight decay term, we incorporate prior to the objective. !! [[Gaussian Process]] GPs seems to be a different approach from neural networks. ! Dropout as a Bayesian Approximation of Gaussian Process [[link|http://arxiv.org/abs/1506.02142v6]] The dropout objective minimises the KL-divergence between an approximate distribution and the posterior of a deep Gaussian process. !! Deep Gaussian Process DGP allows us to model distributions over functions. A deep Gaussian process with $L$ layers and covariance function $$ K(x, y) = \int p(w)p(b)\sigma(w^\top x+b)\sigma(w^\top y+b)dwdb $$ can be approximated by placing a variational distribution over each component of a spectral decomposition fo the GP's covariance functions. ! Dropout for Recurrent Layers [[link|http://arxiv.org/abs/1512.05287v5]]
[[Understanding Dropout]] Since a fully connected layer occupies most of the parameters, it is prone to overfitting. The dropout method is introduced to prevent overfitting. At each training stage, individual nodes are either "dropped out" of the net with probability 1-p or kept with probability p, so that a reduced network is left; incoming and outgoing edges to a dropped-out node are also removed. Only the reduced network is trained on the data in that stage. The removed nodes are then reinserted into the network with their original weights. In the training stages, the probability a hidden node will be retained (i.e. not dropped) is usually 0.5; for input nodes the retention probability should be much higher, intuitively because information is directly lost when input nodes are ignored. At testing time after training has finished, we would ideally like to find a sample average of all possible 2^n dropped-out networks; unfortunately this is infeasible for large n. However, we can find an approximation by using the full network with each node's output weighted by a factor of p, so the expected value of the output of any node is the same as in the training stages. This is the biggest contribution of the dropout method: although it effectively generates $2^n$ neural nets, and as such allows for model combination, at test time only a single network needs to be tested. By avoiding training all nodes on all training data, dropout decreases overfitting in neural nets. The method also significantly improves the speed of training. This makes model combination practical, even for deep neural nets. ! [[Bayesian interpretation|Dropout as a Bayesian Approximation]]
Data: * question: $q$ * paragraph: $p$ ** with 300-dim Glove embedding, fine-tune 1000 most frequent ones ** 3 match indicators ** token features, POS, NER, TF ** alighed question embedding Architecture: * Retriever: find 5 wikipedia articles given any question * Reader: Similar to [[Attentive Reader]] ** encoder: 3-layer biLSTM
! cider-jack-in !! cider-mode |load current buffer|`C-c C-k`| ! Emacs |delete spaces between 2 words|`M-\`| |reload current file|`M-x` `load-file`| ! Paredit [[Paredit Reference Card|http://pub.gajendra.net/src/paredit-refcard.pdf]] |slurpage|`ctl`+`<left>`/`<right>`| |split quote|`M-s`| ! Vim [[ Vim Command Cheat Sheet|http://www.fprintf.net/vimCheatSheet.html]] |toggle case|`~`| |change 3 char|`c3l`| |global replace (confirmation)|`:%s/OLD/NEW/g(c)`| |open all folds|`zR`|
! Other Models * Speaker Model Synthesis * Feature Mapping The idea is different because this is speaker dependent. <<< The alignment indicates that the neutral and emotional utterances have the same content <<< p33 premilinary Does that mean it's text-dependent? * How to align Gaussian components * Emotional vector generation could be different for different person unless Gaussian components are utterances If the relationship between the utterances (emo/neutral) can be determined before the mapping learning process, then linear regression is likely enough for local translations. ! Approaches !! Neural Net The neural net uses a [[rbf|RBF Nerwork]] for activation function. !!! Questions * How to match gaussian componets * How many input samples are there * Performance relies on #samples * If the input is different speech samples and the output is one paticular speech sample, lots of text-based knowledge may affect the result !! Sparse Representation !!! Questions * How to get $D_e$
* [[paper|https://openreview.net/forum?id=rJxdQ3jeg¬eId=rJxdQ3jeg]]
Generalized divisive normalization. “which has previously been shown to be highly efficient in Gaussianizing the local joint statistics of natural images.” Gaussianizing here means mapping the data onto a normal distribution. The normalisation operation looks like this, where $w_{i}^{(k)}(m,n)$ is the output of the downsampling step,corresponding to spatial location $(m,n)$, and $\beta$, $\gamma$ are learned parameters.
The distortion measure is MSE. Since no reliable perceptual metric for colored image.
Questions
* What is adaptive entropy coder.
* This should be at least useful in generating images.
* Similar to VAE, but approximate the discrete problem along the rate-distortion curve. Study eq 10, the equivalence to VAE holds at least for any norm, and any affine and invertible perceptual transform.
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
! Perturbed SGD
From a series of work by Rong Ge:
* [[Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition|https://arxiv.org/abs/1503.02101]]: [[blog|http://www.offconvex.org/2016/03/22/saddlepoints/]]
* [[Gradient Descent Converges to Minimizers|https://arxiv.org/abs/1602.04915]]: [[blog|http://www.offconvex.org/2016/03/24/saddles-again/]]
* [[How to Escape Saddle Points Efficiently|https://arxiv.org/abs/1703.00887]]: [[blog|http://www.offconvex.org/2017/07/19/saddle-efficiency/]]
Pertubing Gradient Descent can escape saddle points if already there. Two simple methods have been studied:
* ''Intermittent perturbations'': [Ge et al. 2015] considered adding occasional random perturbations to GD, and were able to provide the first polynomial time guarantee for GD to escape saddle points.
* ''Random Initialization'': [Lee et al. 2016] showed that with only random initialization, GD provably avoids saddle points asymptotically.
<<<
''Perturbed gradient descent''
# ''for'' $t = 1, 2, \dots$ ''do''
# $\qquad x_t\rightarrow x_{t-1}-\eta\nabla f(x_{t-1})$
# $\qquad$ ''if'' perturbation condition holds ''then''
# $\qquad \qquad x_t\rightarrow x_t+\xi_t$
<<<
We make the following two assumptions regarding smoothness:
* Assumption 1: $f$ is $l$-gradient-Lipschitz, i.e. $\forall x_1, x_2, |\nabla f(x_1)-\nabla f(x_2)|\le l|x_1-x_2|$.
* Assumption 2: $f$ is $\rho$-Hessian-Lipschitz, i.e. $\forall x_1, x_2, |\nabla^2 f(x_1)-\nabla^2 f(x_2)|\le \rho|x_1-x_2|$.
A point $x$ is an $\epsilon$-first-order stationary point if $|\nabla f(x)|\le\epsilon$. Further more, if $\lambda_{\min}(\nabla^2f(x))\ge-\sqrt{\rho\epsilon}$, $x$ is a $\epsilon$-second-order stationary point. $\rho$ is the Hessian Lipschitz constant introduced above.
''Theorem'': If Assumption 1 holds, then GD, with $\eta = 1/l$, finds an $\epsilon$-first-order stationary point in $2l(f(x_0)-f^*)/\epsilon^2$ iterations.
For second-order stationary points, same property can be found:
''Main Theorem'': If Assumption 1 and 2 holds, then PGD, with $\eta = O(1/l)$, finds an $\epsilon$-second-order stationary point in $\tilde{O}(2l(f(x_0)-f^*)/\epsilon^2)$ iterations.
! Operator Analysis POV
[[On the Importance of Consistency in Training Deep Neural Networks|https://arxiv.org/abs/1708.00631]]claims that @@color:#859900;some layers are harder to train (esp. with GD), thus are the bottleneck of the whole training process. According to ''the law of minimum''@@.
Because gradient as an operator is contractive? The proposed SGD2 is just a perturbed SGD.
! Natasha 2
The objective of the EM algorithm is to maximize the likelihood of the observed data.
!Definition
!! Single-parameter
$$
f_X(x|\theta) = h(x) \exp \left (\eta(\theta) \cdot T(x) -A(\theta)\right )
$$
where $T(x)$, $h(x)$, $\eta(\theta)$, and $A(\theta)$ are known functions.
In the definitions above, the functions $T(x)$, $\eta(\theta)$ and $A(\eta)$ were apparently arbitrarily defined. However, these functions play a significant role in the resulting probability distribution.
* $T(x)$ is a sufficient statistic of the distribution. For exponential families, the sufficient statistic is a function of the data that fully summarizes the data $x$ within the density function. This means that, for any data sets $x$ and $y$, the density value is the same if $T(x) = T(y)$. This is true even if $x$ and $y$ are quite different—that is, $d(x,y)>0$. The dimension of $T(x)$ equals the number of parameters of $\theta$ and encompasses all of the information regarding the data related to the parameter $\theta$. The sufficient statistic of a set of independent identically distributed data observations is simply the sum of individual sufficient statistics, and encapsulates all the information needed to describe the posterior distribution of the parameters, given the data (and hence to derive any desired estimate of the parameters). This important property is further discussed below.
* $\eta$ is called the natural parameter. The set of values of $\eta$ for which the function $f_X(x;\theta)$ is finite is called the natural parameter space. It can be shown that the natural parameter space is always convex.
* $A(\eta)$ is called the log-partition function because it is the logarithm of a normalization factor, without which $f_X(x;\theta)$ would not be a probability distribution ("partition function" is often used in statistics as a synonym of "normalization factor"):
$$
A(\eta) = \ln\left ( \int_x h(x) \exp (\eta(\theta) \cdot T(x)) \operatorname{d}x \right )
$$
The function $A$ is important in its own right, because the mean, variance and other moments of the sufficient statistic $T(x)$ can be derived simply by differentiating $A(\eta)$. For example, because $\ln(x)$ is one of the components of the sufficient statistic of the gamma distribution, $\mathbb{E}[\ln x]$ can be easily determined for this distribution using $A(\eta)$. Technically, this is true because
$$
K(u|\eta) = A(\eta+u) - A(\eta),
$$
is the cumulant generating function of the sufficient statistic.
! Properties
Exponential families have a large number of properties that make them extremely useful for statistical analysis. In many cases, it can be shown that, except in a few exceptional cases, only exponential families have these properties. Examples:
* Exponential families have sufficient statistics that can summarize arbitrary amounts of independent identically distributed data using a fixed number of values.
* Exponential families have conjugate priors, an important property in Bayesian statistics.
* The posterior predictive distribution of an exponential-family random variable with a conjugate prior can always be written in closed form (provided that the normalizing factor of the exponential-family distribution can itself be written in closed form). Note that these distributions are often not themselves exponential families. Common examples of non-exponential families arising from exponential ones are the Student's t-distribution, beta-binomial distribution and Dirichlet-multinomial distribution.
* In the mean-field approximation in variational Bayes (used for approximating the posterior distribution in large Bayesian networks), the best approximating posterior distribution of an exponential-family node (a node is a random variable in the context of Bayesian networks) with a conjugate prior is in the same family as the node.
[[Geometry of exponential families]]
!! Conjugate Distributions
In the case of a likelihood which belongs to the exponential family there exits a conjugate prior, which is often also in the exponential family. A conjugate prior $\pi$ for the parameter $\eta$ of an exponential family
$$
f(x|\boldsymbol\eta) = h(x) \exp \left ( {\boldsymbol\eta}^{\rm T}\mathbf{T}(x) -A(\boldsymbol\eta)\right )
$$
is given by
$$
p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) = f(\boldsymbol\chi,\nu) \exp \left (\boldsymbol\eta^{\rm T} \boldsymbol\chi - \nu A(\boldsymbol\eta) \right ),
$$
or equivalently
$$
p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) = f(\boldsymbol\chi,\nu) g(\boldsymbol\eta)^\nu \exp \left (\boldsymbol\eta^{\rm T} \boldsymbol\chi \right ), \qquad \boldsymbol\chi \in \mathbb{R}^s
$$
where $s$ is the dimension of $\boldsymbol\eta$ and $\nu > 0$ and $\boldsymbol\chi$ are hyperparameters (parameters controlling parameters). $v$ correspinds to the effective number of observations that the prior distribution contributes, and $\boldsymbol\chi$ corresponds to the total amount that these pseudo-observations contribute to the sufficient statistic over all observations and pseudo-observations.
We can then compute the posterior as follows:
$$
\begin{align}
p(\boldsymbol\eta|\mathbf{X},\boldsymbol\chi,\nu)& \propto p(\mathbf{X}|\boldsymbol\eta) p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) \\
&= \left(\prod_{i=1}^n h(x_i) \right) g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T} \sum_{i=1}^n \mathbf{T}(x_i)\right)
f(\boldsymbol\chi,\nu) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \\
&\propto g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T}\sum_{i=1}^n \mathbf{T}(x_i)\right) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \\
&\propto g(\boldsymbol\eta)^{\nu + n} \exp\left(\boldsymbol\eta^{\rm T} \left(\boldsymbol\chi + \sum_{i=1}^n \mathbf{T}(x_i)\right)\right)
\end{align}
$$
! Examples
# normal
# exponential
# log-normal
# [[gamma|Gamma Distribution]]
# chi-squared
# beta
# Dirichlet
# Bernoulli
# categorical
# Poisson
# geometric
# inverse Gaussian
# von Mises-Fisher
! Applications !! Face Recognition # Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification, CVPR, 2014. [[link|https://www.cs.toronto.edu/~ranzato/publications/taigman_cvpr14.pdf]] # Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang, DeepID3: Face Recognition with Very Deep Neural Networks, 2015. [[link|http://arxiv.org/abs/1502.00873]] Florian Schroff, Dmitry Kalenichenko, James Philbin, FaceNet: A Unified Embedding for Face Recognition and Clustering, CVPR, 2015. [[link|http://arxiv.org/abs/1503.03832]] !! Facial Landmark Detection # Yue Wu, Tal Hassner, Kang Geon Kim, Gerard Medioni, Prem Natarajan, Facial Landmark Detection with Tweaked Convolutional Neural Networks, 2015. [[link|http://arxiv.org/abs/1511.04031]] [[Project|http://www.openu.ac.il/home/hassner/projects/tcnn_landmarks/]] !! Others # Spoofing 2D Face Detection: Machine See People Who Aren't There [[link|http://128.84.21.199/abs/1608.02128]] ! Bibs * [[An All-In-One Convolutional Neural Network for Face Analysis|https://arxiv.org/abs/1611.00851]] * Best up to now: [[Finding Tiny Faces]] ! Models * [[VGG-Face|http://www.robots.ox.ac.uk/~vgg/software/vgg_face/]] ! Datasets * [[CelebA|http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html]]
Factor analysis is the most common example of latent variable model, using a linear function as the mapping. $$ t = Wx + \mu + \epsilon $$ Conventionally, the latent variables are defined to be independent and Gaussian with unit variance, so $x\sim N(0, I)$. The noise model is also Gaussian such that $\epsilon\sim N(0, \Psi)$. As a result, $t\sim N(\mu, \Psi+WW^\top)$. The key motivation for this model is that, because of the diagnality of $\Psi$, the observed variables $t$ are conditionally independent given the latent variables, or factors, $x$. The intention is that the dependencies between the data variables $t$ are explained by a smaller number of latent variables $x$, while $\epsilon$ represents variance unique to each observation variable. This is in contrast to conventional PCA, which effectively treats both variance and covariance identically. There is no closed-form analytic solution for $W$ and $\Psi$, and so their values must be determined by iterative procedures.
[[Billion-scale similarity search with GPUs|https://arxiv.org/abs/1702.08734]] * near-optimal exact and approximate GPU k-selection
[[code|https://github.com/obachem/kmc2]] Random seeding can lead to arbitrarily bad clustering result. K-means++ is slow. A Markov chain sampling process with the stationary distribution exactly the same as k-means++ is proposed. The result is provably good without any assumptions of the data. The performance is of up to 1,064x speedup and 1.32% relative error comparing to k-means++
[[ArXiv|http://arxiv.org/abs/1504.08083]]
This is an exciting model for detection allowing fine-tuning from previous networks.
! Training
!! Multi-task loss
$$
L(p, u, t^u, v) = L_{cls}(p, u)+\lambda[u\ge1]L_{loc}(t^u, v)
$$
where $L_{cls}(p, u)=-\log p_u$ and
$$
L_{loc}(t^u, v) = \sum_{i\in\{x, y, w, h\}}\text{smooth}_{L_1}(t_i^u-v_i),
$$
where
$$
\text{smooth}_{L_1} = \left\{\begin{array}{rr}
0.5x^2 & |x| < 1\\
|x|-0.5 & \text{otherwise,}
\end{array}\right.
$$
!! Mini-batch sampling
2 images, 64 RoIs each. 25% of them have at least intersection over union (IoU) with gt, others have $[0.1, 0.5)$.
! Region Pooling SPP-Net
unify the conv feature map dimensions by convolutions.
Fast-RCNN use ROI pooling to unify the dimensions and replaces SVM with softmax. Use bbox regressor to refine the regions.
[[link|http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks]]
! Implementations
* [[Tensorflow|https://github.com/endernewton/tf-faster-rcnn]]
! Anchors
! Loss function
$$
L(\{p_i\}, \{t_i\}) = \frac {1} {N_{cls}} \sum_iL_{cls}(p_i, p_i^*) + \lambda \frac {1} {N_{reg}} \sum_ip_i^*L_{reg}(t_i, t_i^*)
$$
where $p_i^*$ is the probability of this anchor being an object. $L_{cls}$ is the log loss over 2 classes. $L_{reg}(t_i, t_i^*) = R(t_i-t_I^*)$ is the robust loss function (smooth $L_1$).
! Optimization
randomly sample 256 anchors to compute the loss in a mini-batch to keep positive and negative sample ratio to 1:1. However, if there is not enough positive samples, negative samples are padded.
[[RNN for ASR|http://research.microsoft.com/pubs/164627/4085.pdf]]: Using features other than MFCCs has long been a focus of research. A common technique to merge streams is to use a Tandem method, in which sun How to generate good spectro-temporal modulation features? !! Deep Learning !! wu10robust It is used to deal with noisy environment. Constrained nonnegative tensor factorization (cNTF) using common tensor decomposition methods such as CANCECOMP/PARAFAC model with nonnegative constraints.
* Unpacking all files * make * make test
[[f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization|https://arxiv.org/abs/1606.00709]]
* generalize GAN objective to arbitrary $f$-divergences
* simplify GAN algorithm + local convergence proof
* demonstrate different divergences
! Objective
Divergence between two distributions
$$
D_f(P\|Q) = \int_{\mathcal X}q(x)f\left(\frac{p(x)}{q(x)}\right)dx
$$
$f$ is convex and $f(1) = 0$.
<<<
Any convex $f$ can be represented as point-wise max of linear functions
<<<
Every convex function $f$ has a convex Fenchel conjugate $f^*$ so that
$$
f(u) = \underset{t\in\text{dom}_{f^*}}{\sup}\{tu-f^*(t)\}
$$
$$
\begin{align}
D_f(P\|Q) &= \int_Xq(x)f\left(\frac{p(x)}{q(x)}\right)dx \\
&= \int_Xq(x)\underset{t_x\in\text{dom}_{f^*}}{\sup}\left\{t_x\frac{p(x)}{q(x)}-f^*(t_x)\right\}dx \\
&\ge \underset{T\in\mathcal T}{\sup}\left(\int_Xp(x)T(x)dx-\int_Xq(x)f^*(T(x))dx\right) \\
&= \underset{T\in\mathcal T}{\sup}\left(\mathbb E_{x\sim P}[T(x)]-\mathbb E_{x\sim Q}[f^*(T(x))]\right)
\end{align}
$$
$\mathcal T$ is a function family e.g. some neural networks. We can approximate $f$-divergence from sampling from $Q$ and $P$.
This has a structural similarity with GAN.
* The caveat here is $f^*$ only exist in some cases.
* Only need to change some LOC to change any GAN implementation into this.
! Algorithm
Original GAN uses a doule-loop algorithm.
* Inner loop: tighten divergence lower bound
* Outer loop: minimize generator loss
In practice, the inner loop is run for one step
fGAN proposed single-step algorithm. Which simultaneously take
* a positive gradient w.r.t. variational function $T_\omega(x)$
* a negative gradient w.r.t. generator function $P_\theta(x)$
! Convergence
The ''local'' convergence theorem around a saddle point. Assume:
* $F$ is locally (strongly) convex w.r.t. $\theta$
* $F$ is (strongly) concave w.r.t. $\omega$
that is,
$$
\nabla^2F = \left[\begin{array}{cc}
\nabla_\theta^2F & \nabla_\theta\nabla_\omega F\\
\nabla_\omega\nabla_\theta F& \nabla_\theta^2F
\end{array}\right], \nabla_\theta^2F\succ 0, \nabla_\omega^2F\prec 0
$$
The algorithm has geometric rate of convergence. Define $J(\theta, \omega) = \frac 1 2\|\nabla_\theta F\|^2 + \frac 1 2\|\nabla_\omega F\|^2$, then
$$
J(\theta^t, \omega^t)\le\left(1-\frac{\delta^2}{L}\right)^tJ(\theta^0, \omega^0)
$$
where $\delta$ is strong convexity parameter and $L$ is smoothness parameter.
! Remarks
The divergence is so noisy that I wonder if this is useful.
A filter bank is an array of [[band-pass filters|Band-pass Filter]] that separates the input signal into multiple componets, each one carrying single frequency sub-band of the original signal.
* [[link|https://arxiv.org/abs/1612.04402]] * [[demo|https://github.com/peiyunh/tiny]] Improvements * multi-task modeling of scales: trained detectors from multiple layer features (hypercolumn features). Help with large objects not small ones. Foveal descriptors. * process image pyramid to capture large scale variations * encode context: high-resolution components are crucial * change resolution: to find large objects, use 2x smaller canonical resolution, to find small objects, use 2x larger canonical resolution.
Fisher information metric is the only Riemannian metric that "makes sense" for [[statistical manifolds|Statistical manifold]]:
$$
I(\theta) = \left[\int\frac{\partial\log p(x;\theta)}{\partial\theta_i}\frac{\partial\log p(x;\theta)}{\partial\theta_j}p(x;\theta)dx\right]=[g_{ij}]
$$
The [[Infinitesimal length element]] is given by
$$
ds^2 = \sum_{i=1}^d\sum_{j=1}^dd\theta_i^T\nabla^2F(\theta)d\theta_j
$$
Cencov proved that for a non-singular transformation of the parameters $\lambda=f(\theta)$, the information matrix
$$
I(\lambda)=\left[\frac{\partial\theta_i}{\partial\lambda_j}\right]I(\theta)\left[\frac{\partial\theta_i}{\partial\lambda_j}\right].
$$
Normalized and de-correlated activation is well known for improving the conditioning of the Fisher information matrix.
! Appearances
In reinforcement learning, the natural gradient is defined as $F^{-1}g$, where $F$ is the average Fisher information matrix
$$
F = \mathbb E_{s, a\sim\pi}[(\nabla_\theta\log\pi_\theta(a|s))^T(\nabla_\theta\log\pi_\theta(a|s))]
$$
from [[Optimization for Machine Learning I|https://simons.berkeley.edu/talks/elad-hazan-01-23-2017-1]]
This is a generalized form of Gradient descent, like a best-in-hindsight plus regularization.
We want to minimize the regret
$$
\sum_tf_t(x_t) - \underset{x^*\in K}{\min}\sum_tf_t(x^*)
$$
over time. The most natural way of picking $x$ is choosing the best to our knowledge:
$$
x_t = \arg\underset{x\in K}{\min}\sum_{i=1}^{t-1}f_i(x)
$$
But this result is not stable. We consider linear case, where we replace $f_t$ by $\nabla f_t(x_t)^\top x$, we add regularization:
$$
x_t = \arg\underset{x\in K}{\min}\sum_{i=1}^{t-1}\nabla_t^\top x+\frac 1 \eta R(x)
$$
if $R(x)$ is strongly convex, we ensure stability:
$$
\nabla_t^\top(x_t - x_{t+1}) = O(\eta)
$$
! Choosign $R$
!! Euclidean case
$$
\begin{align}
x_t &= \arg\underset{x\in K}{\min}\sum_{i=1}^{t-1}\nabla_t^\top x+\frac 1 \eta \frac 1 2 \|x\|^2\\
&= \prod_K(-\eta\sum_{i=1}^{t-1}\nabla_t)
\end{align}
$$
This is exactly gradient descent.
!! Multiplicative Weights
$$
\begin{align}
x_t &= \arg\underset{x\in K}{\min}\sum_{i=1}^{t-1}c_i^\top x+\frac 1 \eta \sum_ix_i\log x_i\\
&= \exp(-\eta\sum_{i=1}^{t-1}c_i)/Z_t
\end{align}
$$
!! Online Mirror Descent
! Adaptive Regularization: AdaGrad
OGD update: $x_{t+1} = x_t - \eta l(a_t, b_t, x)a_t$. The feature $a_t$ is always sparse the learning is slow.
AdaGrad treats the regularization factor as a learning problem:
$$
R(x) = |x|^2_A \qquad s.t.\qquad A\succcurlyeq0, Trace(A)=d
$$
The regret bound can be $\sqrt{d}$ better than SGD.
! Definition
The Fourier transform of a function $x\in L^2(\mathbb R)$ is defined as
$$
\hat x(\omega)=\int x(u)e^{-iwu}du
$$
! Properties
* Linear: $z=\alpha x+\beta y\Rightarrow\hat z=\alpha\hat x+\beta y$.
* Parseval identity: $\|\hat x\|=\|x\|, \langle x, y\rangle=\langle\hat x, \hat y\rangle$.
* Inverse Fourier transform: $x(u) = \int\hat x(\omega)e^{-iwu}dw$.
* Translation: $y(u) = x(u-u_0)\Rightarrow\hat y(\omega)=e^{iwu_0}\hat x(\omega)$.
* Dilation: $y(u)=x(su)$ for $s>0\Rightarrow\hat y(\omega)=s^{-1}\hat x(s^{-1}\omega)$.
[[Fourier Transform vs Wavelet Transform]]
Short time Fourier transform (STFT) suffers from resolution problem. We have equal time and frequency resolution at all frequencies in STFT. Fourier modulus is unstable: high-frequency information spans a large linear subspace as soon as there is non-rigid deformation. So we want to smooth along the orbits.
! Bibs * An OpenCL Deep learning on A10 [[Neural Networks on Silicon|https://github.com/fengbintu/Neural-Networks-on-Silicon]] ! Convolution divided into 6x8 blocks * Winograd computation within one layer is done with local cache, no DDR communication (for 224x224 input)
Terrence talking about [[Free Probability|https://terrytao.wordpress.com/2010/02/10/245a-notes-5-free-probability/#more-3466]].
Speicher: solutions to noncommutative functions
* Tensor product: $\varphi_{xy}(X_1Y_1\dots X_nY_n) = \varphi_x(X_1\dots X-n)\varphi_y(Y_1\dots Y_n)$
* Free product: Anytime $\varphi(X_i)=\varphi(Y_i)=0$ $\forall i$, $\varphi(X_1Y_1\dots X_nY_n) = 0$
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
[[code|https://github.com/bertinetto/siamese-fc]]
! Siamese architecture
Given an exampler image $z$ to a candidate image $x$, siamese networks apply an identical transformation $\phi$ to both inputs and then combine their representations using another function $g$ according to $f(z, x) = g(\phi(z), \phi(x))$.
We say that a function is fully-convolutional if it commutes with translation. We can provide a larger search image and convolve the features to compute the similarity:
$$
f(z, x) = \phi(z)\star\phi(x)+b
$$
! Training
On positive and negative pairs and adotping the logistic loss
$$
l(y, v) = \log(1+\exp(-yv))
$$
where $v$ is the real-valued score and $y\in\{+1, -1\}$ is its ground truth label.
|! |!Discrete |!Continuous | |!Directed |<li>NADE</li><li>EoNADE</li><li>Fully-visible sigmoid belief networks</li><li>[[Pixel-CNN]]</li><li>[[WaveNet]]</li><li>RNN Language Model</li><li>Context tree switching</li> |<li>Normal Means</li><li>Coninuous Markov Models</li><li>N-AR(p)</li><li>RNADE</li> | |!Undirected |<li>[[Boltzmann Machines|Restricted Boltzmann Machines]]</li><li>Discrete Markov Random Fields</li><li>Ising, Hopfield and Potts Models</li> |<li>Gaussian MRFs</li><li>Log-linear models</li> | @@color:#859900; * Can directly encode how observed points are related * Any data type can be used * For directied graphical models: parameter learning simple * Log-likelihood is directly computable, no approximation needed * Easy to scale-up, many optimisation tools @@ @@color:red; * Order sensitive * For undirected models, parameter learning difficult: need to compute normalising constants (like RBM) * Generation can be slow: iterate through elements sequentially, or using a Markov chain @@ ! Bib * [[Recursive Cortical Network]]
* Batch normalization * Historical averaging (Salimans et al 2016): A reinvention of Polyak averaging in RL * Minibatch discrimination (Salimans et al 2016): Like 2 sample test, try classify dataset * Instance noise (Sonderby et al 2016) * Weight clipping or approximate Wasserstein loss (Arjovsky et al 2017) * [[Unrolling SGD (Metz et al 2017)|https://github.com/poolio/unrolled_gan]]
[[reddit discussion|https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/]] Both use backprop through continuous random number generation * VAE: ** generator gets direct output target ** need Reinforce to do discrete latent variables ** possible underfitting due to variational approximation ** gets global image composition right but blurs details * GAN ** generator never sees the data ** need Reinforce to do discrete visible variables ** possible underfitting due to non-convergence ** gets local image features right but not global structure GAN can train any differentiable generative network by avoiding the maximum likelihood principle altogether. The metrics that measure the diversity in the generated samples are currently intractable.
Looking forward to off the convex path's new post and paper on GAN.
The generator tries to maximize $E_u[f(D(G(h)))]$. The original GAN uses $f(x)=\log(x)$, making the training more sentitive to terrible generations. If generator wins, $P_{real}$ and $P_{synth}$ are close in JS divergence. While [[WGAN]] uses $f(x)=x$, the two distributions are close in Wasserstein or earth-mover distance. In practice, the analysis can be very off.
[[Deduction|http://andrew.gibiansky.com/blog/machine-learning/gauss-newton-matrix/]]
The usual view of the Gauss-Newton matrix is that it is simply a positive semi-definite approximation to the Hessian.
Martens 2010 found that using the Gaussian-Newton matrix instead of $H$ was highly preferable because:
# $H$ is general indefinite, the sub-objective $q_{\theta_n}$ may not be bounded below. Infinite reduction will be possible along any directions of negative curvature that have a non-zero inner product with the gradient.
# The parameter updates produced by using $G_f$ performed much better in practice of neural network learning.
Importance of the Gaussian # The Gaussian density is the only density for real random variables that is "closed" under marginalization and ''multiplication''. # A linear (or affine) function of a Gaussian random variable is Gaussian; and, a sum of Gaussian variables is Gaussian. # Some graphical model algorithms will be tracable only for finite random variables or Gaussian random variables. # Non-Gaussian variables will be approximated using discrete and Gaussian tools
[[Renewal Processes]]
* C=AB can be parameterized by 3 matrix dimensions: MxN = MxK * KxN. * Floating point ops = MN(2K-1)~2MNK (K mult, K-1 sum) * Bytes through memory = sizeof(dataType) * (MK + KN + MN)
2nd part of [[Theory and Algorithms for Forecasting Non-Stationary Time Series]] ! Previous work With assumption of stationarity and $\beta$-mixing: * PAC-learning preserved in that setting (Vidyasagar, 1997), argues $\beta$-mixing is nice. * VC-dimension bounds for binary classification (Yu, 1994) * covering number bounds for regression (Meir, 2000) * Rademacher complexity bounds for general loss functions (MM and Rostamizadeh, 2000) * PAC-Bayesian bounds (Alquier et al., 2014) And there are algorithm dependent studies. Mixing assumptions are hard to verify. Even if we know the exact form of mixing, estimating the parameters is still a very difficult problem. The hypothesis and loss function sets are not representative enough. ! The bound We would like more realistic assumptions. We use weighted discrepancy guided by real numbers $\mathbf q$, this sequence emphasis particular set of samples. The choice of hypothesis set and $q$ are crucial. We have to use a sequential version of coverting number: [[Weighted sequential $\alpha$ cover]] ! Algorithms * Linear hypothesis
! Theory
The GAN framework consists of two players - the //generator// $G_\phi(z)$ whose aim is to create realistic samples and the //discriminator// $D_\theta(x)$ whose aim is to classify an instance as having come from training data or the generator. Their cost functions are defined as:
$$J^{(D)}(\phi, \theta) = -\frac12\mathbb E_{x\sim p_{real}}\log D_\theta(x)-\frac12\mathbb E_z\log(1-D_\theta(G_\phi(z)))$$
$$J^{(G)}(\phi, \theta) = \frac12\mathbb E_z\log(1-D_\theta(G_\phi(z)))$$
The complete game can be specified as:
$$\underset{\phi}{\min}\underset{\theta}{\max} g(\phi, \theta) = \mathbb E_{x\sim p_{real}}\log D_\theta(x) + \mathbb E_z\log(1-D_\theta(G_\phi(z)))$$
At equilibrium, JS divergence between the data and model distributions is minimized meaning G has converged to $P_{real}$ and D outputs 1/2 for all x.
If we plug in the optimal discrinimator
$$
D^* = \frac{p^*(x)}{p^*(x)+q_{\phi}(x)}
$$
The loss $\mathcal L(\theta^*, \phi) = 2\mathrm{JS}(p^*\|q_\phi)-\log(4)$.
!! Understanding GAN
* Ian's [[Generative Adversarial Network Talk]]
* [[OpenAI's survey|https://openai.com/blog/generative-models/]]
* [[What Arora thinks about GANs|GANs, Some Open Questions]]
* [[How to train a GAN]]
* [[The Numerics of GANs]]
!! Research Directions
* [[GAN Stablizing Strategies]]
* Generate HQ images. Image is a good field for generative models, because we are sensible to image plausibility.
** Higher resolution images can be generated in a multiscale fashion.
* Address GAN training difficulties
** [[Amortised MAP Inference for Image Super-resolution|https://arxiv.org/abs/1610.04490]]
** [[Towards Principled Methods for Training Generative Adversarial Networks|https://arxiv.org/abs/1701.04862]]
** When training GAN, we collpase distribution dimensionalities, but when the 2 distributions does not intersect, we get problem training. So we convolve the distribution and later recover the full distribution.
* Leverage Kullback-Leibler divergence
** Do maximum-likelihood estimation in a likelihood free setting. Similar to approximate Bayesian computation.
** The reverse of KL divergence, use GAN in Variational inference.
* More applications
** Text (large discrete ouput spaces)
** Reinforcement learning.
* Reducing variance?
** Many networks like GAN are stochastic, depends on a non-stationary sampling
** Look into RL literatures where value distribution depends on policy.
! Bibs
!! Extensions
* [[Unsupervised representation learning with deep convolutional generative adversarial networks]]
** Laplacian Pyramid of GANs is further suggested to generate high quality image in a coarse-to-fine manner.
* [[DCGAN|Unsupervised representation learning with deep convolutional generative adversarial networks]]: a stable family of architecture in training.
* [[SeqGAN|http://arxiv.org/abs/1609.05473]]
* [[Stacked GAN|https://arxiv.org/abs/1612.04357]]
* [[Super-Resolution GAN|https://arxiv.org/abs/1609.04802]]
* [[InfoGAN]]
* LS-GAN enforces $D_\theta(G_\phi(z)) - D_\theta(x)\ge\delta(x, G_\phi(z))$ and @@color:#859900;impose a Lipschitz constraint on both G and D using weight decay@@. This leads to D having non-vanishing gradients everywhere between all real and fake sample pairs.
* [[WGAN]] uses Earth-Mover distance as the objective. This requires the discriminator to be a Lipschitz function and they use weight clipping to achieve that.
* [[Improved WGAN|https://arxiv.org/abs/1704.00028]] imposes $\|\nabla_{\hat x}D_{\theta}(\hat x)\|_2\approx 1$ where $\hat x = \epsilon x+(1-\epsilon)G_\phi(z)$. This leads to D having norm 1 gradients everywhere between all real and fake sample pairs in the limit.
* [[CycleGAN and pix2pix|https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix]]: pretty good image effects.
* [[Progressive Growing of GANs for Improved Quality, Stability, and Variation|http://research.nvidia.com/publication/2017-10_Progressive-Growing-of]]
** Good results
!! Theory
* [[Divergence Classed GANs]]
* [[Density Ratio GANs]]
!! Applications
* [[Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling|https://arxiv.org/abs/1610.07584]]
* [[Semi-Supervised Learning with Generative Adversarial Networks|http://arxiv.org/abs/1606.01583]]
** Better images than DCGAN are generated adding the classification label
* [[Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network]]
* [[Learning to Pivot with Adversarial Networks|https://arxiv.org/abs/1611.01046]]
** joint training adversarial networks to constrain the predictive model to be robust on nuisance parameter.
!! Talks
* [[How to train a GAN]]
* [[DALI 2017 GAN Workshop]]
GANs are recent approaches to modelling data in high dimensional spaces. Following are notes from [[Ian Goodfellow's ICML DL Workshop 2015 talk|https://www.youtube.com/watch?v=LXDuuYSNtUY]].
Papers relative in this post:
* Generative Adversarial Networks
* Conditional Generative Adversarial Nets
* On Distinguiashability Criteria for Estimating Generative Models
* Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
! Generative adversarial networks
The general idea of GAN is to learn a sampling mechanism. We will learn to generate some input noise from a fixed prior distribution and then transform that noise into data space.
* The advantage over generative stochastic network is there is no Markov chain involved so no issue of mixing.
* The advantage over [[VAE|Variational Autoencoder]] is that there is no variational lower bound.
!! Game theory: the basics
GANs are trained by //playing a game//. In game theory we consider the following senario:
* N>1 players
* Clearly defined set of actions each player can take
* Clearly defined relationship between actions and outcomes
* Clearly defined value of each outcome
* Can't control the other player's actions, which is different from maximization.
Each player tries to maximize his reward during the game. While simulating the game, we are looking for an equilibrium form, where no player can improve his reward by changing their own strategies.
We are going to formulate the simplest GAN as a two-player zero-sum game, where:
* Your winnings + your opponent's winnings = 0
* ''Minimax theorems'': a rational strategy exists for all such finite games
* Strategy: specification of which moves you make in which circumstances.
* Equilibrium: each player's strategy is the best possible for their opponent's strategy.
This setup is simple because players don't have to think about cooperating with other players. In GAN, we are using a continuous game, but there is a special case where minimax does apply. When players take action randomly it is called ''mixed stategy'' and the equilibrium is called ''mixied strategy equilibrium''.
!! Adversarial nets game
The game is between two players: ''Discriminator'' $D$, and ''Generator'' $G$. The generator is the same thing as the decoder. The discriminater tries to discriminate between:
* A sample from the data distribution
* And a sample from the generator $G$
While $G$ tries to "trick" $D$ by generating samples that are hard for $D$ to distinguish from data. This is a game theory model, we try to do gradient descent on one side and ascent on the other.
The value function takes the form of a binary discrimination task:
$$
\underset{G}{\min}\underset{D}{\max} V(D, G) = E_{x\sim p_{data}}(x)[\log D(x)]+E_{z\sim p_z(x)}[\log(1-D(G(z)))]
$$
Together this forms a mathematical saddle-point problem.
At every step, if the discriminator is optimal, it is going to take on this particular form, the optimal strategy for any $p_{model}(x)$ is always
$$
D(x) = \frac{p_{data}(x)}{p_{data}(x)+p_{model}(x)}
$$
!!! Noise contaction estimation
This is the same function used in noise contraction estimation. The different is that NCE is learning a model that used implicitly to define the discriminator, and here we're learning the generator. And in NCE, the generator is fixed. This function has a close connection to likelihood.
MLE is NCE modified such that the discriminators believes the data distribution are copied into the generator network at every step. NCE is computationally cheaper because it does not update the generator. And because of this, it is failing to obtain the same asymptotic error rate as MLE.
!! Theoretical properties
This process has some nice theoretical properties, if we assume we can optimize directly over probability distributions, then there is one unique global optimum, this saddle point corresponds to the true distribution. Assuming infinite data, infinite model capacity, direct updating of generator's distribution, converge to optimum guaranteed. But in practice, there is no proof that SGD will converge, where we are training gradient steps by opposite sign on the parameters of each model.
This is different from the usually analyzed statistical estimator, where we have an optimization problem where we minimize a function, and we show that in function space, the minimum of our objective function corresponds to recovering the true distribution. In this case we are not guaranteed to reach a critical point, that makes it more problematic when it comes to nonconvex optimization. In particular, we can have a network that oscilates forever.
! [[GANs vs VAEs]]
! Open problems
* Is non-convergence a serious problem in practice?
* If so, how can we prevent non-convergence?
* Is there a better loss function for generator?
! [[Learning in Implicit Generative Models|https://arxiv.org/abs/1610.03483]]
The objective is to make the density ratio funtion $r(x) = p^*(x)/q(x)$ to be one, where $p*(x)$ is the true data distribution and $q(x)$ is the model distribution. By estimating this value, we can avoid computing the marginal likelihood. There are four ways of achieving this objective:
!! Class probability estimation
As in the GANs, we specify a discriminator, $\mathcal D(x;\phi) = p(y|x)$, and a scoring rule.
!! Statistical distance minimization
!! Ratio matching
!! Moment matching
For an [[exponential family|Exponential Family]], the [[Kullback-Leibler divergence]] is a [[Bregman divergence]] on the natural parameters. Using Taylor approximation with exact remainder, we get $KL(\theta||\theta+d\theta)=\frac{1}{2}d\theta^T\nabla^2F(\theta)d\theta$. Where $F(\theta)$ is the log-normalizer. The [[Fisher information|Fisher information metric]] is the Hessian of the log-normalizer:
$$
I(\theta) = \left[\frac{\partial\eta}{\partial\theta}\right]=\nabla^2F(\theta) = I^{-1}(\eta) = \left[\frac{\partial\theta}{\partial\eta}\right]
$$
To a given Riemannian manifold $\mathcal M$ with tensor $G$, we may associate a probability measure as follows: Let $p(\theta)=\frac{1}{V}\sqrt{\det G(\theta)}$ with overall volume $V=\int_{\theta\in\Theta}\sqrt{G(\theta)}d\theta$. These distributions are built from infinitesimal volume element and bear the names of Jeffreys priors.
In an exponential family manifold, teh geometry is flat and $\theta/eta$ are dual coordinate systems.
Gibbs sampling is one of the most popular MCMC techniques. It takes an unnormalized distribution which can be specified by a factor graph, along with a list of variables values that can be arbitrarily initialized. Gibbs sampling iteratively selects a variable and then samples the chosen variable from its conditional distribution. ! Scan order in Gibbs sampling The scan order in Gibbs sampling determines which variable is selected in each step: # random scan # systematic scan selects a fixed permutation and repeatedly samples the variables in that order In [[Scan Order in Gibbs Sampling: Models in Which it Matters and Bounds on How Much|https://arxiv.org/abs/1606.03432]], a theorem bounding the relative mixing times of random and systematic scan is proved. Under mild conditions, this theorem implies that the mixing times differ by only polynomial factors.
! Image
[[Faster R-CNN Features for Instance Search|http://gitxiv.com/posts/WKiQ5mLBat7qZQozX/faster-r-cnn-features-for-instance-search]]
!! Captioning
[[densecap|https://github.com/jcjohnson/densecap]]
!! Applications
[[Age & Gender Classification|http://gitxiv.com/posts/sKTDSvwzoiRaojLG6/age-and-gender-classification-using-convolutional-neural]]
!! Visualization
!!! Weakly localization
[[Learning Deep Features for Discriminative Localization]]
[[ccnn|https://github.com/pathak22/ccnn]]
[[wsd|https://github.com/marcopede/wsd]]
! NLP
!! Classification
[[Convolutional Neural Networks for Sentence Classification|http://gitxiv.com/posts/2SAx7ccSYYDvNXq8Y/convolutional-neural-networks-for-sentence-classification]]
!! Application
[[A Convolutional Attention Network for Extreme Summarization of Source Code|https://github.com/mast-group/convolutional-attention]]
$\mathbin{\rotatebox[origin=c]{180}{\amalg}}$
They are consonants
The GLR is a likelihood-based metric that proposes a ratio between two hypotheses: on one hand $H_0$ considers that both segments are uttered by the same speaker, therefore $X=X_i\cup X_j\sim M(\mu,\sigma)$ represents better the data. H_1 considers that each segment has been uttered by a different speaker, therefore $X_i\sim M_i(\mu_i,\sigma_i)$ and $X_j\sim M_j(\mu_j,\sigma_j)$ together suit better the data. The ratio test is computed as a likelihood ratio between the two hypotheses as
$$
GLR(i,j)=\frac{H_0}{H_1}=\frac{\mathcal L(X, M(\mu,\sigma))}{\mathcal L(X_i, M(\mu_i,\sigma_i))\mathcal L(X_j, M(\mu_j,\sigma_j))}
$$
and determining the distance as $D(i,j)=-\log(GLR(i,j))$ which, upon using an appropriate threshold one can decide whether both segments belong to the same speaker or otherwise.
The advantage of GMM-based technique is that it enables the continuous mapping of acoustic features between speakers on the basis of ''soft clustered conversion functions''.
GMM-based statistical mapping methods have improved the performance of VC to a great extent. Sylianou et al proposed a conversion method with Gaussian mixure that realizes continuous mapping based on soft clustering as follows:
$$
\hat y_t = \sum_{m=1}^Mw_{m,t}^{(x)}(A_mx_t+b_m).
$$
GMM models the densities of source cepstral vectors or joint cepstral vectors, but the map is frame-to frame thus cannot take account of the contextual information.
! JDGMM
GMM is employed as the generative model to describe the distribution of the joint spectral feature space. The distribution of the GMM is defined as
$$
P(v_t) = \sum_{m=1}^M\alpha_mN(v_t;\mu_m^{(v)}, \Sigma_m^{(v)}), \sum_{m=1}^M\alpha_m=1,
$$
The covariancea and cross-diagonal covs are diagonal, these matrices are suitable for modeling the mel-cepstra with weak inter-dimensional correlations.
At the conversion stage, the conditional distribution is
$$
P(y_t|x_t,\lambda^{(v)}) = \sum_{m=1}^M\beta_{m, t}N(y_t;\mu_{m, t}^{(y|x)},\Sigma_m^{(y|x)}),
$$
!!! Problems with GMM
GMM-based models suffer from overfitting and oversmoothing.
* overfitting: using relatively small dataset but overly complex model very likely leads to overfitting.
* oversmoothing: the converted spectra tend to be very smooth as a result of the averaging nature of GMM. Some details in the spectra are lost.
* piecewise linear
* local discontinuity [Toda et al. 2007]
* Source feature clustering because of low inter-speaker covariance. Which can make the sound muffled due to lower variance in converted voice.
* delta and delta2 are not good contextual (dynamic) information.
To build the model, parallel utterances are required for training. Recording of the two speakers are first aligned with DTW (or dynamic programming) and then an F0 transformation is used for the excitation features.
! Output Six softmax outputs, one determines the number of digits, the others specify each one of them. ! Experiments !! SVHN !!! Data preprocessing # find rectangle bounding of all digits # expand the bounding box by 30% # resize to 64x64 # crop 54x54 from it !!! Model architecture 8 CONV5x5, 1 locally connected layer (maxout), 2 FCs. !! Internal No ground truth bounding boxes !!! Data preprocessing # find door centroid # crop 128x128 !!! Model architecture No dropout, 5 CONV5x5 !! CAPTCHA !!! Model architecture No dropout, 9 CONV5x5
33,000 utterance, the topologies are similar to HTS (2005). Speech data are downsampled from 48 kHz to 16kHz. 40 melceps, logarithmic fundamental frequency (log $F_0$) values, and 5-band aperiodicities (0-1, 1-2, 2-4, 4-6, 6-8 kHz) were extracted every 5 ms. The feature is consisted of these and their delta, delta-delta feaures ($3\times(40+1+5)=138$). Five-state, left=to-right, no-skip hidden semi-Markov models (HSMMs) were used. A multi-space probability distribution was used to model log $F_0$ sequences consisting of voiced and unvoiced observations. 2554 questions are used for the decision tree-based context clustering. The input of DNN included 342 binary features for categorical linguistic contexts (phonemes indentities) and 25 numerical features (the number of syllables in a word, position of the current syllable in phrase), 3 numerical features for ''coarse-coded'' position of the current phoneme and 1 numerical feature for duration of the current segment. The output features are same aas HMM-based systems. To model $\log F_0$ sequences by a DNN, the continuous $F_0$ with explicit voicing modeling approach was used; voiced/unvoiced binary value was added to the output features.
[[In-Datacenter Performance Analysis of a Tensor Processing Unit|https://dl.acm.org/citation.cfm?id=3080246]] * The Matrix Unit: 256x256 8-bit multiply-accumulate units * 700MHz clock rate * Peak: 92T ops/s ** 65,536 * 2 * 700M * > 25x as many MACs vs GPU * > 100x as many MACs vs CPU * 4MB of on-chip Accumulator memory * 24MB of on-chip Unified Buffer (activation memory) * 3.5x as much on-chip memory vs GPU * Two 2133 MHz DDR3 DRAM channels * 8 GB of off-chip weight DRAM memory
[[link|https://arxiv.org/abs/1409.4842]] GoogLeNet uses 12$\times$ fewer parameter than most current deep architectures while achieves the best performance. ! Basic idea !! Inception module <<< Neurons that fire together, wire together. <<< Hebbian principle [[Arora et al.|http://arxiv.org/abs/1310.6343]] states that if the distribution of the data-set is representatable by a large, very sparse deep network, then the optimal network topology can be constructed layer by layer by analyzing the correlation statistics of the activations of the last layer and clustering neurons with highly correlated outputs. (A personal interpretation would be as long as the net is sparse enough, the depth does not matter.) In image, correlations tend to be local. The very local clusters can be covered by 1x1 CONVs, and less local ones by 3x3 and 5x5 ones. This leads to the naive version of Inception: [img height=250 [naive_inception.png]] The pooling path is an adoptation of current deep learning interest which is believed to be beneficial. However, the output number of POOL is the same as the total output of the previous Inception module. And CONV 5x5 are expensive when the number of filters are large. The final design deals with this filter explosion and looks like this: [img height=250 [inception.png]] Notice that pooling results (192=>32 in `inception_3a`) and CONV 5x5 inputs (16) are all sharply reduced. All of the CONVs and POOLs are ReLUed. !! Auxiliary classifiers There are 2 additional classifiers connected to the intermediate layers where discriminative features may already been generated. So that the gradient signal propagated back can get increased and additional regularization is provided. ! Training Asynchronous stochastic gradient descent with 0.9 momentum and stepwise learning rate (decreasing by 4% every 8 epochs). Polyak averaging was used to create the final model used at inference time. Several interpolation methods (bilinear, area, nearest neighbor and cubic) are used for resize, since it is quite random, no further practices can be assured to be helpful. ! [[General Design Principles|Inception Evolutions]]
A kernel essentially means: * CPU launches the kernel to the GPU with some small overhead * For each pointwise element, launch one thread * This thread reads the value it is responsible for, does a simple operation and writes its result
Let's say that we have a cost or error function $E(w)$ that we want to minimize. Gradient descent tells us to modify the weights $w$ in the direction of steepest descent in $E$:
$$
w_t = w_{t-1}-\eta\frac{\partial E(w)}{\partial w}
$$
where $\eta$ is the ''learning rate'', and if it's large you will have a correspondingly large modification of the weights $W$ (in general it shouldn't be too large, otherwise you'll overshoot the local minimum in your cost function).
For a quadratic $E$,
$$
\frac{dE(w)}{dw} = \frac{dE(w_c)}{dw} + (w-w_c)\frac{d^2E(w)}{dw^2}
$$
by setting the derivative to $0$ at $w_{min}$
$$
w_{min} = w_c-\left(\frac{d^2E(w)}{dw^2}\right)^{-1}\frac{dE(w_c)}{dw}
$$
So we can reach optima by setting
$$
\eta = \left(\frac{d^2E(w)}{dw^2}\right)^{-1}
$$
!! Weight decay
In order to effectively limit the number of free parameters in your model so as to avoid over-fitting, it is possible to regularize the cost function. An easy way to do that is by introducing a zero mean Gaussian prior over the weights, which is equivalent to changing the cost function to $\tilde E(w)=E(w)+\frac\lambda2w^2$. In practice this penalizes large weights and effectively limits the freedom in your model. The regularization parameter $\lambda$ determines how you trade off the original cost $E$ with the large weights penalization.
Applying gradient descent to this new cost function we obtain:
$$
w_t=w_{t-1}−\eta\frac{\partial E}{\partial w_{t-1}}-\eta\lambda w_{t-1},
$$
The new term $-\eta\lambda w_i$ coming from the regularization causes the weight to decay in proportion to its size. It causes the weights to exponentially decay to zero, if no other update is scheduled.
!! Stochastic gradient descent
[[Theory|Stochastic Gradient Descent]]
Stochastic Gradient Descent (SGD) simply does away with the expectation in the update and computes the gradient of the parameters using only a single or a few training examples. The new update is given by,
$$
w_t = w_{t-1}-\eta\frac{\partial E(w;x_i, y_i)}{\partial w}
$$
with a pair $(x_i, y_i)$ from the training set.
!! Stochastic gradient with classical momentum (CM)
If the objective has the form of a long shallow ravine leading to the optimum and steep walls on the sides, standard SGD will tend to oscillate across the narrow ravine since the negative gradient will point down one of the steep sides rather than along the ravine towards the optimum. The objectives of deep architectures have this form near local optima and thus standard SGD can lead to very slow convergence particularly after the initial steep gains.
To minimize a cost function $f(\theta)$ classical momentum updates amount to,
$$
\begin{align}
v_t& = \mu v_{t-1}-\eta\nabla E(w_{t-1})\\
w_t&=w_{t-1}+v_t
\end{align}
$$
where $v_t$ denotes the accumulated gradient update, or velocity, $\eta>0$ is the learning rate, and the momentum constant $\mu\in[0, 1]$ governs how we accumulate the velocity.
[[Polyak 1964|http://www.mathnet.ru/php/getFT.phtml?jrnid=zvmmf&paperid=7713&what=fullt&option_lang=eng]] showed that CM can considerably accelerate convergence to a local minimum, requiring $\sqrt{R}$-times fewer iterations than steepest descent to reach the same level of accuracy, where $R$ is the condition number of th e curvature at the minimum and $\mu$ is set to $(\sqrt{R}-1)(\sqrt{R}+1)$.
To find more about the optimizers, [[caffe document|http://caffe.berkeleyvision.org/tutorial/solver.html]] is a good place to start.
We can find similarity between SGD and Spike-timing Dependent Plasticity in [[this talks|Deep learning in biology]]
* For general non-convex problems, GD finds a stationary point in polynomial time. * GD almost always escapes saddle points asymptotically. [[(Lee et al. 2016) Gradient descent converges to minimizers|https://arxiv.org/abs/1602.04915]] * GD with perturbations is not slowed down by saddle points. [[(Ge et al. 2015) Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition|https://arxiv.org/abs/1503.02101]], [[(Jin et al. 2017) How to escape saddle points efficiently|https://arxiv.org/abs/1703.00887]]
[[link|https://arxiv.org/abs/1502.03492]] [[talk|http://videolectures.net/icml2015_duvenaud_reversible_learning/?q=icml%202015]] TL;DR: We can compute the gradient of learning procedures. The only problem is it's memory inefficient. But if we do the reversible learning thing, we can scale to optimize thousands hyperparameters. This talk is given on ICML 2015 Bayesian optimization workshop. But interestingly, this paper has nothing to do with Bayesian optimization. Hyperparameter optimization looks like the most important part of machine learning, and we have to do it without gradient. We can find some continuous relaxation of what we are trying to do, take the gradient and optimize it with gradient descent. So we can think SGD as a funtion and take its gradients. Think of SGD as a function. If we fix the random seed, with twice differentiable loss from e.g. a deterministic loss funtion of all the hyperparameters. Hyperparameter opt like optimizing the output of the function of SGD. We can differentiate the SGD. In each for-loop, different samples are in it. It seems like optimizing thousand layer net. Now we have automatic differentiation engines. Why this is not done 20yrs ago, because memory consumption would kill you. The naive reverse-mode differentiation is infeasible from a memory perspective. ! Hyper gradient !! Reversible learning with finite precision arithmetic The Hessian-vector product of weights w and loss parameter $\theta$ can be computed exactly by applying RMD to the dot product of the gradient with a vector. The time complexity to reverse SGD is the same as forwarding. However, the naive reverse would fail due to finite numerical precision. Each multiplication by momentem decay $\gamma < 1$ shifts bits to the right, destroying the least significant bits. By repeatedly multiplying $1/\gamma$ accumulates errors exponentially. A $\gamma > 1$ results in unstable dynamics, and $\gamma = 1$ recovers the leap frog integrator, a perfectly reversible set of dynamics, but one that does not converge. The $\gamma$ term is analogous to a drag term in the simulation of Hamilton dynamics. Having $\gamma < 1$ corresponds to //dissipative// dynamics which generates heat, increases the entropy of the environment and is not therefore not reversible. !! Optimal storage of discarded entropy The number of destroyed bits is $-\log_2(\gamma)$ We can take derivation of cross validation loss. The computation is huge. 10GB memory. The naive reverse will go astray Use a buffer to store the destroyed bits. if $\beta$ is one, it is Hamiltonian dynamics. Hamiltonian Monte Carlo. What about Bayesian optimization?
Convolutions in Fourier-domain are simple pointwise multiplications of the Fourier-transform of a signal. This, however, is a very impractical view: computing the spectrum of signal over general graphs @@color:#859900;involves matrix diagonalisation@@, which generally has cubic complexity in the number of nodes. Computing exact convolutions over graphs is thus computationally intensive. ! Bib * [[Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering|https://arxiv.org/abs/1606.09375]] * [[Semi-Supervised Classification with Graph Convolutional Networks|https://arxiv.org/abs/1609.02907]] ** [[author's blog|http://tkipf.github.io/graph-convolutional-networks/]] ** [[discussion of expressiveness|http://www.inference.vc/how-powerful-are-graph-convolutions-review-of-kipf-welling-2016-2/]]
! Overview Graphical model is a probabilistic model expressing probability distributions via graph. The graph learning problem is formulated as follows: * we are given a set of nodes, each with some observed numeric attributes $x_i$. * For each node we'd like to predict an output or label $y_i$. We observe these labels for some, but not all, of the nodes. * We are also given a set of weighted edges, summarised by an adjacency matrix $A$. The main assumption is that when predicting the output $y_i$ for node $i$, the attributes and connectivity of nearby nodes provide useful side information or additional context. ! Applications * in machine learning ** Learning low-level vision * statistical physics ** Exactly solved models in statistical mechanics * theoretical computer science ** Probabilistic reasoning in intelligent systems: networks of plausible inference ! Resources [[The link to the short course|http://ai.stanford.edu/~paskin/gm-short-course/]] Probability is the basic. There are alternatives to probability theory: * Dempster-Shafer theory * Disjunctive uncertainty, etc. * (Fuzzy logic is about imprecision, not uncertainty.) The reason probality theory is better is de Finetti's theory: <<< if you fo not reasone according to [[Probability theory]], you can be made to act irrationally. <<< ! Inference [[Computing Partition Functions of Graphical Models]] !! Algorithms * [[Variable Elimination Algorithm]] * [[Hammersley-Clifford Theorem]] * [[Junction Tree Algorithm]] ! Deep Learning * [[Graph Convolution]]
@article{graves2005framewise,
title={Framewise phoneme classification with bidirectional LSTM and other neural network architectures},
author={Graves, Alex and Schmidhuber, J{\"u}rgen},
journal={Neural Networks},
volume={18},
number={5},
pages={602--610},
year={2005},
publisher={Elsevier}
}
Note of [[a talk given by Anandkumar|http://videolectures.net/iclr2016_anandkumar_nonconvex_learning/]] at ICLR 2016 Keynotes.
Mentioned papers:
# Anandkumar et al. 2012, [[Tensor decompositions for learning latent variable models|http://arxiv.org/abs/1210.7559]]
# Agarwal et al. 2014, [[Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization|https://arxiv.org/abs/1310.7991]]
# Anandkumar et al. 2015, [[Convolutional Dictionary Learning through Tensor Factorization|https://arxiv.org/abs/1506.03509]]
! Non-convexity in unsupervised learning
How do we overcome non-convexity and why Tensors are something everybody should think about to solve this problem. Currently, we have enough data to make supervised neural nets work, however, unsupervised learning is not improved that much.
In the case of unsupervised learning, our goal is to learn model parameters $\theta$ from observations $x$ that maximize the likelihood $p(x; \theta)$. It is reasonable to assume that local optima (or saddle points) should be a problem since there are always exponential no. of critical points according to dimensions.
Traditional local search methods such as SGD, EM and Variational Inference could have problem dealing with those critical points and GAN should also have a non-convex architecture.
! Tensor methods
We can always alter the objective of max likelihood to best tensor decomposition. Under infinite sample condition, the global optimum is preserved:
$$
\arg\underset{\theta}{\max} p(x;\theta) = \arg\underset{\theta}{\min}\|\hat T(x) - T(\theta)\|^2_F
$$
We will later show this technique can suceed with many algorithms.
We can extract latent factors with matrix factorization but the solution is not unique. So we always add orthogonal constraints. There are two drawbacks:
# If the matrix is not orthogonal, the solution is hard to get.
# Latent information is constrained by the rank of matrix.
To overcome these, we can collect more information and apply shared matrix decompotion, generalizing it to tensor decomposition.
A simple example similar to this talk's is given by [[Ge|http://www.offconvex.org/2015/12/17/tensor-decompositions/]]. [[Kruskal|http://www.sciencedirect.com/science/article/pii/0024379577900696]] gave sufficient conditions for such decompotisions to be unique, i.e., when rank one pairs are linearly independent. This is much weaker than the matrix case (orthogonal).
!! Implementing tensor decomposition
[[The general tensor decomposition is NP-hard|http://arxiv.org/abs/0911.1393]]. Ge introduced [[a generalized version of Jenrich's method|http://dl.acm.org/citation.cfm?id=173234]] to
We can use power method to iteratively find eigenvectors. For matrices, we take a random vector, muliply and normalize it and repeat. Then we can get the top eigenvector, substract it and repeat this process. We can do the same for tensors
We extends the notion of matrix product with tensor contraction:
$$
T(u, v, \cdot) = \sum_{i,j}u_iv_jT_{i, j, :}
$$
We contract the tensor along two directions, if we repeat this for orthogonal tensors, we get the eigenvectors. For orthogonal tensors, all the vectors of the decomposition are stationary points. But in the general case, tensor decomposition is non-linear and there are exponentially many of stationary points.
For non-orthogonal tensors, we orthogonalize it with a matrix $W$, which can be found with SVD. When $v_i$'s linearly independent, the orthogonalization is invertible. [[Anandkumar et al. 2012|http://arxiv.org/abs/1210.7559]] gives a proof that tensor power method is efficient and robust to noise.
! Applications
!! Finding latent variables
Authors apply tensor decomposition to clustering problems like topic modeling and [[community detection|https://arxiv.org/abs/1302.2684]], vast improvements are observed in both running time and likelihood comparing to variational inference. This method can speed up probability models in a great extend.
!! Learning representations
One of the use of unsupervised learning is to learn representations. A very popular model is sparse coding. There is strong evidence that neuron conducts sparse coding. In its more simplified form, every sample is a sparse linear combination of dictionary elements. When the dictionary components a linear coherent, we can learn this overcomplete representation [[through tensor method|https://arxiv.org/abs/1506.03509]]. This is a natural assumption because we want these elements to be not redundant. This requires analysing composition of the tensor where the number of components can exceed the dimension, which is impossible for matrix to recover.
We want to find shift-invariant features in e.g. vision applications, convolutional models handle this properly. With convolution constraints, we translate the problem to a tensor where the components are tied to one another. When the componets shift, we can resolve structures efficiently through different operations.
[[Huang et al. 2015|https://arxiv.org/abs/1309.0787]] train text corpus for para-phrase detection. With 4k samples on MSR corpus, the performance is very similar to skipthought's, which relies on extra large book corpus.
Similar story with [[holographic embeddings for knowledge bases|http://arxiv.org/abs/1510.04935]].
!! Reinforcement learning
Another application is reinforcement learning. One challenging aspect is to think about partially observable processes, where we model the environment with hidden state. If we incorporate with Markov DP framework, we can solve these hidden variables through tensor methods. In the author's example, they use memoryless policies to reduce complexity of long history information. First regret bounds are given and exploitation-exploration can be done efficiently under this framework. They also show that tensor methods can have a better reward than CNNs on Atari game playing.
An insight on why this is possible is that the tensor methods are first looking into learning latent representations, and planning in the hidden space could make predictions more effective.
!! Tensors in deep learning
In many recent works, tensors have been effectively used in deep learning frameworks. One natural question is whether SGD is the only thing we can do. Few researchers are dare to train their model from scratch, the major concern is how to avoid bad critical points.
[[Local optima are easy to construct|https://arxiv.org/abs/1511.06856]]. There are mechanisms to overcome these local optima. [[Janzamin et al. 2014|https://arxiv.org/abs/1412.2863]] proposed a method using the score functions. By looking are the relationship between the input and output, it's possible to train a one layer NN with one hidden layer with guarantees. The idea is to look at transformations of the input using score funtions. If we look at the predicted label $y$ and the score funtion of the input $\mathcal S_m(x)$, we will get a linear combination of the weight vectors of the first layer. Now we can construct a hierarchy of them. If we go to 2nd order score funtions, we'll get a decomposition where each rank 1 component correspond to the weight vectors. By resolving with the tensor decomposition, it is possible to learn the weight vectors of the first layer of the two layer network.
Here we require the notion of score funtions to do the transformation. The idea is to overcome the nonlinearity of these neurons here, we also need to transform the input by the score funtions. We need the derivatives of the input model. For example, if we have a Gaussian input, we get Hermite polynomials.
[[Novikov et al. 2015|http://arxiv.org/abs/1509.06569]] uses tensor to compress the dense layers of CNNs. The notion of tensor train format compresses the weight. The idea is we can get a compact representation of these weight matrices. There is potential for a huge compression rate.
[[Cohen et al. 2015|https://arxiv.org/abs/1509.05009]] use hierarchical Tucker tensors for representating arithmetic convnets to study the expressive power of deep neural nets. Decomposition described before is called the [[CP decomposition|https://en.wikipedia.org/wiki/Tensor_rank_decomposition]], whereas the Tucker one is different from the fact that there can be an arbitrary core tensor. The idea is to look at the input tensor and decompose it into a core tensor and its transformation with different factor matrices. In the CP decomposition this core tensor will be a diagonal one. We can do a hierarchical Tucker decomposition by progressively decomposing this core into various representations. They have algebraic arguments to show that deep is better than shallow. Transfering a deep net into a shallow one would require exponentially many parameters.
[[Tensors have also been used in memory model|https://arxiv.org/abs/1511.07972]].
Tensor is a useful tool. Improvements can be done in [[scaling up tensor with dimensionality reduction|https://arxiv.org/abs/1506.04448]] and [[speeding up on computation platforms|http://arxiv.org/abs/1606.05696]]
! Introduction
HMC makes use of Hamiltonian dynamics in MCMC, following Newton's laws of motion. HMC is based on dynamical simulation that does not rely on any prior assumptions about the form of the posterior distribution. HMC can be difficult to work with, as setting the leapfrog step sizes is hard. This method does not scale to large data. This is simplified by [[Langevin method|Stochastic Gradient Langevin Dynamics]].
! [[A Conceptual Introduction to Hamiltonian Monte Carlo|https://arxiv.org/abs/1701.02434]]
HMC is a procedure for introducing auxiliary momentum with a probabilistic structure such that one can generate efficient exploration.
As the system evolves, any compression or expansion in position space must be compensated with a respective expansion or compression in momentum space to ensure that the volume of any neighborhood in position-momentum phase space is unchanged.
$$
\pi(q, p) = \pi(p|q)\pi(q)
$$
is called the canoical distribution. We write it in terms of an invariant Hamilton function:
$$
\pi(q, p) = e^{-H(q, p)}
$$
The Hamilton
$$
H(q, p) = -\log\pi(q, p) = -\log\pi(p|q) - \log\pi(q) = K(p, q)+V(q)
$$
where $K$ is the kinetic energy and $V$ is the potential energy.
@inproceedings{harati2012applications,
title={Applications of Dirichlet Process Mixtures to speaker adaptation},
author={Harati Nejad Torbati, AH and Picone, Joe and Sobel, Marc},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on},
pages={4321--4324},
year={2012},
organization={IEEE}
}
! Intro !! Model HDP-HMM inadequately models the temporal persistence of states, in which the Bayesian bias toward simpler models is insufficient to prevent the HDP-HMM from giving high posterior probability to models with unrealistically rapid switching. * Transition distribution * Emmision distribution ** the conditional distribution of observations given states ** high performance HMMs generally use finite Gaussian mixtures as emission distributions ** nonparametric, used Dirichlet * Computation ** classical: forward-backward ** nonparametric: Gibbs sampler !! The speaker diarization task [[Speaker diarization literatures]]
HF is a practical large scale implementation of Newton's method to learn deep neural networks from random initializations.
$$
q_{\theta_n}(\theta) = M_{\theta_n}(\theta)+\lambda R_{\theta_n}(\theta),
$$
where $M_{\theta_n}(\theta)$ is a $\theta_n$-dependent "local" quadratic approximation to $f(\theta)$ given by
$$
M_{\theta_n}(\theta) = f(\theta_n)+f'(\theta_n)^\top\delta_n+\frac{\delta_n^\top C_n\delta_n}{2},
$$
where $C_n$ is an approximation to the curvature of $f$ at $\theta_n$, $\delta_n=\theta-\theta_n$, and $R_{\theta_n}(\theta)$ is a damping function that penalizes the solution according to the difference between $\theta$ and $\theta_n$, thus encouraging $\theta$ to remain close to $\theta_n$.
In standard HF, A.K.A. truncated-Newton, $C_n = d^2f(\theta_n)$ and $R_{\theta_n}(\theta)=\|\delta_n\|^2/2$. Unlike with quasi-Newton approaches like L-BFGS there is no low-rank or diagonal approximation involved, and as a consequence, fully optimizing the sub-objective $q_{\theta_n}$ can require a matrix inversion or some similarly expensive operation. The HF approach circumvents this problem by performing a much cheaper partial optimization of $q_{\theta_n}$ using the ''linear conjugate gradient algorithm'' (CG).
CG is invoked for minimizing quadratic function $\phi(z) = z^\top (C_n+\lambda I)z/2 - f'(\theta_n)^\top z$ starting from $z = \delta_{n-1}$. The Hessian-vector products is computed within a black-box function. The modifications on HF on neural networks include:
# using the positive semi-definite [[Gauss-Newton curvature matrix]] $G_f$ in place of the possibly indefinite Hessian
# using damping function for $R_{\theta_n}(\theta)=\|\delta_n\|^2/2$ with the damping parameter $\lambda$ adjusted by Levenberg-Marquardt style heuristics
# using a criterion based directly on the value of $q_{\theta_n}$ in order to terminate CG (not residual-error $\|Bz-b\|^2/2$)
# computing the curvature-matrix products $Bv$ using mini-batches and the gradients with much larger mini-batches.
!! [[Structral Damping]]
! Expriments
HF algorithm is capable of training RNNs to solve problems with very long-term dependencies.
[[TIMIT HF]]
HMM combine non-linearity with tractable inference by using a posterior that is a discrete distribution over a fixed number of mutually exclusive alternatives, which makes them exponentially inefficient at dealing with componential structure: to allow the history of a sequence to impose N bits of constraint on the future of the sequence, an HMM requires at least $2^N$ nodes.
! TODO * clojure support * other themes
@@color:#859900;text@@
! Bibs * [[Training Very Deep Networks|https://arxiv.org/abs/1507.06228]] * [[Recurrent Highway Networks|http://arxiv.org/abs/1607.03474]]
! Monophone ! Triphone
The homogeneous Poisson process counts events that occur at a constant rate; it is one of the most well-known [[Levy processes|Levy Process]]. This process is characterized by a rate parameter $\lambda$, also known as //intensity//, such that the number of events in time interval $(t, t+\tau]$ follows a [[Poisson distribution|Poisson Distribution]] with associated parameter $\lambda\tau$. This relation is given as
$$
P [N(t+ \tau) - N(t) = k] = \frac{e^{-\lambda \tau} (\lambda \tau)^k}{k!} \qquad k= 0,1,\ldots,
$$
where $N(t + \tau) − N(t) = k$ is the number of events in time interval $(t, t + \tau]$.
The capacity of Hopfield net is limited by the speed of energy decay related to second-order neuron connection weight. This could explain why RNN is inferior: The hidden state is first-order, which means no cross bit connection (such as term $h_t^i*h_t^j$ in hopfield net) is defined in $\mathbf h_t$
From NIPS 2016 GAN Workshop
# Normalize the inputs
#* normalize the images between -1 and 1
#* Tanh as the last layer of the generator output
# Modified loss function
#* change $\min\log(1-D)$ into $\max\log(D)$: first has vanishing gradient early on.
#* $\mathcal L_G(\theta, \phi) = -\mathbb E_{q_\phi(x)}[\log(D_\theta(x))]$
#* Flip labels when training generator
# Use spherical $z$
#* interpolation via great circle from [[Sampling Generative Networks|https://arxiv.org/abs/1609.04468]]
#* probably because of loss?
# Use BatchNorm properly
#* different batches for real and fake
#* when batchnorm is not an option, use instance norm, anyone tried weight norm?
# Avoid Sparse Gradients: ReLU, MaxPool
#* use LeakyReLU both $G$ and $D$
#* downsampling by: ave pooling, conv+stride
#* upsampling by: PixelShuffle, ConvTranspose2d+stride
# Soft and noisy labels
#* label smoothing
#* making the labels noisy a bit for the discriminator, sometimes
# DCGANs / Hybrid models
#* DCGAN when you can
#* if you can't use CDGANs, and no model is stable, use a hybrid model: KL+GAN or VAE+GAN
# Stability tricks from RL
#* experience replay
#* things that work for deep deterministic policy gradients
#* See [[Connecting Generative Adversarial Networks and Actor-Critic Methods|https://arxiv.org/abs/1610.01945]]
# Use Adam
#* at least for generators
# Track failures early
#* $D$ loss = 0
#* check norms of gradients
#* $D$ loss should have low variance and goes down over times
#* if loss of generator steadily decreases, then it's fooling $D$ with garbage
# Don't balance via loss statistics
# If you have labels, use them
# Add noise to inputs, decay over time
#* Add some artificial noise to inputs to D ([[Arjovsky et. al|https://openreview.net/forum?id=Hk4_qw5xe]], Huszar, 2016) [[inFERENCe|http://www.inference.vc/instance-noise-a-trick-for-stabilising-gan-training/]]
#* Add Gaussian noise to every layer of generator (EBGAN)
#* Improved GANs: OpenAI code also has it (commented out)
# Train discriminator more
Installing all dependencies for HTS demo CMU Arctic slt
* HTS 2.2 patch for HTK 3.4.1, installed
* [[Festival]] (festvox): festival requires a bunch of files to download and comes with a dataset
* hts_engine API, installed
* SPTK: requires csh, installed tcsh on Arch machine
* tcl/tk (already up to date)
! Model
$$
P(O|\lambda) = \sum_{Q}P(O,Q|\lambda)
$$
where $O$ is speech parameter vector sequence, $Q$ is state and $\lambda$ is the continuous mixture HMM. The speech parameter vector $o_t$ consists of the static feature vector (e.g. cepstral coefficients) and dynamic feature vectors (delta, delta2 cepstral coefficients) that is $o_t = [c_t, \Delta c_t, \Delta^2c_t]$.
The model is solved by alternating gradient descend.
```
@inproceedings{graves2013hybrid,
title={Hybrid speech recognition with deep bidirectional LSTM},
author={Graves, Alan and Jaitly, Navdeep and Mohamed, Abdel-rahman},
booktitle={Automatic Speech Recognition and Understanding (ASRU), 2013 IEEE Workshop on},
pages={273--278},
year={2013},
organization={IEEE}
}
```
! Experiments
|Network |Dev PER |Test PER |Dev FER |Test FER |Dev CE |Test CE |
|DBRNN |19.91+-0.22|21.92+-0.35|30.82+-0.31|31.91+-0.47|1.07+-0.010|1.12+-0.014|
|DBLSTM |17.44+-0.16|19.34+-0.15|28.43+-0.14|29.55+-0.31|0.93+-0.011|0.98+-0.019|
|DBLSTM (Noise) |16.11+-0.15|17.99+-0.13|26.64+-0.08|27.88+-0.16|0.88+-0.008|0.93+-0.004|
A phonetic dictionary was used, with three states per phoneme, giving 183 target states in total. A biphone language model was estimated on the training set, and a simple GMM-HMM system was used to provide forced alignments. The posterior state probabilities provided by the network were not divided by the state occupancy priors, as this has been found to make no difference on TIMIT.
! Introduction !
! Keynote Talks [[Guaranteed Non-convex Learning Algorithms through Tensor Factorization]] ! Papers !! Theory [[Data-dependent Initializations of Convolutional Neural Networks]] !! Older ones !!! Generating images from captions with attention from reference, a machine translation result is mentioned (Bahdanau et al., 2015) combines bidirectional rnn: for script understanding inference/generative rnn: for latent sequence posterior estimation and image generation !!! Neural variational inference for text processing NVDM, neural variational document model: MLP softmax BoW generation NASM, neural answer selection model: LSTM model trained with stochastic gradient variational bayes !!! All you need is a good init Unsupervised representation learning with deep convolutional generative adversarial networks Related works GANs * generator and discriminator nets * unstable to train this paper developed Deep Convolutional GAN Representation learning: * classic: hierarchical clustering * mainstream: autoencoders * deep belief networks !!! Delving Deeper into Convolutional Networks for Learning Video Representations
2017 papers * [[Hunting through the ICLR 2017 submissions|http://smerity.com/articles/2016/iclr_2017_submissions.html]] !! Oral Papers * 7: [[End-to-end Optimized Image Compression]] !! Talks [[Talk|https://www.facebook.com/iclr.cc/videos/vb.1026262914069441/1713144705381255/?type=2&theater]] * [[New Directions for RNNs]] * Best Paper Award: [[NPI via Recursion]] * 8: [[Optimization as a Model for Few-Shot Learning]] ! Identity Mapping * [[Identity Matters in Deep Learning]] ! Optimization * [[On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima]] * Inefficiency of Stochastic Gradient Descent with Large Mini-batches (and More Learners) ! Network * [[Understanding Deep Learning Requires Re-thinking Generalization|https://arxiv.org/abs/1611.03530]] * [[Categorical Reparameterization with Gumbel-Softmax|https://arxiv.org/abs/1611.01144]] ! NLP * [[Tracking the world state with recurrent entity networks|https://arxiv.org/abs/1612.03969]] * Deep Biaffine Attention for Neural Dependency Pasrsing * ReasoNet: Learning to Stop Reading in Machine Comprehension * Vocabulary Selection Strategies for Neural Machine Translation * Deep Character-Level Neural Machine Translation by Learning Morphology * Iterative Refinement for Machine Translation * A Convolutional Encoder Model for Neural Machine Translation * Reference-Aware Language Models
# [[ICML 2015]] #* [[Invariance Principles for Neural Network Training]] #* [[video recordings|http://dpkingma.com/?page_id=483]] # [[ICML 2016]] # [[ICML 2017]]
! DL Workshop * [[Generative Adversarial Network Talk]] ! Bayesian Optimization Workshop * [[Gradient-based Hyperparameter Optimization through Reversible Learning]]
[[List of all accepted papers|http://icml.cc/2016/?page_id=1649#]] !!! A Deep Learning Approach to Unsupervised Ensemble Learning RBM !!! Convolutional Rectifier Networks as Generalized Tensor Decompositions By analogy to convolutional arithmetic circuits, shows ReLU activation, average pooling losing universality; max pooling loses depth efficiency comparing to product pooling with linear activation. Therefore, authors argue convolutional arithmetic circuits if effectively trained, can have better expressive power and depth eff !!! End-to-End Speech Recognition in English and Mandarin From Baidu AI lab Connectionist Temporal Classification loss function to predict transcriptions from audio: coupled with an RNN model temporal information. Trained from scratch without the need of framewise alignments for pre-training. dataset: 11,940 hours in English, 9,400 hours in Mandarin. 11 layers with many bidirectional recurrent layers and convolutional layers. Exploit long strides between RNN (reduces computation). remark: multilayer RNN structure does not seem intuitive. BatchNorm (before non-linear activation) to accelerate training. GRU better than 1-D RNN, but RNN scales better as data size increases. (optimization issues?) Language model: Kneser-Ney smoothed character level 5-gram model. English: 850mil n-grams trained with KenLM on Common Crawl Repository Chinese: 2bil n-grams !!! Inverse Optimal Control with Deep Networks via Policy Optimization paper not available Why Regularized Auto-Encoders learn Sparse Representation? Try to explain the effect of activation function and regularization on enforcing the sparsity of AEs. 1. The suff condition on regularization 2. Case study on Denoising AE and Contractive AE efficiency than convolutional rectifier nets. !!! Training Neural Networks Without Gradients: A Scalabel ADMM Approach
* [[ICML 2017 Papers]]
! Tutorials
!! Recent Advances in Stochastic Convex and Non-Convex Optimization
The form of master problem:
$$
\min_{x\in\mathbb R^d}\{\psi(x)+\frac1n\sum_{i=1}^nf_i(x)\}
$$
* [[Decoupled Neural Interfaces using Synthetic Gradients|https://arxiv.org/abs/1608.05343]]: summarized in [[Synthetic Gradients]], together with another paper [[on DNI|https://arxiv.org/abs/1703.00522]] * [[Meta Networks|https://arxiv.org/abs/1703.00837]] * [[On the Expressive Power of Deep Neural Networks]] * [[Training Quantized Nets: A Deeper Understanding]] * [[Attentive Recurrent Comparators]] ! Google Brain * [[Neural Message Passing for Quantum Chemistry|http://proceedings.mlr.press/v70/gilmer17a.html]] ! Facebook * High-Dimensional Variance-Reduced Stochastic Gradient Expectation-Maximization Algorithm. //Rongda Zhu, Lingxiao Wang, Chengxiang Zhai, Quanquan Gu// * An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis. //Yuandong Tian// * [[Convolutional Sequence to Sequence Learning]]. //Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann Dauphin// * Efficient softmax approximation for GPUs. //Edouard Grave, Armand Joulin, Moustapha Cisse, David Grangier, Hervé Jégou// * Gradient Boosted Decision Trees for High Dimensional Sparse Output. //Si Si, Huan Zhang, Sathiya Keerthi, Dhruv Mahajan, Inderjit Dhillon, Cho-Jui Hsieh// * Language Modeling with Gated Convolutional Networks. //Yann Dauphin, Angela Fan, Michael Auli, David Grangier// * Parseval Networks: Improving Robustness to Adversarial Examples. //Moustapha Cissem, Piotr Bojanowski, Edouard Grave// * Unsupervised Learning by Predicting Noise. //Piotr Bojanowski, Armand Joulin// * [[Wasserstein Generative Adversarial Networks|WGAN]]. //Martin Arjovsky, Soumith Chintala, Leon Bottou// ! DeepMind * [[Parallel Multiscale Autoregresssive Density Estimation]] * [[Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders]] * Reinforcement Learning ** [[Why is Posterior Sampling Better than Optimism for Reinforcement Learning?]] ** [[DARLA: Improving Zero-Shot Transfer in Reinforcement Learning]] ** [[Automated Curriculum Learning for Neural Networks]] ** [[Learning to learn without gradient descent by gradient descent]] ** [[A Distributional Perspective on Reinforcement Learning]] ** [[A Laplacian Framework for Option Discovery in Reinforcement Learning]] ! Tencent Recommendations * [[FeUdal Networks for Hierarchical Reinforcement Learning]]
! Linearized ResNet
Refs
* [[Geometry of linearized neural networks|http://blogs.princeton.edu/imabandit/2016/11/13/geometry-of-linearized-neural-networks/]]
* [[Identity Matters in Deep Learning|https://arxiv.org/abs/1611.04231]]
We consider a linearized net, simplified to a product of matrices:
$$
g(A_0,\dots, A_L) = \mathbb{E}_{(x,y) \sim \nu} \|\prod_{i=0}^L A_i x - y\|^2 ,
$$
which, although non-convex, satisfies the second-order optimality condition:
<<<
''Proposition'' [[[Kawaguchi 2016|https://arxiv.org/abs/1605.07110]]]<br>
Assume that $x$ has a full rank covariance matrix and that $y=Rx$ for some deterministic matrix $R$. Then all local minima of $g$ are global minima.
<<<
The residual network looks like this:
$$
f(A_0,\dots, A_L) = \mathbb{E}_{(x,y) \sim \nu} \|\prod_{i=0}^L (A_i+\mathrm{I}) x - y\|^2 .
$$
<<<
''Proposition'' [Hardt and Ma 2016]<br>
Assume that $x$ has a full rank covariance matrix and that $y=Rx$ for some deterministic matrix $R$. Then $f$ has first order optimality on the set $\{(A_0, \dots, A_L) : \|A_i\| \lt 1\}$.
<<<
''Proof'': One has with $E = R - \prod_{i=0}^L (A_i+\mathrm{I})$ and $\Sigma = xx^{\top}$,
$$
f = (E x)^{\top} Ex = \mathrm{Tr}(E \Sigma E^{\top}) =: \|E\|_{\Sigma}^2 ,
$$
so with $E_{\lt i} = \prod_{j\lt i} (A_j + \mathrm{I})$, and $E_{\gt i}=\prod_{j\gt i} (A_j + \mathrm{I})$,
$$
\begin{eqnarray*}
f(A_0,\dots, A_i + V, \dots, A_L) & = & \|R - \prod_{j \lt i} (A_j + \mathrm{I}) \times (A_i + V +\mathrm{I}) \times \prod_{j\gt i} (A_j + \mathrm{I})\|_{\Sigma}^2 \\
& = & \|E + E_{\lt i} V E_{\gt i}\|_{\Sigma}^2 \\
& = & \|E\|_{\Sigma}^2 + 2 \langle \Sigma E, E_{\lt i} V E_{\gt i} \rangle + \|E_{\lt i} V E_{\gt i}\|_{\Sigma}^2 ,
\end{eqnarray*}
$$
which exactly means that the derivative of $f$ with respect to $A_i$ is equal to $E_{\gt i}^{\top} \Sigma E E_{\lt i}^{\top}$. On the set under consideration one has that $E_{\gt i}$ and $E_{\lt i}$ are invertible (and so is $\Sigma$ by assumption), and thus if this derivative is equal to 0 it must be that $E=0$ and thus $f=0$ (which is the global minimum).
! Remark
* Since non-linearity is so important in deep learning, how similar is it with linear system analysis?
* Similar result is given for RNN in [[Optimization Properties of Linearized RNN]].
! Equivalence of ResNet and RNN
Refs
* [[Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex|https://arxiv.org/abs/1604.03640]]
!! Background [[Neuromorphic Cognitive Computing]] Karlheinz Meier - Neuromorphic Computing beyond von Neumann Cons of von Neumann: # Uniform # No separation of memory and computing # Not programmed # No established theory (in brain science?) Firing patterns represents the action potential Mimic this activity in microscopic level Biology Cell Level Simulation # 10,000 reconstructed neurons # 2 mWalt:100 Walt # 1000 times slower Timing problem is foundamental: e.g. simulating brain envolvement within 15yrs !! Definition Neuromorphic computing simulates neurons with silicon substrates. And it can solve time and energy issues (?) # massive parallel # configurability # asnchronous communication # SpiNNaker: 18 ARM 6 inter chip connections # IBM Almaden: 45nm processors, real programming model for AI problems # 10,000 faster than biology: 50mil synapses # real time approach analog cells (Boahen, stanford) # Qualcomm Neural Processing Units !! Interests # Cell properties ## Cell firing patterns, synchronisations, stability, order-chaos # Closed loops reverse engineering ## Functional units, cortical structures ## small brains: insect brain, 3 layer NN ## owl hearing: coincident neurons # test theories # Neuromorphic computing outside neural science !! Questions Lateral Prefrontal Cortex volume lossing (simulating this?) How is this related to von Neuman machine?
! Implementations [[Google|https://github.com/tensorflow/models/tree/master/im2txt]] ! Bibs * [[Self-Guiding Multimodal LSTM - when we do not have a perfect training dataset for image captioning|https://arxiv.org/abs/1709.05038]]
! Rigit transformation symmetries
* Translations: $\{\varphi_v;v\in\mathbb R^2\}$, with $\varphi_v(x)(u)=x(u-v)$.
* Dilations: $\{\varphi_s;s\in\mathbb R_+\}$, with $\varphi_s(x)(u)=s^{-1}x(s^{-1}u)$.
* Rotations: $\{\varphi_\theta;\theta\in[0, 2\pi)\}$, with $\varphi_\theta(x)(u)=x(R_\theta u)$.
* Mirror symmetry: $\{e, M\}$, with $Mx(u_1, u_2) = x(-u_1, u_2)$.
We can combine all these transformations into a single group, the Affine Group $Aff(\mathbb R^2)$. It has 6 degrees of freedom, in the representation
$$
\left(\begin{array}{c}u_1\\u_2\end{array}\right)\rightarrow
\left(\begin{array}{c}v_1\\v_2\end{array}\right)
\left(\begin{array}{cc}a_1&a_2\\a_3&a_4\end{array}\right)
\left(\begin{array}{c}u_1\\u_2\end{array}\right)
$$
A particular simple example is given by [[continuous one-parameter unitary transformations|Unitary Groups]].
This is in general a //non-commutative// group. For some groups, we might only observe partial invariance. In speech, the underlying group modeling time-frequency shifts is the ''Heisenberg'' group.
Given a transformation group $G$ and an input $x$, the ''action of $G$'' onto $x$ is called an ''orbit'':
$$
G\cdot x = \{\varphi_g(x), g\in G\}
$$
With a linear $\Phi(x)$, a 6-dim curvy space looks flat in a high-dimensional space. Group symmetries are not sufficient to beat the curse of dimensionality. It is too strict.
!! Limits of Group Diagonalisation
A shallow (1 layer) network is thus sufficient to achieve invariance to commutative group transformations. However, this architecture has a number of shortcomings.
!!! Non-commutative groups
''Proposition:'' If $G = \{\varphi t\}_t$ is non-commutative, then there is no basis $V$ that diagonalises simultaneously all $\varphi_t$.
Roto-translation is a group that is non-commutative: $(v', \theta')\cdot(v,\theta)=(v+R_{-\theta}v', \theta+\theta')$
!!! Losing degrees of freedom
Because of Hermitic symmetry, $\Phi:\mathbb R^n\rightarrow\mathbb R^{\lceil n/2\rceil}$, we “pay” $n/2$ degrees of freedom to remove group variability, independently of the group dimensionality.
If the group has dimension $p$, a G-invariant representation could have up to $n-p$ d.f.: we are losing discriminability when $p$ is small.
Fourier Phases encode most of the relevant signal information.
! Stability
[[Shallow invariants are unstable|Deformation Metric]]
Image and audio recognition is ''stable'' to local deformations. Let $x\in L^2(\mathbb R^m)$ be the data, $\tau:\mathbb R^m\rightarrow\mathbb R^m$ be the diffeomorphism. And $x_\tau = \varphi_\tau(x)$, where $\varphi_\tau$ is a change of variables: (think of $x_\tau$ as adding noise to the pixel //locations// rather than to the pixel values). $x_\tau(u) = x(u=\tau(u))$.
Informally, if $\|\tau\|$ measures the amount of deformation, many recognition tasks satisfy
$$
\forall x, \tau, |f(x)-f(x_\tau)\lesssim\|\tau\|
$$
Thus $F:\tau\mapsto\Phi(x_\tau)$ is Lipschitz w.r.t. the deformation metric $\|\tau\|$ uniformly on $x$. If our representation is stable, then
$$
\forall x, \tau, \|\Phi(x)-\Phi(x_\tau)\|\le C\|\tau\|\Rightarrow|\hat f(x)-\hat f(x_\tau)|\le\tilde C\|\tau\|
$$
The stablity can be represented by sampling from a Ergodic process:
* In statistical physics, a process with an ''Integral Scale'' is ergodic
* In statistics, linear processes are ergodic (provided the moments are finite).
! Bib
!! Deep Identity and Invariance
!!! ICLR 2017 openreview
* [[Identity Matters in Deep Learning|http://openreview.net/pdf?id=ryxB0Rtxx]]
** Discuss the representation power of ResNet. Deviced a not very powerful model though. The performance needs further investigation.
* [[Learning Invariant Representations Of Planar Curves|http://openreview.net/pdf?id=BymIbLKgl]]
** Linking to numerical differential geometry. Using a Siamese architecture to learn (rotation) invariance. Should help generative model.
* [[Warped Convolutions: Efficient Invariance to Spatial Transformations|http://openreview.net/pdf?id=BkmM8Dceg]]
** Handles scale, rotation and spatial translation with warp and generalized conv. Good performance in pose estimation with ''soft argmax''. Hinders general classification.
! Traditional Methods !! Descriptors * [[LATCH: Learned Arrangements of Three Patch Codes|https://github.com/csp256/cudaLATCH]] * Product quantization: IVFADC ! Autoencoders [[MNIST example|https://github.com/BVLC/caffe/blob/master/examples/mnist/mnist_autoencoder.prototxt]] and there are several convolutional autoencoders in caffe and pylearn. However, I don't find it convincing that object level infomation can be extracted this way, considering how [[easily ConvNets can be fooled|http://www.evolvingai.org/fooling]]. ! Search algorithms for binary codes The search time on large-scale retrival database should be sub-linear in the number of examples. Hashing is widely used in this case because it's quick and require only binary data. It uses all dimension in query in contrast to tree-based algorithms. !! Linear scan in Hamming space Paralleling `xor`s and 1 counting's are trival in machine level. Easy speedup for multiples cores/machines, and gracefully scaling to longer code lengths. !! Semantic hashing Binary codes correspond to addresses in memory. # provided the radius is small, it is quick ($\mu$s) # constructing the database is also fast. The deficit is in codelength scaling, which makes the Hamming ball exponentially larger. In addition, a 32-bit code requires 4Gb memory. ! GPU base fast algorithms * [[Faiss]]
* given: state & action space, roll-out from $\pi^*$, dynamics model * goal: recover reward function, then use reward to get policy The challenges are: * underdefined problem * difficult to evaluate a learned reward * demonstrations may not be precisely optimal Imitation learning is very similar to GAN: * a generator generate policy samples from $\pi$ and update policy in the outer loop * a discriminator update reward using samples & demos and update reward in the inner loop DeepMind use GAN and VAE to learn human actions: [[Robust Imitation of Diverse Behaviors|https://arxiv.org/abs/1707.02747]]
!! MFCC Frame the signal into 20-40 ms frames. 25ms is standard.
Normalizing flows are a series of invertible transformations to latent variables with a simple posterior.
! General Normalizing Flows
Aim at formulating the flow for which the Jacobian-determinant is relatively easy to compute. Example is [[Variational Inference with Normalizing Flows|https://arxiv.org/abs/1505.05770]]
! Volume-Preserving Flows
Design series of transformations s.t. the Jacobian-determinant equals 1 while still it allows to obtain flexible posterior distributions.
Examples:
* Non-linear Independent Components Estimation
* Hamiltonian Variational Inference
!! Inverse Autoregressive Flow
[[link|https://arxiv.org/abs/1606.04934]] [[github|https://github.com/openai/iaf]]
IAF is the lower triangular inverse Cholesky matrix with ones on the diagonal. It improves nomalizing flow to be computationally cheap to:
# compute and differentiate for its probability density $q(z|x)$
# to sample from, parallizable. (sample $z$?)
# parallizable of these operations across dimensions of $z$
The autoregressive whitening operation makes $y$ with complicated distribution into $z$ with i.i.d. elements. The transformation $y\rightarrow z$ can be completely vectorized:
$$
z = (y-\mu(y))/\sigma(y)
$$
where subtraction and division are elementwise. This has a lower triangular jacobian matrix, whose diagonal elements are the elements of $\sigma(y)$, and:
$$
\log\det\left|\frac{dz}{dy}\right| = -\sum_{i=1}^D\log\sigma_i(y)
$$
!! Householder Flow
[[paper|http://arxiv.org/abs/1611.09630]]
The absolute value of Jacobian-determinant of an orthogonal matrix is 1. Any orthonogal matrix can be represented in the following form:
<<<
$\bf U=I-YSY^\top$
<<< [[The Basis-Kernel Representation of Orthogonal Matrices]]
! Implementations * Tensorflow models * [[Keras|https://github.com/kentsommer/keras-inceptionV4]] ! Batch Normalizations [[BN-Inception|Batch Normalization]] ! Guidelines [[link|http://arxiv.org/abs/1512.00567]] General rules are: # Avoid representational bottlenecks, especially early in the network. # Add filters per tile in CONVs to get higher dimensional representations # Spatial aggregation (dimension reduction) before CONVs # Balance the width and depth. Grid reduction: avoiding representational bottleneck or too much params. Reduce size with pooling and conv together: [img[inception_reduction.PNG]] ! Inception-v2 # 7x7 to 3 3x3 CONVs, output size is 35x35x288 # 3 traditional Inceptions, keep size # Change size to 17x17x768 with grid reduction # 5 factorized Inceptions, with [1x7, 7x1] CONVs replacing the 3x3 ones # Change size to 8x8x1280 with grid reduction # 2 factorized Inceptions, with [1x3, 3x1] CONVs replacing the 3x3 ones with 2048 filters All together 42 layers. BN auxiliary as regularizers. !! Remarks Historically, Inception-v3 had inherited a lot of the baggage of the earlier incarnations. The technical constraints chiefly came from the need for partitioning the model for distributed training using DistBelief. ! Inception-v4 and Inception-ResNet [[link|http://arxiv.org/abs/1602.07261]] Has a more uniform simplified architecture and more inception modules than Inception-v3. Introduces residual connections: Inception-ResNet. Difference is batch-normalization is only on top of the traditional layers, but not on top of the summations because otherwise, the network won't fit into one GPU.
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
!! Log sum
Let $a_1,\ldots,a_n$ and $b_1,\ldots,b_n$ be nonnegative numbers. Denote the sum of all $a_i$s by $a$ and the sum of all $b_i$s by $b$. The log sum inequality states that
$$
\sum_{i=1}^n a_i\log\frac{a_i}{b_i}\geq a\log\frac{a}{b},
$$
with equality if and only if $\frac{a_i}{b_i}$ are equal for all $i$.
Notice that after setting $f(x)=x\log x$ we have
$$
\begin{align}
\sum_{i=1}^n a_i\log\frac{a_i}{b_i} & {} = \sum_{i=1}^n b_i f\left(\frac{a_i}{b_i}\right)
= b\sum_{i=1}^n \frac{b_i}{b} f\left(\frac{a_i}{b_i}\right) \\
& {} \geq b f\left(\sum_{i=1}^n \frac{b_i}{b}\frac{a_i}{b_i}\right) = b f\left(\frac{1}{b}\sum_{i=1}^n a_i\right)
= b f\left(\frac{a}{b}\right) \\
& {} = a\log\frac{a}{b},
\end{align}
$$
where the inequality follows from Jensen's inequality since $\frac{b_i}{b}\geq 0$, $\sum_i\frac{b_i}{b}= 1$, and $f$ is convex.
! Theory
Probabilistic inference is used to handle noisy and imprecise data. Exact inference is difficult, so approximate methods like Gibbs sampling are used.
* Exact methods (conjugacy, enumeration)
* Numerical integration (Quadrature)
* Generalised method of moments
* [[Maximum Likelihood Estimation]]
* Maximum a posteriori
* [[Expectation-Maximization]]
* Contastive estimation (NCE)
* Cavity methods (EP)
!! Approximate Inference
* Laplace approximation
* Integrated nested Laplace approximations
* Importance sampling
* [[Variational Inference]]
* Perturbative corrections
* [[Monte Carlo Methods]]
! Tools
* [[Stan]]
* [[BOPP|https://github.com/probprog/bopp/]]: favorite tool in favorite language, based on [[Anglican|https://github.com/probprog/anglican/blob/master/doc/codemap.md]]
! Objectives
* prediction: $p(x_{t+1, \dots, \infty}|x_{-\infty, \dots, t})$
* planning: $J = \mathbb E_p[\int_0^\infty dtC(x_t)|x_0, u]$
* parameter estimation: $p(\theta|x_{0, \dots, N})$
* experiment design: $EIG = D[p(f(x_{t, \dots, \infty})|u);p(f(x_{-\infty, \dots, t}))]$
* hypothesis testing: $\frac{p(f(x_{-\infty, \dots, t})|H_0)}{p(f(x_{-\infty, \dots, t})|H_1)}$
Influence function is a classic technique from robust statistics that tells us how the model parameters change as we upweight a training point by an infinitesimal amount. We want to estimate the counterfactual:
<<<
How would the model's predictions change if we did not have this training point?
<<<
IFs give us an efficient approximation by computing the parameter change if $x$ were upweighted by some small $\epsilon$:
$$
\hat{\theta}_{-z} = \min_{\theta\in\Theta}\sum_{z_i\neq z}\frac 1 n\sum_{i=1}^nL(z_i, \theta)+\epsilon L(z,\theta)
$$
The influence of upweighting $z$ on the parameters $\hat\theta$ is given by
$$
\mathcal I_{up,params}(z)=\frac{d\hat\theta_{\epsilon, z}}{d\epsilon}|_{\epsilon=0}=-H_{\hat\theta}^{-1}\nabla_\theta L(z, \hat\theta)
$$
where $H_{\hat\theta} = \frac1n\sum_{i=1}^nL(z_i, \hat\theta)$ is the Hessian and is positive definite (PD) by assumption. The computation of Hessian and its reverse is expensive. The Hessian vector product can be approximated by conjugate gradient:
$$
H^{-1}v = \arg\min_t \frac12t^THt-v^Tt
$$
* [[link|https://arxiv.org/abs/1606.03657]]
* [[blog|http://www.inference.vc/infogan-variational-bound-on-mutual-information-twice/]]
In order to maximise the mutual information between generated samples and a small subset of latent variables $c$, the authors make use of a variational lower bound. This, conveniently, results in a recognition model, similar to the one we see in variational autoencoders. The recognition model infers latent representation $c$ from data.
The idealised InfoGAN objective is the weighted difference of two mutual information terms.
$$
\mathcal l_{infoGAN}(\theta)=I[x,y]−\lambda I[x_{fake},c]
$$
To arrive at the algorithm the authors used, one uses the bound on both mutual information terms.
* When you apply the bound on the first term, you get a lower bound, and you introduce an auxillary distribution that ends up being called the discriminator. This application of the bound is wrong because it bounds the loss function from the wrong side.
* When you apply the bound on the second term, you end up upper bounding the loss function, because of the negative sign. This is a good thing. The combination of a lower bound and an upper bound means that you don't even know which direction you're bounding or approximating the loss function from anymore, it's neither an upper or a lower bound.
! To use this on WGAN
! Definitions [[Perplexity]] ! Theorems [[Principle of maximum entropy]] !! Data Processing Inqeuality (DPI) & Invariance Let $I(X;Y)$ be the ''mutual information'': <<< ''Definition'' [Mutual Information]<br> $I(X;Y)=D[p(x, y)\|p(x)p(y)] = D[p(x|y)\|p(x)]=H(X)-H(X|Y)$ <<< for any Markov chain $X\rightarrow Y\rightarrow Z$: $I(X;Y)\ge I(X;Z)$ Reparametrization Invariance: for invertible $\phi, \psi$: $$ I(X;Y) = I(\phi(X);\psi(Y)) $$ ! Talks [[Information Theory for Deep Learning]] ! Bibs * [[On Unlimited Sampling|http://www.mit.edu/~ayush/MIT/Unlimited_Sampling.html]]: higher bandwidth sampling on ADC.
! The Information Bottleneck Method
* Approximate Minimal Sufficient Statistics:
** Markov chain: $Y\rightarrow X\rightarrow S(X)\rightarrow \hat X$: $\hat X=\arg\min_{I(S(X);Y)=I(X;Y)}I(S(X);X)$
** Relaxation given $p(X;Y)$: $\hat X=\arg\min_{p(\hat X;X)}I(\hat X;X)-\beta I(\hat X;Y)$， $\beta>0$
* A Rate-Distortion problem with KL-divergence distortion:
** $d_{IB}(x, \hat x) = D[p(y|x)\|p(y|\hat x)]$
* The ONLY distributional quantization measure which satisfy both DPI (f-divergences) and Statistical Consistency
! DNN Layer represent
* A Markov chain of topologically distincy [soft] partitions of the input variable X
* Successive Refinement of Relevant Information
* Individual neurons can be easily "scrambled" within each layer
! Input Compression bounds
We create a generalization bound considering the cardinality of the input. $T_\epsilon$ is the $\epsilon$ partition of the input variable $X$ w.r.t. the distortion
$$
d_{IB}(x, t) = D(p(y|x)\|p(y|t)]\ge\frac{1}{2\ln 2}\|p(y|x)-p(y|t)\|^2_1
$$
when $p(y|t)=\sum_xp(y|x)p(x|t)$:
$$
\langle d_{IB}\rangle=I(X;Y)-I(T;Y)
$$
minimizing IB distorion we maximize $I(T;Y)$
$$
\epsilon^2<\frac{2^{I(T_\epsilon; X)}+\log\frac 1 \delta}{2m}
$$
$K$ bits of compression of X are like a factor of $2^K$ training examples
There is another information loss:
$$
I(T;Y)\le\hat I_{emp}(T;Y)+O\sqrt{\frac{2^{I(T;Y)}|Y|}{m}}
$$
! The benefit of the hidden layers
More layers take much FEWER training epochs for good generalization. The optimization time depend super-linearly (exponentially?) on the compressed information, delta Ix, for each layer
The relaxation time is exponential to the compression rate:
$$
\Delta t_k\sim\exp(\Delta S_k)
$$
Let the layer compression be: $\Delta S_k = I(X;T_k)-I(X;T_{k-1})$, since $\exp(\sum_k\Delta S_k)>>\sum_k\exp(\Delta S_k)$, exponential boost in relaxation time with K layers!
The inference is done by solving a convex optimization problem about the input. Thus the net should be made ''input convex''.
[[link|https://www.altera.com/en_US/pdfs/literature/solution-sheets/efficient_neural_networks.pdf]] Data is passed from one layer to the next through a mechanism called OpenCL Channels or Pipes, which allow data to move from one kernel to the next without sending data to the external memory. KNL based on socket, no PCIE, 72 cores each with 2 512bit VPU 384g ddr4, 80G/S, 16G MCDRAM CAFFE-MPI
! Deep learning # What is an auto-encoder? Why do we “auto-encode”? Hint: it’s really a misnomer. # What is a Boltzmann Machine? Why a Boltzmann Machine? # Why do we use sigmoid for an output function? Why tanh? Why not cosine? Why any function in particular? # Why are CNNs used primarily in imaging and not so much other tasks? # Explain backpropagation. Seriously. To the target audience described above. # Is it OK to connect from a Layer 4 output back to a Layer 2 input? # Can they derive the back-propagation and weights update? # Extend the above question to non-trivial layers such as convolutional layers, pooling layers, etc. # How to implement dropout? # Their intuition when and why some tricks such as max pooling, ReLU, maxout, etc. work. There are no right answers but it helps to understand their thoughts and research experience. # Can they abstract the forward, backward, update operations as matrix operations, to leverage BLAS and GPU? ! Machine learning # [[What are the best interview questions to evaluate a machine learning researcher?|https://www.quora.com/What-are-the-best-interview-questions-to-evaluate-a-machine-learning-researcher]] # [[Machine Learning Engineer interview questions|http://resources.workable.com/machine-learning-engineer-interview-questions]]
[[link|http://arxiv.org/abs/1602.08007]]
[[video|https://www.youtube.com/watch?v=5rYiDpVFXV8]]
! examples modeling structured text
# Synthetic music notes: constraints that notes belong to a perfect chord.
# Nesting: Matching quotes and one style of text in each scope.
# Long distance xor
# $a^nb^n$
! Model rewriting
Consider a gated RNN which reads $(x_1, \cdots, x_t)$. The activities are
$$
h^{t+1} = sigm(b_i+\rho_{ix}+\sum_jh^t_jw_{jix})
$$
where $x$ is the latest character read, $w_{jix}$ are the weights when reading $x$ and $\rho_{ix}$ are the input weights.
''remark:'' layer normalization?
Removing bias worsen the performance, so we can try the reverse thing. Substituting $w_{jix}\rightarrow w_{jix} + w_{ji}^{base}$
The bias has to balance $n$ weights $w_{ij}$ which change at each training step, rewrite $b = nb$. For backprop, amounts to multiplying the learning rate of b_i by $n^2$. Not compensated by Adagrad.
Training on data $x$ is not equivalent to training on data $1-x$.
! Avoid model rewritings
# identify the best vars
# Use a learning algorithm that is insensitive to model rewriting
Standard answer is to use natural gradient using the inverse Fisher metric. But its algorithmic cost is $O(\#params)^2 dim(output))$ per data point. This is unsuable for larger than 100 neurons.
Invariance often provides:
# Fewer arbitrary choices, no model rewriting, fewer magic numbers
# Often, better performance
# Performance transfer
!! Gradient descent
$\theta\leftarrow\theta-\eta\frac{\partial L}{\partial \theta}$ is not homogenous unless unit of $\eta = $ unit of $\theta^2$.
For a small learning rate $\eta$, this is equivalent (up to $O(\eta^2)$) to
$$
\theta^{k+1} = \underset{\theta}{arg min} L(\theta)+\frac{1}{2\eta}\|\theta-\theta^k\|^2
$$
minimizing loss and not moving too far. This depends on the numerical representation of $\theta$.
The solution is to use a norm depends on what the network does, rather than how $\theta$ is represented. Riemannian metrics are norms
$$
\|\delta\theta\|^2 = \delta\theta^\top M(\theta)\delta\theta
$$
with $M(\theta)$ a well-chosen positive definite matrix depending on $\theta$.
Riemannian gradient descent:
$$
\theta^{k+1} = \theta^k -\eta M(\theta)^{-1}\frac{\partial L(\theta^k)}{\partial \theta}
$$
For natural gradient which defines $\|\theta-\theta^k\|^2$ as the KL divergence, $M(\theta)$ is Fisher information matrix. Two possible strategies:
# Try to scale down the natural gradient
# Backprop a metric on the output, but keeping only the diagonal of Hessian breaks invariance
[[Learn javascript in Y Minutes]]
! Introduction
The i-vector extaction could be seen as a probabilistic compression process that reduces the dimensionality of speech-session super-vectors according to a linear-Gaussian model. The speaker- and channel- dependent super-vector $M_{(s, h)}$ of concatenated GMM means is projected in a low dimensionality space, named Total Variability space, as follows
$$
M_{(s, h)} = m + Tw_{(s, h)}
$$
where $m$ is the mean super-vector of a ''gender-dependent'' UBM, $T$ is called Total Variability matrix and $w_{(s, h)}$ is the resulting i-vector.
! Joint Factor Analysis
A given speaker GMM supervector $s$ can be decomposed as follows:
$$
s = m + Vy + Ux +Dz
$$
where $m$ is a speaker-independent supervector from UBM, $V$ $(U)$ is the eigenvoice (eigenchannel) matrix, $y$ $(x)$ is the speaker factors, with prior $N(0, 1)$, $D$ is the residual matrix and $z$ is the speaker-specific residual factors.
For a 512-mixture GMM-UBM, 300 eigenvoice and 100 eigenchannel components are used.
''Remark'': GMM weights makes the variances of different components vary. How does JFA perform on unnormalized supervectors?
!! Training
[[Factor Analysis and PCA]]
First train $V$ assuming $U$ and $D$ are zeroes, then train $U$ given $V$, and so on. Estimating $V$:
# Accumulate the 0th, 1st and 2nd order sufficient statistics for each speaker $s$ and Gaussian mixture component $c$, $N$, $F$ and $S$.
# Center the 1st and 2nd order statistics to get $\tilde F$, $\tilde S$
# Expand the statistics into matrices, $NN(s) = \text{diag}(N_1(s)I, \dots, N_C(s)I)$, $SS(s) = \text{diag}(\tilde S_1(s),\dots, \tilde S_C)$, $FF(s) = [\tilde F_1(s);\dots;\tilde F_C(s)]$.
# Iterate the estimation of $V$ and $y$ according to the distribution.
Estimation of $U$ is similar except the centering of $F$ is done by $\tilde F = F - N(m+Vy)$ and second order is not used. $D$ with no exception.
[[Deduction|JFA Explaination]]
[[Training Eigenemotion]]
! I-Vector Approach
Training thetotal variability vector $T$ is the same as training $V$ but with all recordings regarded as from different speakers.
!! Channel compensation
Perform LDA then WCCN (techniques empirically determined to perform well) on i-vectors.
The most popular post i-vector process is PLDA.
! ALIZE Example
! DET Curve
[[Probabilistic View of JFA]]
! Training JFA
The joint posterior in a general JFA model is hard to compute. brute force [4] or much more efficiently by the Gauss-Seidel method [17].
!! Gauss-Seidel
It provides a very good approximation to the joint posterior and is guaranteed to find the ''mode'' of the posterior when converge.
The posterior dsitribution of $y(s)$ conditioned on the acoustic observations of the speaker is
$$
N(l^{-1}(s)v^*\Sigma^{-1}\tilde F(s), l^{-1}(s))
$$
where
$$
l(s) = I + v^*\Sigma^{-1}N(s)v
$$
!! Updating UBM variance
TODO: Do I need to update it? during eigenvoice or eigenemotion?
!! Variational Bayes EM training
The Gauss-Seidel method is an instance of variational Bayes. These methods are needed when dealing with general cases of JFA when assignments of frames to Gaussians are treated as hidden variables.
! Why i-vector works
When there is no UBM adaptation, the calculation of joint posterior of hidden variables is straightforward.
The feature vector extracted from one recording rather than multiple recordings as in the JFA case seems to be less reliable. I-vector averaging can be used to merge multiple i-vectors into one.
!! PLDA
This gives the state-of-the-art speaker recognition performance.
The success of i-vector/PLDA cascade can be viewed as one way of decomposing JFA into front-end and back-end models.
''Uncertainty propagation'' incorporates
!! Does LDA/WCCN help with emotions
!! Cosine Similarity vs Likelihood Ratio
! Installing !! With OpenBLAS Kaldi now supports linking against the OpenBLAS library, which is an implementation of BLAS and parts of LAPACK. OpenBLAS also automatically compiles Netlib's implementation of LAPACK, so that it can explort LAPACK in its entirety. OpenBLAS is a fork of the GotoBLAS project (an assembler-heavy implementation of BLAS) which is no longer being maintained. In order to use GotoBLAS you can cd from "src" to "../tools", type "make openblas", then cd to "../src" and give the correct option to the "configure" script to use OpenBLAS (look at the comments at the top of the configure script to find this option). Thanks to Sola Aina for suggesting this and helping us to get this to work. !! Bulding ``` ./configure --openblas-root=/ ``` ! Refs [[kaldi lectures|https://sites.google.com/site/dpovey/kaldi-lectures]]
! Plenary talks * Is Deep Learning the New 42? * [[Learning to learn and compositionality with deep recurrent neural networks|https://www.youtube.com/watch?v=x1kf4Zojtb0]] * A VC view of investing in ML
! Kernels
The kernel function arises as an effortless way to perform an inner product $\langle x, y\rangle$ in a high-dimensional feature space $\mathcal F$. The positive definiteness of the kernel function guarantees the existence of a dot product space $\mathcal F$ and a mapping $\phi:\mathcal X\rightarrow \mathcal F$ such that $k(x, y) = \langle \phi(x), \phi(y)\rangle_{\mathcal F}$. We will refer to $\phi$ and $k$ as a ''feature map'' and a ''kernel function'', respectively. Likewise, we can interpret $k(x, y)$ as a non-linear similarity measure between $x$ and $y$.
For example, let's consider a polynomial feature map $\phi(x) = (x_1^2, x_2^2, x_1x_2, x_2x_1)$ when $x\in\mathbb R^2$. Then, we have
$$
\langle\phi(x), \phi(y)\rangle_{\mathcal F} = x_1^2y_1^2+x_2^2y_2^2+2x_1x_2y_1y_2 = \langle x, y\rangle^2
$$
the new similarity measure is simply the square of the inner product in $\mathcal X$. The equality holds more generally for a $d$-degree polynomial, i.e., $\phi$ maps $x\in\mathbb R^N$ to the vector $\phi(x)$ whose entries are all possible $d$th degree ordered products of the entries of $x$. We have $k(x, y) = \langle x, y\rangle^d$.
For algorithms depend only through the inner product of the data set, we can obtain a non-linear extension of these algorithms by substituting $\langle x, y\rangle$ with $k(x, y)$.
Algorithms capable of operating with kernels include
* the kernel perceptron
* support vector machines (SVM)
* Gaussian processes
* principal components analysis (PCA)
* canonical correlation analysis
* ridge regression
* spectral clustering
* linear adaptive filters
and many others. Any linear model can be turned into a non-linear model by applying the kernel trick to the model: replacing its features (predictors) by a kernel function. The capability of kernel trick also allows easy invention of domain-specific kernel functions.
; p.s.d. kernel
: A function $k: \mathcal X\times\mathcal X\rightarrow \mathbb R$ is a positive semidefinite kernel if it is symmetric, i.e., $k(x, y) = k(y, x)$, and the Gram matrix is p.s.d.
A p.s.d. kernel defines a space of functions from $\mathcal X$ to $\mathbb R$ called a //reproducing kernel Hilbert space// (RKHS) $\mathscr H$.
We may view the kernel evaluation as an inner product in $\mathscr H$ induced by a map from $\mathcal X$ into $\mathscr H$: $x\rightarrow k(x,\cdot)$:
$$
k(x, y) = \langle k(x,\cdot),k(y,\cdot)\rangle_{\mathscr H}
$$
$k(x,\cdot)$ is a high-dimensional representer of $x$.
!! Mercer's theorem
Are wider range of psd kernels.
There is an intrinsic connection between the integral operator $\mathcal T_k$, covariance operator $\mathcal C_{XX}$, and Gram matrix $\mathbf K$. And there is a fundamental connection between Mercer's theorem in functional analysis and Karhunen-Loeve theorem of stochastic processes.
!! Bochner's theorem
It states that a function $f:\mathbf R^n\rightarrow \mathbf C$ is the Fourier tranform of a finite Borel measure iff $f$ is positive definite.
$$
\varphi(x-y) = \int_{\mathbb R^d}e^{\sqrt{-1}\omega^\top(x-y)}d\Lambda(\omega)
$$
The measure $\Lambda$ determines which frequency component occurs in the kernel by putting non-negative power on each frequency $\omega$.
!! Schoenberg's theorem
Radial kernels: $k(x, y) = \varphi (\|x-y\|^2)$ where $\varphi$ is positive definite. Well known radial kernels include Gaussian RBF, mixture-of-Gaussians, inverse multiquadratic and Matern kernel.
; Schoenberg's theoren
: Suppose $f: \mathbf R_+\rightarrow\mathbf R_+$ is continuous. Then, the following are equivalent:
# The function $\mathbf R^n\ni x \rightarrow f(\|x\|_n)$ is positive semi-definite.
# The function $\mathbf R^n\ni t \rightarrow f(\sqrt{t})$ is the Laplace transform of a finite Borel measure on $\mathbf R_+$.
Originally, this theorem was used to describe isometric embeddings of Hilbert spaces.
!! RKHS
A Hilbert space $\mathscr{H}$ of functions is a reproducing kernel Hilbert space if the evaluation functionals are bounded, i.e., $\forall x\in\mathcal X$ there exists some $C>0$ such that
$$
|f(x)|\le C\|f\|_{\mathscr{H}}
$$
This smoothness property ensures that the solution in RKHS obtained from learning algorithms will be well-behaved. For example, we obtain models $\hat f$ in classification and regression problems that is close to the true solution $f$ also generalize well to unseen test data, i.e., avoid overfitting.
A positive definite kernel defines a unique RKHS of functions from $\mathcal X$ to $\mathbb R$, so it is also called reproducing kernels. We define \textbf{canonical feature map} from $\mathcal X$ into $\mathbb R^{\mathcal X}:=\{f:\mathcal X\rightarrow\mathbb R\}$, via
\begin{align}
\phi: \mathcal X&\rightarrow \mathscr H \subset \mathbb R^{\mathcal X}\\
x &\mapsto k(x,\cdot)
\end{align}
An inner product in $\mathscr H$ satisfies the \emph{reproducing property}, i.e., $\forall f\in\mathscr H$ and $x\in\mathcal X$:
$$
f(x) = \langle f, k(x,\cdot)\rangle_{\mathscr H}
$$
Take $k(y,\cdot)$ as $f$, we get $f(x) = k(y, x) = \langle\phi(x), \phi(y)\rangle_{\mathscr H}$. We do not need to know the feature map explicitly, the inner product can be calculated directly from $k(x, y)$.
! Hilbert Space Embedding of Marginal Distributions
The idea of Kernel mean embedding is to extend the feature map $\phi$ to the space of probability distributions by representing each distribution $\mathbb P$ as a mean function
$$
\phi(\mathbb P) = \mu_{\mathbb P} := \int_{\mathcal X}k(x, \cdot)d\mathbb P(x),
$$
where $k: \mathcal X\times\mathcal X\rightarrow \mathbb R$ is a symmetric and positive definite kernel function. We essentially transform the distribution $\mathbb P$ to an element in the feature space $\mathcal F$, which is nothing but a reproducing kernel Hilbert space endowed with the kernel $k$. Most KRHS methods can therefore be extended to probabiity measures.
$$
\int f(t)d\delta_x(t) = \langle f, k(x, \cdot)\rangle_{\mathscr H}
$$
[[Kernel Trick Cheatsheet]]
* Bochner integral:
* Borel measure:
* Characteristic funtion: If a random variable admits a density function, then the characteristic function is its dual, in the sense that each of them is a Fourier transform of the other.
* Gram matrix is positive semidefinite:$\sum_{i, j=1}^nc_ic_jk(x_i, x_j)\ge 0$ for any $n\in\mathbb N$, any choice of $x\in\mathcal X$ and any $c\in\mathbb R$.
* Hilbert space: compelete normed space where the norm is induced by an inner product. Element can be functions.
** standard Euclidean space $\mathbb R^d$ with $\langle x, y\rangle$ being the dot product
** space of square summable sequences $l^2$ with an inner product $\langle x, y\rangle = \sum_{i=1}^\infty x_iy_i$
** space of square-integrable functions $L_2[a, b]$ with inner product $\langle f, y\rangle = \int_a^b f(x)g(x)dx$
* Hausdorff space: where distinct points have disjoint neighbors.
* Moment generating funtion: Essentially the Laplace transformatino of a random variable $X$. $E[e^{tx}]$
* Positive Semidefinite: In linear algebra, a symmetric $n\times n$ real matrix $M$ is said to be positive definite if the scalar $z^{\mathrm {T} }Mz$ is positive for every non-zero column vector $z$ of $n$ real numbers.
[[paper|https://arxiv.org/abs/1606.03126]]
! Access
# Key Hashing: inverted index finds kvs where k shares at least one word with the question (with frequency $< F = 1000$ to ignore stop words). The retrieval scheme can be complicated.
# Key Addressing: should allow approximate association. aka ''content-based addressing'': $p_{h_i} = \text{Softmax}(A\Phi_X(x)\cdot A\Phi_K(k_{h_i}))$, comparing the question to each key.
# Value Reading: weighted sum of mem values using p_{h_i}$.
! Code
why base model is called `mlp`?
[[Approximating KL between GMMs]]
! Model
We imagine that data points from a training set are presented sequentially, we can consider that the posterior distribution after the $N$-th point becomes the prior for the next iteration.
With the Bayesian approach, the background information of an observer is captured by his //prior// probability distribution $P_{t-1}(M) = P(M|\mathcal O_{t-1})$ given previous inputs $\mathcal O_{t-1}$ over the models $M$. The effect of a new data $o_t$ on the observer is to change the prior into the //posterior// distribution $P_t(M) = P(M|\mathcal O_t)$ where $\mathcal O_t = o_t\cup\mathcal O_{t-1}$.
!! Acoustic Surprise
The surprise $S(o_t)$ is measured as the [[Kullback-Leibler divergence]] $D_{\text{KL}}$ between $P_{t-1}$ and $P_t$,
$$
S(o_t) \doteq D_{\text{KL}}(P_{t-1}||P_{t}) = E_{p_{t-1}}\log\frac{P_{t-1}}{P_{t}}.
$$
And the single model surprise of $M$ is given by
$$
S(o_t, M) = \log\frac{P_{t-1}}{P_{t}}.
$$
The unit of surprise, representing a 2-fold variation between $P_t$ and $P_{t-1}$ is called a ''wow''. Under this definition, the surprise can be understood as
$$
H(P_t, P_{t-1}) - H(P_{t-1})
$$
where $H(P,Q)$ is the cross entropy of $P$ and $Q$, and $H(P)$ is the entropy of $P$.
KL-dviergence is not symmetric, the first parameter is often referred to as ''true'' and the second ''approximate''. [baldi10bits] and [schuerte13wow] use opposite distribution orders which is somehow bad.
Now the problem is how to define these two distributions. A convenient way to represent posteriors of certain distributions (data generating processes) is [[conjugate prior|Conjugate Prior]].
!!! Poisson [itti06bayesian]
Suppose that data $x_1, \dots, x_n$ are independent and identically distributed from a Poisson process where $\mu$, the mean, is unknown. The value of each $x_i$ is an integer greater than or equal to zero. For data from a Poisson process, the likelihood function, $L(\mu|x_1,\dots,x_n)$, with respect to $\mu$, is
$$
L(\mu|x_1,\dots,x_n)\propto \mu^{\sum x_i}\exp(-n\mu), \qquad x_i>0.
$$
The sufficient statistics are $n$, the number of data points, and $\sum x_i$ the sum of the data. This likelihood factor is proportional to a gamma distribution of $\mu$. The conjugate prior, $g(\cdot)$, is a gamma distribution with hyperparameters $k, \theta > 0$,
$$
g(\mu|k,\theta)=\frac{\mu^{k-1}\exp\frac{-\mu}{\theta}}{\Gamma(k)\theta^k},\qquad \mu>0.
$$
and the posterior is also a gamma $\mu \sim \Gamma(\mu|k',\theta')$ where the updated hyperparameters are
$$
k' = k+\sum x_i, \qquad \theta' = \frac {\theta}{1+n}.
$$
The KL-divergence of $\Gamma(k_p, \theta_p)$ from $\Gamma(k_q,\theta_q)$ is given by
$$
D_{\mathrm{KL}}(k_p,\theta_p; k_q, \theta_q) = (k_p-k_q)\psi(k_p) - \log\Gamma(k_p) + \log\Gamma(k_q) + k_q(\log \theta_q - \log \theta_p) + k_p\frac{\theta_p - \theta_q}{\theta_q}
$$
where $\psi$ is the digamma function
$$
\psi(x) =\frac{d}{dx} \ln{\Gamma(x)}= \frac{\Gamma'(x)}{\Gamma(x)}.
$$
It is shown (applying [[Stirling's formula|Stirling's Formula]]) that as $N\rightarrow\infty$, $S$ grows linearly according to with $n$ the number of data
$$
S(o_n) \approx n\left(k\theta - \bar\mu (1-\log\bar\mu+\psi(k)+\log\theta)\right).
$$
As the input data accumulates, it is believed old data is not as important. So a decay factor $0<\zeta<1$ is used in [schuerte13wow]
$$
k'_n = \zeta k'_{n-1}+ x_n, \qquad \frac{1}{\theta_n'} = \zeta\frac{1}{\theta'_{n-1}}+\frac 1\theta.
$$
!!! Generalization [baldi10bits]
The result can be easily extended onto [[exponential family|Exponential Family]], which all have conjugate prior and thus easier to compute the posterior. The likelihood of a observation from an exponential family with parameter $\boldsymbol \eta$ is
$$
P(x|\boldsymbol \eta) = h(x) g(\boldsymbol\eta) \exp\left(\boldsymbol\eta^{\rm T} \mathbf{T}(x)\right)
$$
Then, for data $\mathbf{X} = (x_1,\ldots,x_n)$, the likelihood is computed as follows:
$$
p(\mathbf{X}|\boldsymbol\eta) =\left(\prod_{i=1}^n h(x_i) \right) g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T}\sum_{i=1}^n \mathbf{T}(x_i) \right)
$$
Then, for the above conjugate prior:
$$
\begin{align}p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) &= f(\boldsymbol\chi,\nu) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \propto g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi)\end{align}
$$
We can then compute the posterior as follows:
$$
\begin{align}
p(\boldsymbol\eta|\mathbf{X},\boldsymbol\chi,\nu)& \propto p(\mathbf{X}|\boldsymbol\eta) p_\pi(\boldsymbol\eta|\boldsymbol\chi,\nu) \\
&= \left(\prod_{i=1}^n h(x_i) \right) g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T} \sum_{i=1}^n \mathbf{T}(x_i)\right)
f(\boldsymbol\chi,\nu) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \\
&\propto g(\boldsymbol\eta)^n \exp\left(\boldsymbol\eta^{\rm T}\sum_{i=1}^n \mathbf{T}(x_i)\right) g(\boldsymbol\eta)^\nu \exp(\boldsymbol\eta^{\rm T} \boldsymbol\chi) \\
&\propto g(\boldsymbol\eta)^{\nu + n} \exp\left(\boldsymbol\eta^{\rm T} \left(\boldsymbol\chi + \sum_{i=1}^n \mathbf{T}(x_i)\right)\right)
\end{align}
$$
Compute the surprise yields
$$
S(o_t|\boldsymbol\eta(M)) = \log g(\boldsymbol\eta)+E_{p_t}\boldsymbol\eta^{\rm T}\mathbf T(o_t)
$$
Examples given in [baldi10bits] are
* Discrete data
** Multinomial
** Poisson
* Gaussian
** Unknown mean/known variance
** Known mean/unknown variance
** Unknown mean/unknown variance
!!! Gaussian [schuerte13wow]
If we model the data as a Gaussian $P\sim\mathcal G(\mu, \Sigma)$, we can get a closed form of saliency.
$$
\begin{align*}
D_{\text{KL}} &= \int \frac 1 2\left[\log\frac{|\Sigma_2|}{|\Sigma_1|} - (x-\mu_1)^T\Sigma_1^{-1}(x-\mu_1)+(x-\mu_2)^T\Sigma_2^{-1}(x-\mu_2)\right]P_t(x)dx\\
&= \frac 1 2\left[\log\frac{|\Sigma_2|}{|\Sigma_1|} - E_{P_t}[(x-\mu_1)^T\Sigma_1^{-1}(x-\mu_1)]+E_{P_t}[(x-\mu_2)^T\Sigma_2^{-1}(x-\mu_2)]\right]\\
&= \frac 1 2\left[\log\frac{|\Sigma_2|}{|\Sigma_1|} - \text{tr}(I_d)+\text{tr}(\Sigma_2^{-1}\Sigma_1)+(\mu_1-\mu_2)^T\Sigma_2^{-1}(mu_1-\mu_2)\right]\\
\end{align*}
$$
!! Bits (Entropy) and Wows (Surprise)
* For a uniform prior model with $P(o_t)\ll 1$
** Entropy: $\underset{p\rightarrow0+}{\text{lim}}p\log p = 0$
** Surprise: $0$, because the posterior does not change.
* For a very unlikely data
** Entropy: cannot exceed 1
** Surprise: can be quite large ($\log N$, where $N$ is #models)
! Neural Network Implementation
* Input: dyadic image pyramids as feature maps
* Layer: 5 chained cascade of surprise detectors at every pixel in every feature map
** Temporal $SL$: Gaussian/Poisson Likelihood
** Addtion $SS$: spatial surprises
** $S \leftarrow S(SL+SS/20)^{1/3}$ (from LMS)
* Application in eye movements: CRCNS eye-tracking
! In Speech
!! Frequency Spectrogram [schuerte13wow]
The squared magnitude spectrum of:
# Short-time Fourier transform (STFT)
# Short-time cosine transform (STCT)
# Modified discrete cosine transform (MDCT)
$G(t,\omega) = |F(t,\omega)|^2$ is used to characterize the audio features.
!! Speaker diarization task
This can be used to employ a segmentation algorithm ([[Changepoint algorithms]])
KL divergence is also known as information divergence, information gain, relative entropy.
! Motivation
The motivation for Kullback and Leibler's work was to provide a rigorious definition of "information" in relation to Fisher's [[sufficient statistics]]. [[[p79 burnham02model|Model selection and multimodel inference]]]
In information theory, the [[Kraft–McMillan theorem]] establishes that any directly decodable coding scheme for coding a message to identify one value $x_i$ out of a set of possibilities $X$ can be seen as representing an implicit probability distribution $q(x_i)=2^{-l_i}$ over $X$, where $l_i$ is the length of the code for $x_i$ in bits. Therefore, KL divergence can be interpreted as the expected extra message-length per datum that must be communicated if a code that is optimal for a given (wrong) distribution Q is used, compared to using a code based on the true distribution $P$.
$$
\begin{matrix}
D_{\mathrm{KL}}(P\|Q) & = -& \sum_x p(x) \log q(x)& + & \sum_x p(x) \log p(x) \\
& = & H(P,Q) & - & H(P)\, \!
\end{matrix}
$$
where $H(P,Q)$ is called the cross entropy of $P$ and $Q$, and $H(P)$ is the entropy of $P$ [[[wikipedia|http://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence#Motivation]]]. This notation $D_{\mathrm{KL}}(P\|Q)$ denotes the ''information lost when $Q$ is used to approximate $P$''. It is the logical basis for model selection in conjunction with likelihood inference. [[[p78 burnham02model|Model selection and multimodel inference]]]
! Implementations [[AWD-LSTM|https://github.com/salesforce/awd-lstm-lm]] ! Talks [[Language Modelling - Phil Blunsom DLSS 2017]] ! Datasets * NIST Hub5 2000 ** Switchboard (SWB) ** CallHome (CH) ! Bibs * Exploring Asymmetric Encoder-Decoder Structure for Context-based Sentence Representation Learning ** An unsupervised RNN-CNN hybrid feature extractor that is not very efficient or powerful, should know someone has tried this
! [[Part I|http://videolectures.net/deeplearning2017_blunsom_language_understanding/]]
Much of NLP (e.g. Translation, QA, Dialogue) can be structured as (conditional) language modelling. The simple objective of modelling the next word contains much of the complexity of nature language understanding
<<<
@@color:red;Alice@@ went to the @@color:blue;beach@@. @@color:blue;There@@ @@color:red;she@@ build a ...
<<<
Models can be evaluated with cross entropy, a measure of how many bits are needed to encode text with our model：
$$
H(w^N_1) = -\frac1N\log_2p(w^N_1)
$$
or perplexity, a measure of how surprised our model is on seeing each word:
$$
\text{perplexity}(w_1^N) = 2^{H(w_1^N)}
$$
* Count based N-Gram Models
** Back-off to lower interpolation: if never observe the trigrams, try bigrams
** Heap's Law: smoothing techniques match the empirical distribution of languge
** hard to capture long dependencies, semantic correlations and morphological regualrities.
* Neural N-Gram Language Models
* Recurrent Neural Network Language Models
* Encoder-Decoder Models and Machine Translation
Truncated BPTT is efficient for mini-batching as all sequences have length $T$.
! Part II
Deep RNN Models:
* Stack and skip connections
* Deep-in-time: Recurrent Highway Networks
Scaling large vocabularies, when the softmax becomes hard to compute
* use the neural LM for the most frequent words, and a traditional ngram LM for the rest. This nullifies the neural LM's main advantage
* batch local short-lists: choose a subset of the vocabulary for the segment of the data. [[On Using Very Large Target Vocabulary for Neural Machine Translation|https://arxiv.org/abs/1412.2007]]
* approximate the gradient/change the objective: maximize likelihood by making the log partition function an independent parameter $c$: $\hat p_n = \exp(Wh_n+b)\times\exp(c)$. Mnih and Teh use Noise Contrastive Estimation, to distinguish data from k samples from a noise distribution:
$$
p(Data=1|\hat p_n) = \frac{\hat p_n}{\hat p_n+kp_{noise}(w_n)}
$$
Now parametrising the log partition function as $c$ does not degenerate. This is effective for speeding up training, but has no impact on testing time.
* Factorise the output vocabulary
** [[A scalable hierarchical distributed language model|https://papers.nips.cc/paper/3583-a-scalable-hierarchical-distributed-language-model]]
** [[Efficient softmax approximation for GPUs|https://arxiv.org/abs/1609.04309]]
[[Programming category]] ! Tutorials * [[Learn X in Y Minutes]] ! Functional Programming !! Languages * [[clojure]] * Elixir * Scala !! Concepts * Side effects ! Scientific Computing * [[MATLAB]] * [[Python]]
! Introduction AKA Saddle-point approximation, delta-method Azevedo-Filho & Shachter 1994 reviews Laplace's method results (univariate and multivariate) and presents a detailed example showing the method used in parameter estimation and probabilistic inference under a Bayesian perspective. Laplace's method is applied to a meta-analysis problem from the medical domain, involving experimental data, and compared to other techniques. !
[[link|http://arxiv.org/abs/1603.06744]] [[Hugo's review|https://www.evernote.com/shard/s189/sh/1fe4a407-3aa0-4ad2-8340-e58fceeb1b71/baa114c50096b41f341a54d478af8160]] ! Structured Attention * Attention before LSTM * Learn word representation with C2W: don't have to deal with OOV * Choose where to copy from with CRF * Code compression while maintaining structure
! Model Zoo |>|!Direct/Linear|<| |! |!Parametric |!Non-parametric | |!Discrete |<li>[[Hidden Markov Model]]</li><li>Discrete LVM</li><li>Sparse LVM</li> |<li>Indian Buffet Process</li><li>[[Dirichlet Process Mixtures]]</li> | |!Continuous |<li>PCA</li><li>[[Factor Analysis]]</li><li>Independent Component Analysis</li><li>Gaussian LDS</li><li>Latent Gauss Field</li> |<li>Gaussian Process LVM</li> | |>|!Deep|<| |! |!Parametric |!Non-parametric | |!Discrete |<li>Sigmoid Belief Net</li><li>Deep Auto-Regressive Networks</li> |<li>Cascaded Indian Buffet Process</li><li>[[Hierarchical Dirichlet Process|HDP-HMM Diarization]]</li> | |!Continuous |<li>Nonlinear Factor Analysis</li><li>Nonlinear Gaussian Belief Network</li><li>Deep Latent Gaussian ([[VAE|Variational Autoencoder]], [[DRAW|Variational Inference for Generative Models]])</li> |<li>Deep Gaussian Process</li><li>Recurrent Gaussian Process</li><li>GP State Space Model</li> | !! Conjugate * Bayesian mixture models * Times series models (HMM, linear dynamic systems） * Factorial models * Matrix factorization (factor analysis, PCA, CCA) * [[Dirichlet Process Mixtures]], HDPs * Multilevel regression (linear, probit, Poisson) * Stochastic block models * Mixed-membership models (LDA and some variants) !! Nonconjugate * Nonlinear Time series models * Deep latent Gaussian models * Models with attention (DRAW) * Generalized linear models (Poisson regression) * Stochastic volatility models * Discrete choice models * Bayesian neural networks * Deep exponential families (sparse Gamma or Poison) * Correlated topic models (nonparametric variants) * Sigmoid belief network ! Remarks @@color:#859900; * Easy sampling * Easy way to include hierarchy and depth * Easy to encode structure * Avoids order dependency assumptions: marginalisation induces dependencies. * Provide compression and representation * Scoring, model comparison and selection possible using the marginalised likelihood @@ @@color:red; * Inversion process to determin latents corresponding to an input is difficult to generalize * Difficult to compute marginalised likelihood requiring approximations * Not easy to specify rich approximations for latent posterior distribution @@ ! Learning Principles
[[LaTeX Snippets]] ! Tools * [[Detexify|http://detexify.kirelabs.org/classify.html]] * In Browser: [[MathJax]]
Inline separation
```
P [N(t+ \tau) - N(t) = k] = \frac{e^{-\lambda \tau} (\lambda \tau)^k}{k!} \qquad k= 0,1,\ldots,
```
$$
P [N(t+ \tau) - N(t) = k] = \frac{e^{-\lambda \tau} (\lambda \tau)^k}{k!} \qquad k= 0,1,\ldots,
$$
equation array
```
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \mbox{if } m_\pi(x)>kf(x|\theta_1),\\
0 & \mbox{otherwise}.
\end{array}\right.
```
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \mbox{if } m_\pi(x)>kf(x|\theta_1),\\
0 & \mbox{otherwise}.
\end{array}\right.
$$
Get the code: [[learnclojure.clj|http://learnxinyminutes.com/docs/files/learnclojure.clj]]
```
; Comments start with semicolons.
; Clojure is written in "forms", which are just
; lists of things inside parentheses, separated by whitespace.
;
; The clojure reader assumes that the first thing is a
; function or macro to call, and the rest are arguments.
; The first call in a file should be ns, to set the namespace
(ns learnclojure)
; More basic examples:
; str will create a string out of all its arguments
(str "Hello" " " "World") ; => "Hello World"
; Math is straightforward
(+ 1 1) ; => 2
(- 2 1) ; => 1
(* 1 2) ; => 2
(/ 2 1) ; => 2
; Equality is =
(= 1 1) ; => true
(= 2 1) ; => false
; You need not for logic, too
(not true) ; => false
; Nesting forms works as you expect
(+ 1 (- 3 2)) ; = 1 + (3 - 2) => 2
; Types
;;;;;;;;;;;;;
; Clojure uses Java's object types for booleans, strings and numbers.
; Use `class` to inspect them.
(class 1) ; Integer literals are java.lang.Long by default
(class 1.); Float literals are java.lang.Double
(class ""); Strings always double-quoted, and are java.lang.String
(class false) ; Booleans are java.lang.Boolean
(class nil); The "null" value is called nil
; If you want to create a literal list of data, use ' to stop it from
; being evaluated
'(+ 1 2) ; => (+ 1 2)
; (shorthand for (quote (+ 1 2)))
; You can eval a quoted list
(eval '(+ 1 2)) ; => 3
; Collections & Sequences
;;;;;;;;;;;;;;;;;;;
; Lists are linked-list data structures, while Vectors are array-backed.
; Vectors and Lists are java classes too!
(class [1 2 3]); => clojure.lang.PersistentVector
(class '(1 2 3)); => clojure.lang.PersistentList
; A list would be written as just (1 2 3), but we have to quote
; it to stop the reader thinking it's a function.
; Also, (list 1 2 3) is the same as '(1 2 3)
; "Collections" are just groups of data
; Both lists and vectors are collections:
(coll? '(1 2 3)) ; => true
(coll? [1 2 3]) ; => true
; "Sequences" (seqs) are abstract descriptions of lists of data.
; Only lists are seqs.
(seq? '(1 2 3)) ; => true
(seq? [1 2 3]) ; => false
; A seq need only provide an entry when it is accessed.
; So, seqs which can be lazy -- they can define infinite series:
(range 4) ; => (0 1 2 3)
(range) ; => (0 1 2 3 4 ...) (an infinite series)
(take 4 (range)) ; (0 1 2 3)
; Use cons to add an item to the beginning of a list or vector
(cons 4 [1 2 3]) ; => (4 1 2 3)
(cons 4 '(1 2 3)) ; => (4 1 2 3)
; Conj will add an item to a collection in the most efficient way.
; For lists, they insert at the beginning. For vectors, they insert at the end.
(conj [1 2 3] 4) ; => [1 2 3 4]
(conj '(1 2 3) 4) ; => (4 1 2 3)
; Use concat to add lists or vectors together
(concat [1 2] '(3 4)) ; => (1 2 3 4)
; Use filter, map to interact with collections
(map inc [1 2 3]) ; => (2 3 4)
(filter even? [1 2 3]) ; => (2)
; Use reduce to reduce them
(reduce + [1 2 3 4])
; = (+ (+ (+ 1 2) 3) 4)
; => 10
; Reduce can take an initial-value argument too
(reduce conj [] '(3 2 1))
; = (conj (conj (conj [] 3) 2) 1)
; => [3 2 1]
; Functions
;;;;;;;;;;;;;;;;;;;;;
; Use fn to create new functions. A function always returns
; its last statement.
(fn [] "Hello World") ; => fn
; (You need extra parens to call it)
((fn [] "Hello World")) ; => "Hello World"
; You can create a var using def
(def x 1)
x ; => 1
; Assign a function to a var
(def hello-world (fn [] "Hello World"))
(hello-world) ; => "Hello World"
; You can shorten this process by using defn
(defn hello-world [] "Hello World")
; The [] is the list of arguments for the function.
(defn hello [name]
(str "Hello " name))
(hello "Steve") ; => "Hello Steve"
; You can also use this shorthand to create functions:
(def hello2 #(str "Hello " %1))
(hello2 "Fanny") ; => "Hello Fanny"
; You can have multi-variadic functions, too
(defn hello3
([] "Hello World")
([name] (str "Hello " name)))
(hello3 "Jake") ; => "Hello Jake"
(hello3) ; => "Hello World"
; Functions can pack extra arguments up in a seq for you
(defn count-args [& args]
(str "You passed " (count args) " args: " args))
(count-args 1 2 3) ; => "You passed 3 args: (1 2 3)"
; You can mix regular and packed arguments
(defn hello-count [name & args]
(str "Hello " name ", you passed " (count args) " extra args"))
(hello-count "Finn" 1 2 3)
; => "Hello Finn, you passed 3 extra args"
; Maps
;;;;;;;;;;
; Hash maps and array maps share an interface. Hash maps have faster lookups
; but don't retain key order.
(class {:a 1 :b 2 :c 3}) ; => clojure.lang.PersistentArrayMap
(class (hash-map :a 1 :b 2 :c 3)) ; => clojure.lang.PersistentHashMap
; Arraymaps will automatically become hashmaps through most operations
; if they get big enough, so you don't need to worry.
; Maps can use any hashable type as a key, but usually keywords are best
; Keywords are like strings with some efficiency bonuses
(class :a) ; => clojure.lang.Keyword
(def stringmap {"a" 1, "b" 2, "c" 3})
stringmap ; => {"a" 1, "b" 2, "c" 3}
(def keymap {:a 1, :b 2, :c 3})
keymap ; => {:a 1, :c 3, :b 2}
; By the way, commas are always treated as whitespace and do nothing.
; Retrieve a value from a map by calling it as a function
(stringmap "a") ; => 1
(keymap :a) ; => 1
; Keywords can be used to retrieve their value from a map, too!
(:b keymap) ; => 2
; Don't try this with strings.
;("a" stringmap)
; => Exception: java.lang.String cannot be cast to clojure.lang.IFn
; Retrieving a non-present key returns nil
(stringmap "d") ; => nil
; Use assoc to add new keys to hash-maps
(def newkeymap (assoc keymap :d 4))
newkeymap ; => {:a 1, :b 2, :c 3, :d 4}
; But remember, clojure types are immutable!
keymap ; => {:a 1, :b 2, :c 3}
; Use dissoc to remove keys
(dissoc keymap :a :b) ; => {:c 3}
; Sets
;;;;;;
(class #{1 2 3}) ; => clojure.lang.PersistentHashSet
(set [1 2 3 1 2 3 3 2 1 3 2 1]) ; => #{1 2 3}
; Add a member with conj
(conj #{1 2 3} 4) ; => #{1 2 3 4}
; Remove one with disj
(disj #{1 2 3} 1) ; => #{2 3}
; Test for existence by using the set as a function:
(#{1 2 3} 1) ; => 1
(#{1 2 3} 4) ; => nil
; There are more functions in the clojure.sets namespace.
; Useful forms
;;;;;;;;;;;;;;;;;
; Logic constructs in clojure are just macros, and look like
; everything else
(if false "a" "b") ; => "b"
(if false "a") ; => nil
; Use let to create temporary bindings
(let [a 1 b 2]
(> a b)) ; => false
; Group statements together with do
(do
(print "Hello")
"World") ; => "World" (prints "Hello")
; Functions have an implicit do
(defn print-and-say-hello [name]
(print "Saying hello to " name)
(str "Hello " name))
(print-and-say-hello "Jeff") ;=> "Hello Jeff" (prints "Saying hello to Jeff")
; So does let
(let [name "Urkel"]
(print "Saying hello to " name)
(str "Hello " name)) ; => "Hello Urkel" (prints "Saying hello to Urkel")
; Modules
;;;;;;;;;;;;;;;
; Use "use" to get all functions from the module
(use 'clojure.set)
; Now we can use set operations
(intersection #{1 2 3} #{2 3 4}) ; => #{2 3}
(difference #{1 2 3} #{2 3 4}) ; => #{1}
; You can choose a subset of functions to import, too
(use '[clojure.set :only [intersection]])
; Use require to import a module
(require 'clojure.string)
; Use / to call functions from a module
; Here, the module is clojure.string and the function is blank?
(clojure.string/blank? "") ; => true
; You can give a module a shorter name on import
(require '[clojure.string :as str])
(str/replace "This is a test." #"[a-o]" str/upper-case) ; => "THIs Is A tEst."
; (#"" denotes a regular expression literal)
; You can use require (and use, but don't) from a namespace using :require.
; You don't need to quote your modules if you do it this way.
(ns test
(:require
[clojure.string :as str]
[clojure.set :as set]))
; Java
;;;;;;;;;;;;;;;;;
; Java has a huge and useful standard library, so
; you'll want to learn how to get at it.
; Use import to load a java module
(import java.util.Date)
; You can import from an ns too.
(ns test
(:import java.util.Date
java.util.Calendar))
; Use the class name with a "." at the end to make a new instance
(Date.) ; <a date object>
; Use . to call methods. Or, use the ".method" shortcut
(. (Date.) getTime) ; <a timestamp>
(.getTime (Date.)) ; exactly the same thing.
; Use / to call static methods
(System/currentTimeMillis) ; <a timestamp> (system is always present)
; Use doto to make dealing with (mutable) classes more tolerable
(import java.util.Calendar)
(doto (Calendar/getInstance)
(.set 2000 1 1 0 0 0)
.getTime) ; => A Date. set to 2000-01-01 00:00:00
; STM
;;;;;;;;;;;;;;;;;
; Software Transactional Memory is the mechanism clojure uses to handle
; persistent state. There are a few constructs in clojure that use this.
; An atom is the simplest. Pass it an initial value
(def my-atom (atom {}))
; Update an atom with swap!.
; swap! takes a function and calls it with the current value of the atom
; as the first argument, and any trailing arguments as the second
(swap! my-atom assoc :a 1) ; Sets my-atom to the result of (assoc {} :a 1)
(swap! my-atom assoc :b 2) ; Sets my-atom to the result of (assoc {:a 1} :b 2)
; Use '@' to dereference the atom and get the value
my-atom ;=> Atom<#...> (Returns the Atom object)
@my-atom ; => {:a 1 :b 2}
; Here's a simple counter using an atom
(def counter (atom 0))
(defn inc-counter []
(swap! counter inc))
(inc-counter)
(inc-counter)
(inc-counter)
(inc-counter)
(inc-counter)
@counter ; => 5
; Other STM constructs are refs and agents.
; Refs: http://clojure.org/refs
; Agents: http://clojure.org/agents
```
Get the code: [[javascript.js|http://learnxinyminutes.com/docs/files/javascript.js]]
```javascript
// Comments are like C. Single-line comments start with two slashes,
/* and multiline comments start with slash-star
and end with star-slash */
// Statements can be terminated by ;
doStuff();
// ... but they don't have to be, as semicolons are automatically inserted
// wherever there's a newline, except in certain cases.
doStuff()
// Because those cases can cause unexpected results, we'll keep on using
// semicolons in this guide.
///////////////////////////////////
// 1. Numbers, Strings and Operators
// JavaScript has one number type (which is a 64-bit IEEE 754 double).
// As with Lua, don't freak out about the lack of ints: doubles have a 52-bit
// mantissa, which is enough to store integers up to about 9✕10¹⁵ precisely.
3; // = 3
1.5; // = 1.5
// All the basic arithmetic works as you'd expect.
1 + 1; // = 2
8 - 1; // = 7
10 * 2; // = 20
35 / 5; // = 7
// Including uneven division.
5 / 2; // = 2.5
// Bitwise operations also work; when you perform a bitwise operation your float
// is converted to a signed int *up to* 32 bits.
1 << 2; // = 4
// Precedence is enforced with parentheses.
(1 + 3) * 2; // = 8
// There are three special not-a-real-number values:
Infinity; // result of e.g. 1/0
-Infinity; // result of e.g. -1/0
NaN; // result of e.g. 0/0
// There's also a boolean type.
true;
false;
// Strings are created with ' or ".
'abc';
"Hello, world";
// Negation uses the ! symbol
!true; // = false
!false; // = true
// Equality is ==
1 == 1; // = true
2 == 1; // = false
// Inequality is !=
1 != 1; // = false
2 != 1; // = true
// More comparisons
1 < 10; // = true
1 > 10; // = false
2 <= 2; // = true
2 >= 2; // = true
// Strings are concatenated with +
"Hello " + "world!"; // = "Hello world!"
// and are compared with < and >
"a" < "b"; // = true
// Type coercion is performed for comparisons...
"5" == 5; // = true
// ...unless you use ===
"5" === 5; // = false
// You can access characters in a string with charAt
"This is a string".charAt(0); // = 'T'
// ...or use substring to get larger pieces
"Hello world".substring(0, 5); // = "Hello"
// length is a property, so don't use ()
"Hello".length; // = 5
// There's also null and undefined
null; // used to indicate a deliberate non-value
undefined; // used to indicate a value is not currently present (although
// undefined is actually a value itself)
// false, null, undefined, NaN, 0 and "" are falsy; everything else is truthy.
// Note that 0 is falsy and "0" is truthy, even though 0 == "0".
///////////////////////////////////
// 2. Variables, Arrays and Objects
// Variables are declared with the var keyword. JavaScript is dynamically typed,
// so you don't need to specify type. Assignment uses a single = character.
var someVar = 5;
// if you leave the var keyword off, you won't get an error...
someOtherVar = 10;
// ...but your variable will be created in the global scope, not in the scope
// you defined it in.
// Variables declared without being assigned to are set to undefined.
var someThirdVar; // = undefined
// There's shorthand for performing math operations on variables:
someVar += 5; // equivalent to someVar = someVar + 5; someVar is 10 now
someVar *= 10; // now someVar is 100
// and an even-shorter-hand for adding or subtracting 1
someVar++; // now someVar is 101
someVar--; // back to 100
// Arrays are ordered lists of values, of any type.
var myArray = ["Hello", 45, true];
// Their members can be accessed using the square-brackets subscript syntax.
// Array indices start at zero.
myArray[1]; // = 45
// Arrays are mutable and of variable length.
myArray.push("World");
myArray.length; // = 4
// Add/Modify at specific index
myArray[3] = "Hello";
// JavaScript's objects are equivalent to 'dictionaries' or 'maps' in other
// languages: an unordered collection of key-value pairs.
var myObj = {key1: "Hello", key2: "World"};
// Keys are strings, but quotes aren't required if they're a valid
// JavaScript identifier. Values can be any type.
var myObj = {myKey: "myValue", "my other key": 4};
// Object attributes can also be accessed using the subscript syntax,
myObj["my other key"]; // = 4
// ... or using the dot syntax, provided the key is a valid identifier.
myObj.myKey; // = "myValue"
// Objects are mutable; values can be changed and new keys added.
myObj.myThirdKey = true;
// If you try to access a value that's not yet set, you'll get undefined.
myObj.myFourthKey; // = undefined
///////////////////////////////////
// 3. Logic and Control Structures
// The syntax for this section is almost identical to Java's.
// The if structure works as you'd expect.
var count = 1;
if (count == 3){
// evaluated if count is 3
} else if (count == 4){
// evaluated if count is 4
} else {
// evaluated if it's not either 3 or 4
}
// As does while.
while (true){
// An infinite loop!
}
// Do-while loops are like while loops, except they always run at least once.
var input
do {
input = getInput();
} while (!isValid(input))
// the for loop is the same as C and Java:
// initialisation; continue condition; iteration.
for (var i = 0; i < 5; i++){
// will run 5 times
}
// && is logical and, || is logical or
if (house.size == "big" && house.colour == "blue"){
house.contains = "bear";
}
if (colour == "red" || colour == "blue"){
// colour is either red or blue
}
// && and || "short circuit", which is useful for setting default values.
var name = otherName || "default";
// switch statement checks for equality with ===
// use 'break' after each case
// or the cases after the correct one will be executed too.
grade = 'B';
switch (grade) {
case 'A':
console.log("Great job");
break;
case 'B':
console.log("OK job");
break;
case 'C':
console.log("You can do better");
break;
default:
console.log("Oy vey");
break;
}
///////////////////////////////////
// 4. Functions, Scope and Closures
// JavaScript functions are declared with the function keyword.
function myFunction(thing){
return thing.toUpperCase();
}
myFunction("foo"); // = "FOO"
// Note that the value to be returned must start on the same line as the
// 'return' keyword, otherwise you'll always return 'undefined' due to
// automatic semicolon insertion. Watch out for this when using Allman style.
function myFunction()
{
return // <- semicolon automatically inserted here
{
thisIsAn: 'object literal'
}
}
myFunction(); // = undefined
// JavaScript functions are first class objects, so they can be reassigned to
// different variable names and passed to other functions as arguments - for
// example, when supplying an event handler:
function myFunction(){
// this code will be called in 5 seconds' time
}
setTimeout(myFunction, 5000);
// Note: setTimeout isn't part of the JS language, but is provided by browsers
// and Node.js.
// Function objects don't even have to be declared with a name - you can write
// an anonymous function definition directly into the arguments of another.
setTimeout(function(){
// this code will be called in 5 seconds' time
}, 5000);
// JavaScript has function scope; functions get their own scope but other blocks
// do not.
if (true){
var i = 5;
}
i; // = 5 - not undefined as you'd expect in a block-scoped language
// This has led to a common pattern of "immediately-executing anonymous
// functions", which prevent temporary variables from leaking into the global
// scope.
(function(){
var temporary = 5;
// We can access the global scope by assiging to the 'global object', which
// in a web browser is always 'window'. The global object may have a
// different name in non-browser environments such as Node.js.
window.permanent = 10;
})();
temporary; // raises ReferenceError
permanent; // = 10
// One of JavaScript's most powerful features is closures. If a function is
// defined inside another function, the inner function has access to all the
// outer function's variables, even after the outer function exits.
function sayHelloInFiveSeconds(name){
var prompt = "Hello, " + name + "!";
// Inner functions are put in the local scope by default, as if they were
// declared with 'var'.
function inner(){
alert(prompt);
}
setTimeout(inner, 5000);
// setTimeout is asynchronous, so the sayHelloInFiveSeconds function will
// exit immediately, and setTimeout will call inner afterwards. However,
// because inner is "closed over" sayHelloInFiveSeconds, inner still has
// access to the 'prompt' variable when it is finally called.
}
sayHelloInFiveSeconds("Adam"); // will open a popup with "Hello, Adam!" in 5s
///////////////////////////////////
// 5. More about Objects; Constructors and Prototypes
// Objects can contain functions.
var myObj = {
myFunc: function(){
return "Hello world!";
}
};
myObj.myFunc(); // = "Hello world!"
// When functions attached to an object are called, they can access the object
// they're attached to using the this keyword.
myObj = {
myString: "Hello world!",
myFunc: function(){
return this.myString;
}
};
myObj.myFunc(); // = "Hello world!"
// What this is set to has to do with how the function is called, not where
// it's defined. So, our function doesn't work if it isn't called in the
// context of the object.
var myFunc = myObj.myFunc;
myFunc(); // = undefined
// Inversely, a function can be assigned to the object and gain access to it
// through this, even if it wasn't attached when it was defined.
var myOtherFunc = function(){
return this.myString.toUpperCase();
}
myObj.myOtherFunc = myOtherFunc;
myObj.myOtherFunc(); // = "HELLO WORLD!"
// We can also specify a context for a function to execute in when we invoke it
// using 'call' or 'apply'.
var anotherFunc = function(s){
return this.myString + s;
}
anotherFunc.call(myObj, " And Hello Moon!"); // = "Hello World! And Hello Moon!"
// The 'apply' function is nearly identical, but takes an array for an argument list.
anotherFunc.apply(myObj, [" And Hello Sun!"]); // = "Hello World! And Hello Sun!"
// This is useful when working with a function that accepts a sequence of arguments
// and you want to pass an array.
Math.min(42, 6, 27); // = 6
Math.min([42, 6, 27]); // = NaN (uh-oh!)
Math.min.apply(Math, [42, 6, 27]); // = 6
// But, 'call' and 'apply' are only temporary. When we want it to stick, we can use
// bind.
var boundFunc = anotherFunc.bind(myObj);
boundFunc(" And Hello Saturn!"); // = "Hello World! And Hello Saturn!"
// Bind can also be used to partially apply (curry) a function.
var product = function(a, b){ return a * b; }
var doubler = product.bind(this, 2);
doubler(8); // = 16
// When you call a function with the new keyword, a new object is created, and
// made available to the function via the this keyword. Functions designed to be
// called like that are called constructors.
var MyConstructor = function(){
this.myNumber = 5;
}
myNewObj = new MyConstructor(); // = {myNumber: 5}
myNewObj.myNumber; // = 5
// Every JavaScript object has a 'prototype'. When you go to access a property
// on an object that doesn't exist on the actual object, the interpreter will
// look at its prototype.
// Some JS implementations let you access an object's prototype on the magic
// property __proto__. While this is useful for explaining prototypes it's not
// part of the standard; we'll get to standard ways of using prototypes later.
var myObj = {
myString: "Hello world!"
};
var myPrototype = {
meaningOfLife: 42,
myFunc: function(){
return this.myString.toLowerCase()
}
};
myObj.__proto__ = myPrototype;
myObj.meaningOfLife; // = 42
// This works for functions, too.
myObj.myFunc(); // = "hello world!"
// Of course, if your property isn't on your prototype, the prototype's
// prototype is searched, and so on.
myPrototype.__proto__ = {
myBoolean: true
};
myObj.myBoolean; // = true
// There's no copying involved here; each object stores a reference to its
// prototype. This means we can alter the prototype and our changes will be
// reflected everywhere.
myPrototype.meaningOfLife = 43;
myObj.meaningOfLife; // = 43
// We mentioned that __proto__ was non-standard, and there's no standard way to
// change the prototype of an existing object. However, there are two ways to
// create a new object with a given prototype.
// The first is Object.create, which is a recent addition to JS, and therefore
// not available in all implementations yet.
var myObj = Object.create(myPrototype);
myObj.meaningOfLife; // = 43
// The second way, which works anywhere, has to do with constructors.
// Constructors have a property called prototype. This is *not* the prototype of
// the constructor function itself; instead, it's the prototype that new objects
// are given when they're created with that constructor and the new keyword.
MyConstructor.prototype = {
myNumber: 5,
getMyNumber: function(){
return this.myNumber;
}
};
var myNewObj2 = new MyConstructor();
myNewObj2.getMyNumber(); // = 5
myNewObj2.myNumber = 6
myNewObj2.getMyNumber(); // = 6
// Built-in types like strings and numbers also have constructors that create
// equivalent wrapper objects.
var myNumber = 12;
var myNumberObj = new Number(12);
myNumber == myNumberObj; // = true
// Except, they aren't exactly equivalent.
typeof myNumber; // = 'number'
typeof myNumberObj; // = 'object'
myNumber === myNumberObj; // = false
if (0){
// This code won't execute, because 0 is falsy.
}
if (Number(0)){
// This code *will* execute, because Number(0) is truthy.
}
// However, the wrapper objects and the regular builtins share a prototype, so
// you can actually add functionality to a string, for instance.
String.prototype.firstCharacter = function(){
return this.charAt(0);
}
"abc".firstCharacter(); // = "a"
// This fact is often used in "polyfilling", which is implementing newer
// features of JavaScript in an older subset of JavaScript, so that they can be
// used in older environments such as outdated browsers.
// For instance, we mentioned that Object.create isn't yet available in all
// implementations, but we can still use it with this polyfill:
if (Object.create === undefined){ // don't overwrite it if it exists
Object.create = function(proto){
// make a temporary constructor with the right prototype
var Constructor = function(){};
Constructor.prototype = proto;
// then use it to create a new, appropriately-prototyped object
return new Constructor();
}
}
```
[[Website|http://learnxinyminutes.com/]]
[[GitHub link|https://github.com/jazzsaxmafia/Weakly_detector]] ! Running the program Label names start with 'n' in imagenet. Importing tf seems to introduce segmentation fault.
The reason we need learning theory is that otherwise we have to do parameter searching and design algorithms without principles.
! Topics
* [[Representation Learning]]
* [[Reproducing Kernel Hilbert Space]]
* PAC-learning
* bounds
** VC-dimension bound
** covering number bound
** Rademacher complexity bound
** PAC-Bayesian bound
!!! "Old" Generalization bounds
$$
\epsilon^2<\frac{\log|H_\epsilon|+\log\frac 1 \delta}{2m}
$$
* $\epsilon$: generalization error
* $\delta$: confidence
* $m$: number of training examples
* $H_\epsilon$: $\epsilon$-cover of the Hypothesis class (or cardinality)
We typically assume: $|H_\epsilon|\sim (\frac 1 \epsilon)^d$ where $d$ is the class (VC, ...) dimensions
! Examples
* [[Kernel Mean Embedding]]
* [[Generalization Bounds for Non-stationary Mixing Processes]]
@inproceedings{lee2009unsupervised,
title={Unsupervised feature learning for audio classification using convolutional deep belief networks},
author={Lee, Honglak and Pham, Peter and Largman, Yan and Ng, Andrew Y},
booktitle={Advances in neural information processing systems},
pages={1096--1104},
year={2009}
}
Copyright (c) 2014, Danielo Rodriguez All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
* [[paper|https://arxiv.org/abs/1611.02854]]
* [[blog|http://lstm.seas.harvard.edu/lantm/]]
* [[code|https://github.com/harvardnlp/lie-access-memory]]
Memory is accessed using a continuous head in a key-space manifold. The head is moved via Lie group actions, such as shifts or rotations, generated by a controller, and memory access is performed by linear smoothing in key space.
! Addresssing Procedure
A shift group $\mathbb R^2$ acting additively on $\mathbb R^2$. This means that $\mathcal A = \mathbb R^2$ so that $a(h)=(\alpha, \beta)$ acts upon a head $q = (x, y)$ by
$$
a(h)\cdot q = (\alpha, \beta) + (x, y) = (x+\alpha, y+\beta)
$$
A rotation group $SO(3)$ acting on the shpere $S^2=\{v\in\mathbb R^3: \|v\|=1\}$. Each rotation can be described by its axis $\xi$ (a unit vector) and angle $\theta$. An action ($\xi, \theta)\cdot q$ is just the appropriate rotation of the point $q$, and is given by Rodrigues' rotation formula
$$
a(h)\cdot q=(\xi, \theta)\cdot q = q\cos\theta+(\xi\times q)\sin\theta+\xi\langle\xi, q\rangle(1-\cos\theta)
$$
We show how to use TensorFlow to fit the linear function with sinusoidal noise:
```python
import numpy as np
import seaborn
# Define input data
X_data = np.arange(100, step=.1)
y_data = X_data + 20 * np.sin(X_data/10)
# Plot input data
plt.scatter(X_data, y_data)
```
First we need placeholders to manage the input
```python
# Define data size and batch size
n_samples = 1000
batch_size = 100
# Tensorflow is finicky about shapes, so resize
X_data = np.reshape(X_data, (n_samples,1))
y_data = np.reshape(y_data, (n_samples,1))
# Define placeholders for input
X = tf.placeholder(tf.float32, shape=(batch_size, 1))
y = tf.placeholder(tf.float32, shape=(batch_size, 1))
```
Implement the algorithm. Define the loss:
$$
J(W, b) = \frac 1 N\sum_{i=1}^N(y_i-(Wx_i+b))^2
$$
```python
# Define variables to be learned
with tf.variable_scope("linear-regression"): # resuse=False so tensors created anew
W = tf.get_variable("weights", (1, 1),
initializer=tf.random_normal_initializer())
b = tf.get_variable("bias", (1),
initializer=tf.constant_initializer(0.0))
y_pred = tf.matmul(X, W) + b
loss = tf.reduce_sum((y - y_pred)**2/n_samples)
```
Run one step of gradient descent
```python
# Sample code to run one step of gradient descent
In [136]: opt = tf.train.AdamOptimizer()
In [137]: opt_operation = opt.minimize(loss)
In [138]: with tf.Session() as sess:
.....: sess.run(tf.initialize_all_variables())
.....: sess.run([opt_operation], feed_dict={X: X_data, y: y_data})
```
The optimizer is a special node in the computational graph. It can figure out what to optimize, computes the gradient and use assign to do the update.
And to learn the model
```python
# Sample code to run full gradient descent:
# Define optimizer operation
opt_operation = tf.train.AdamOptimizer().minimize(loss)
with tf.Session() as sess:
# Initialize Variables in graph
sess.run(tf.initialize_all_variables())
# Gradient descent loop for 500 steps
for _ in range(500):
# Select random minibatch
indices = np.random.choice(n_samples, batch_size) # use np to get random samples, doubting if it's good
X_batch, y_batch = X_data[indices], y_data[indices]
# Do gradient descent step
_, loss_val = sess.run([opt_operation, loss], feed_dict={X: X_batch, y: y_batch})
```
! Topics
!! Algorithm
The LLE algorithm is based on simple geometric intuitions. Suppose the data consist of $N$
real-valued vectors $x_i$, each of dimensionality $D$, sampled from some smooth underlying manifold. Provided there is sufficient data (such that the manifold is well-sampled), we expect each data point and its neighbors to lie on or close to a locally linear patch of the manifold.
We can characterize the local geometry of these patches by linear coefficients that reconstruct each data point from its neighbors. In the simplest formulation of LLE, one identifies $K$ nearest neighbors per data point, as measured by Euclidean distance. (Alternatively, one can identify neighbors by choosing all points within a ball of fixed radius, or by using more sophisticated rules based on local metrics.) Reconstruction errors are then measured by the cost function:
$$
E(W) = \sum_i\left|x_i - \sum_jW_{ij}x_j\right|^2,
$$
which adds up the squared distances between all the data points and their reconstructions.
The weights $W_{ij}$ summarize the contribution of the $j$th data point to the $i$th reconstruction.
To compute the weights $W_{ij}$, we minimize the cost function subject to two constraints: first, that each data point $x_i$ is reconstructed only from its neighbors, enforcing $W_{ij} = 0$ if $ x_j$ does not belong to this set; second, that the rows of the weight matrix sum to one: $\sum_jW_{ij} = 1$. The optimal weights $W_{ij}$ subject to these constraints are found by solving a least squares problem.
Note that the constrained weights that minimize these reconstruction errors obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings, and translations of that data point and its neighbors. the invariance to translations is enforced by the sum-to-one constraint on the rows of the weight matrix. A consequence of this symmetry is that the reconstruction weights characterize intrinsic geometric properties of each neighborhood, as opposed to properties that depend on a particular frame of reference.
Suppose the data lie on or near a smooth nonlinear manifold of dimensionality $d\ll D$. To a good approximation, then, there exists a linear mapping—consisting of a translation, rotation, and rescaling—that maps the high dimensional coordinates of each neighborhood to global internal coordinates on the manifold. By design, the reconstruction weights $W_{ij}$ reflect intrinsic geometric properties of the data that are invariant to exactly such transformations. We therefore expect their characterization of local geometry in the original data space to be equally valid for
local patches on the manifold. In particular, the same weights $W_{ij}$ that reconstruct the $i$th data point in $D$ dimensions should also reconstruct its embedded manifold coordinates in $d$
dimensions.
LLE constructs a neighborhood preserving mapping based on the above idea. In the final step of the algorithm, each high dimensional observation $x_i$ is mapped to a low dimensional vector $y_i$ representing global internal coordinates on the manifold. This is done by choosing $d$-dimensional coordinates $y_i$ to minimize the embedding cost function:
$$
\Phi(Y) = \sum_i\left|y_i - \sum_jW_{ij}y_j\right|^2
$$
This cost function—like the previous one—is based on locally linear reconstruction errors, but here we fix the weights $W_{ij}$ while optimizing the coordinates $y_i$. The embedding cost defines a quadratic form in the vectors $y_i$. Subject to constraints that make the problem well-posed, it can be minimized by solving a sparse $N\times N$ eigenvector problem, whose bottom $d$ non-zero eigenvectors provide an ordered set of orthogonal coordinates centered on the origin.
!! Emotion Adaption
!!! Problem
Given the neutral records of each speaker $\{s\}$ and records under emotions $\{e\}$ for all but speaker $s_0$, we wish to construct the model to represent the distribution of $s_0$'s emotional records.
We assume the neighbors of $s_0$ under emotionless speech is also his neighbors under emotional speech. And the emotional speech models are adapted with linear combination from the speaker's emotionless models.
!!! Proposal
We find the neighbors with statistical distances between same variance Gaussian distributions. Then find the best reconstruction of $s_0$'s emotionless speech model with his neighbors using LLE, $W$.
$$
\tilde\mu_{N0} = \sum_jW_{0j}\mu_{Nj}
$$
Then, we add models of $s_0$'s emotional speech with corresponding adapted means of his neighbor and the same set of linear combination $W$
$$
\tilde\mu_{e0} = \sum_jW_{0j}\mu_{ej}
$$
After linear transformations of the dimension reduction procedures, the spatial information is preserved.
! Theory * [[LSTM Basic]] * [[RNN Performance Optimization]] * Gated Recurrent Units ** [[Revised GRU for speech|https://arxiv.org/abs/1710.00641]]: reset gate removed, replaced tanh with ReLU. Probably not very useful for other tasks ! Applications * Image recognition ** [[Feedback Networks|https://arxiv.org/abs/1612.09508]] ! Bib * [[Recurrent Highway Networks|http://arxiv.org/abs/1607.03474]] ** Residual connection for RNN * [[A-LSTM|http://arxiv.org/abs/1610.06258]] ** a decaying recent hidden state weight matrix called fast accociative memory. ** can be used in visual attention * [[Hybrid LSTM]]
* [[WRPN: Training and Inference using Wide Reduced-Precision Networks|https://arxiv.org/abs/1704.03079]] * [[Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent]] * Trainning Quantized Nets: A Deeper Understanding ** Theoretical analysis of BinaryConnect and Stochastic Rounding ** SR cannot exploit greedy local search
Bibtex
```
@article{hochreiter1997long,
title={Long short-term memory},
author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
journal={Neural computation},
volume={9},
number={8},
pages={1735--1780},
year={1997},
publisher={MIT Press}
}
```
Here is [[a great article|http://colah.github.io/posts/2015-08-Understanding-LSTMs/]] for an introduction to LSTMs in particular.
LSTMs are able to learn long-term dependencies which classic RNNs fail to.
! Comparing to RNN
[img width=600 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-SimpleRNN.png]]
The repeating module in a standard RNN caontains a single layer
[img width=600 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-chain.png]]
The repeating module in an LSTM contains four interacting layers.
! Detailed structure
!! Cell state
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-C-line.png]]
Cell state runs down the entire chain, the information can be added or removed, regulated by structures called gates.
!! Gates
[img width=100 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-gate.png]]
Gates are composed out of a sigmoid neural net layer and a pointwise multiplication operation. The sigmoid output with value of one means "let everything through". A LSTM has three gates.
!!! Forget gate
With forget gate, we decide what information we're going to throw away from the cell state.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-f.png]]
One scenario where forget gate could be useful is in word generation. In such a problem, the cell state might include the gender of the present subject, so that the correct pronouns can be used. When we see a new subject, we want to forget the gender of the old subject.
!!! Input gate joining with new value
The we want to decide what to add to the cell state.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-i.png]]
Then we add the regulated old value with the new one:
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-C.png]]
!!! Output gate
We filter our output with output gate. First, we run a sigmoid layer which decides what parts of the cell state we’re going to output. Then, we put the cell state through ''tanh'' (to push the values to be between −1 and 1) and multiply it by the output of the sigmoid gate.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-focus-o.png]]
! Variants on LSTM
[Gers & Schmidhuber (2000)|ftp://ftp.idsia.ch/pub/juergen/TimeCount-IJCNN2000.pdf] adds "peehole connections" to let the gate layers look at the cell state.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-var-peepholes.png]]
Another variation is to use coupled forget and input gates, by making these two decisions together.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-var-tied.png]]
''GRU'' ([[Cho, et al. (2014)|http://arxiv.org/pdf/1406.1078v3.pdf]]) is a slightly more dramatic variation on the LSTM. It combines the forget and input gates into a single “update gate.” It also merges the cell state and hidden state, and makes some other changes. The resulting model is simpler than standard LSTM models, and has been growing increasingly popular.
[img width=500 [http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-var-GRU.png]]
$$
\sum_i^\infty i
$$
[[graves05framewise|graves2005framewise.txt]] shows that LSTM is well suited to framewise phoneme classification, and that bidirectional networks are able to capture time dependencies in speech that elude unidirectional nets.
! Backward Pass
# reset all partial derivatives to 0. (What exactly does that mean?)
# starting at time $\tau_1$, propagate the output errors backwards through the unfolded net, using the standard BPTT equations:
$$
\begin{align}
\delta_k(\tau) &= \frac{\partial E(r)}{\partial x_k}\\
\epsilon_j(\tau_i) &= e_j(\tau_1)
\end{align}
$$
! Previous Work
!! Traditional Methods
!!! Bilinear Model
[[Style & Content]]
!! Normalizing Speaker Variations
!!! Vocal tract length normalization
VTLN warps the frequency axis of the filter-bank analysis to account for the fact that the locations of vocal-tract resonances vary roughly monotonically with the vocal tract length of the speaker.
The filter bank process with frequency warping can be written as:
$$
O_\alpha(n) = \sum_{w = l_n}^{h_n}T_n(w)X(\varphi_\alpha(w))\quad 0\le n\le N-1
$$
where $\varphi_\alpha(w)$ is the warping function, $\alpha$ is the warping factor, $X$ is the speech signal and $T_n$ is the n-th filter bank.
This is done in both training and testing with 20 quantized warping factors from 0.8 to 1.18. During the training, the optimal warping factor can be found using the expectation–maximization (EM) algorithm by repeatedly selecting the best factor given the current model and then updating the model using the selected factor.
!!! Feature-space maximum likelihood linear regression
fMLLR applies an affine transform to the feature vector so that the transformed feature better matches the model.
It is typically applied to the testing utterance by first generating recognition results using the raw feature and then re-recognizing the speech with the transformed feature.
In GMM-HMM systems, these feature transformed speaker-adaptation techniques are critical (9%), but the effect is very small in CD-DNN-HMM (2%), which means a well-trained DNN should be quite robust to speaker variaions. Its effect on more complex and solely DNN models is unknown. It is reasonable to guess even less. And both adaptation techniques can be implemented by adding a layer between the hidden and input layer.
!! Neural Nets
!!! Adaptation
!!! Transfer Learning
Transfer learning aims at developing a reasonably performed system for a new task, domain, or distribution efficiently and effectively by retaining and leveraging the knowledge learned from one or more similar tasks, domains, or distributions.
!!! Multitask Learning
Adding new tasks that share part of the representation can improve the original task performance. This is referred to as maultitask learning. Each layer of a neural net can be seen as a representation. The general structure of a MTL net can be described as 2 nets dedicated to each task sharing some part of their layers.
! Net Architectures
!! RNN
The standard RNN output, $y_O^{(t)}$, at a time step $t$ is calculated as:
$$
y_H^{(t)} = f_H(W_Hy^{(t-1)}+W_Ix^{(t)}),
$$
$$
y_O^{(t)}=f_O(W_Oy_H^{(t)},
$$
where $W_H$, $W_I$ and $W_O$ are the hidden, input and output weight matrices, $x_t$ is the input vector at time step $t$, vectors $y_H$ represent the hidden neuron activations. Functions $f_H$ and $f_O$ are the nonlinear activation functions.
!! Clockwork RNN
The main difference between CW-RNN and an RNN is that at each CW-RNN time step $t$, only the output of modules $i$ that satisfy $t \mod T_i = 0$ are executed.
!![[LSTM|Long short-term memory]]
LSTM is an RNN architecture that elegantly addresses the vanishing gradients problem using "memory units". Besides hidden units, the network contains input gates and output gates that controls when the memory to be updated and the information to leave the unit.
! Some Tuning on the Problem
can the sparse coding like thing be used in this?
! Training
[[Thesis Training RNN]]
! Dataset
TIMIT is used in [[the original LSTM paper|graves2005framewise.txt]]. They used 184 of the 3696 training set utterances (randomly chosen, constant for experiments) as ''validation set'' and trained on the rest.
* ''Content Analytics'' that organize and optimize content modules ** Machine learning method can help teachers organize massive open resources and teaching materials. Related fields are *** Knowledge representation: extract relationships and build theasarus *** Information retrieval: collect online resources *** Knowledge graph: group and visualize complex content ** Examples *** [[Gooru's Open Catalog|http://about.gooru.org/catalog]] is a rich content platform covers different subject areas *** [[IBM Watson Content Analytics|https://www.ibm.com/support/knowledgecenter/en/SS5RWK_3.5.0/com.ibm.discovery.es.nav.doc/iiypofnv_prodover_cont.htm]] is an extensive knowledge graph * ''Adaptive learning'' that track student knowledge and personalize instructions ** Machine learning can help with better assessment of individual student (esp. web-based) and adjust the teaching strategy. Related fields are *** Reinforcement learning: optimizing the instruction/questioning policy *** Statistics: robust and precise evaluation of student performance *** Decision tree: a useful algorithm for categorizing student within least questions ** Examples: Adaptive learning systems: [[dreambox|http://www.dreambox.com]], [[ALEKS|https://www.aleks.com/about_aleks]], [[Knewton|http://www.knewtonhighered.com/]] * ''Game-based learning'': idk, maybe just games [[ST Math|https://web.stmath.com/]], [[Mangahigh|https://www.mangahigh.com/en-au/]]. Don't seem very educational * ''Grading systems'': score essays, detect plagiarism ** Machine learning could be very good at plagiarism detection and can be trained to know language (this is new) *** Natural Language Processing can help finding mistakes in writing and grade answers *** Deep learning can recognize writing styles, grade longer answers ** Examples: [[WriteToLearn|https://www.writetolearn.net/]], [[Lightside|http://turnitin.com/en_us/what-we-offer/revision-assistant]] ** Publication: [[The Eras and Trends of Automatic Short Answer Grading|http://www.uni-weimar.de/medien/webis/publications/papers/stein_2015a.pdf]] * ''Data analysis'' ** There are some data beyond education could be useful *** Statistics can be used wherever there is data *** Graph mining on structured data (network) *** Embedding learning to represent complex data ** Examples: *** predicting enrollment, dropouts, detecting student social connections and learning interests, good teaching behavior, office hour scheduling etc.
@inproceedings{martens2011learning,
title={Learning recurrent neural networks with hessian-free optimization},
author={Martens, James and Sutskever, Ilya},
booktitle={Proceedings of the 28th International Conference on Machine Learning (ICML-11)},
pages={1033--1040},
year={2011}
}
* [[Algebraic Geometry]] * [[Analysis]] * [[Statistics]] * [[Optimization]]
MathJax is a cross-browser JavaScript library that displays mathematical notation in web browsers, using MathML, LaTeX and ASCIIMathML markup. MathJax is released as open-source software under the Apache license.
Using [[kpe's plugin|https://github.com/kpe/tw5-mathjax-plugin/tree/v0.1.0]], [[the previous one|https://gist.github.com/kpe/cc0547b318e6f8d4ddaa]] uses `MutationObserver` and gets error.
! Usage
!! Inline
We can render inline math like $a2+b2=c2$ by surrounding the MathJax markup with single dollar symbols like so: `$a^2 + b^2 = c^2$`.
!! Block
To generate standalone blocks of math, add double dollar symbols on new lines before and after the markup.
```
$$
P(E) = {n \choose k} p^k (1-p)^{ n-k}
$$
```
$$
P(E) = {n \choose k} p^k (1-p)^{ n-k}
$$
! Properties
[[MATLAB Snippets]]
! Vectorization
[[link|http://ufldl.stanford.edu/wiki/index.php/Logistic_Regression_Vectorization_Example]]
!! Logistic regression vectorization
Consider training a logistic regression model using batch gradient ascent. Suppose our hypothesis is
$$
h_\theta(x) = \frac{1}{1+\exp(-\theta^Tx)},
$$
we let $x_0=1$, so that $x \in \Re^{n+1}$ and $\theta \in \Re^{n+1}$, and $\theta_0$ is our intercept term. We have a training set $\{(x^{(1)}, y^{(1)}), \ldots, (x^{(m)}, y^{(m)})\}$ of $m$ examples, and the batch gradient ascent update rule is $\theta := \theta + \alpha \nabla_\theta \ell(\theta)$, where $\ell(\theta)$ is the log likelihood and $\nabla_\theta \ell(\theta)$ is its derivative.
We thus need to compute the gradient:
$$
\nabla_\theta \ell(\theta) = \sum_{i=1}^m \left(y^{(i)} - h_\theta(x^{(i)}) \right) x^{(i)}_j.
$$
```matlab
% Implementation 3
grad = x * (y- sigmoid(theta'*x))';
```
Logical Indexing Setting values below a threshold to be zero ```matlab S(S<th) = 0; ```
! Exponential
In finite dimensions, we define the matrix exponential $e^A$, $A\in\mathbb C^{n\times n}$, as
$$
e^A:=\sum_{k\ge0}\frac{A^k}{k!}.
$$
! Identities
$$
M = \begin{bmatrix}
A & B\\
C & D
\end{bmatrix}
$$
where $M$ is an $n\times n$ and $A$ is an $(n-k)\times(n-k)$ matrix.
* If $A$ is invertible, we can use the technique of [[Schur complementation|https://en.wikipedia.org/wiki/Schur_complement]] to express the inverse of $M$ (if it exists).
* [[Inverting the Schur complement|https://terrytao.wordpress.com/2017/09/16/inverting-the-schur-complement-and-large-dimensional-gelfand-tsetlin-patterns/]]
! Others
* [[Matrix Factorization]]
! Deep Factorization * [[Stable recovery of the factors from a deep matrix product and application to convolution networks|https://arxiv.org/abs/1703.08044]]
MLE can be situated at the fringe of the Bayesian paradigm. Extended coverage can be found in Lehmann and Casella (1998)
The drawbacks of MLE are
# The practical maximization of $l(\theta|x)$ can be quite complex, especially in multidimensional and constrained settings. Numerical procedures like EM for missing data and Robertson et al. (1997) for order-restricted parameter spaces have beem tailored to this approach but unsolved difficulties remain.
# A maximization technique is bound to give estimators that lack smoothness, as opposed to integration for instance. [[Saxena and Alam 1982]] show that, if $x\sim\chi_p^2(\lambda)$, the MLE of $\lambda$ is equal to 0 for $x<p$. MLE can be quite unstable.
# It lacks decision-theoretic and probabilistic supports. For instance, tests are not possible in a purely maximum likelihood context: it is necessary to call for frequentist justifications, even if they are based upon a likelihood ratio [[see Section 5.3]]. Similarly, confidence regions of the form $C=\{\theta;l(\theta)/l(\hat\theta)\ge c\}$, which are asymptotically shortest, will not depend solely on teh likelihood function if the bound $c$ is to be chosen to achieve coverage at a given level $\alpha$.
[[link|http://mi.eng.cam.ac.uk/research/dialogue/papers/yeyo06.pdf]] ! Transform-based * ''Feature extraction:'' The input speech examples are first analyzed to extract the spectral parameters that represent the speaker identity. Usually these parameters encode the short-term acoustic features, such as the spectrum shape and the formant structure. * Train conversion * Transformation: The new spectral parameters are obtained by applying the trained conversion functions to the source parameters. * Synthesize speech from the parameters. Mapping the [[prosody]] is an equally important and challenging problem. The souce pitch is shifted and scaled to match the mean and variance of the target speaker. !! Spectral parameters Estimating the spectral envelope with LPC, cepstral coefficients and [[LSF|Line spectral frequencies]].
! Bibs
* [Gretton et al. 2012]
* [Smola et al. 2007]
* [[Training generative neural networks via Maximum Mean Discrepancy optimization|https://arxiv.org/abs/1505.03906]]
MMD can be thought as difference between feature means. It lets us do the optimization in closed form
* Can get optimum value on each minibatch
* Avoids two-step optimization issue in GAN
! Definition
RKHS $H$, feature map $\varphi: \mathcal X\rightarrow\mathcal H$, $f(x)=\langle f, \varphi(x)\rangle_{\mathcal H}$, we look at the mean feature difference in $\mathcal H$:
$$
\|\mu_P-\mu_Q\|_{\mathcal H} = \|\mathbb E_{X\sim P}\varphi(X)-\mathbb E_{Y\sim Q}\varphi(Y)\|_{\mathcal H}
$$
Here $\mu_P$ is the [[Kernel Mean Embedding]] of a probability measure
$$
\mu_P = \int k(x, \cdot)P(dx)
$$
We take its operator norm and get
$$
\underset{f:\|f\|_{\mathcal H}\le 1}{\sup}|\langle f, E_{X\sim P}\varphi(X)-\mathbb E_{Y\sim Q}\varphi(Y)\rangle_{\mathcal H}|
$$
And with linearity and reproducing property
$$
\underset{f:\|f\|_{\mathcal H}\le 1}{\sup}|E_{X\sim P}f(X)-\mathbb E_{Y\sim Q}f(Y)|
$$
! Computation
Estimator given sample $X = (x_1, \cdots, x_N)$ and $Y = (y_1, \cdots, y_M)$, we just have to take certain sum of kernels.
$$
MMD^2(X, Y) = \frac{1}{N(N-1)}\sum_{n\neq n'}k(x_n, x_{n'})+\frac{1}{M(M-1)}\sum_{m\neq m'}k(y_m, y_{m'})-\frac{2}{MN}\sum_{m=1}^M\sum_{n=1}^Nk(x_n, y_m)
$$
This family uses a universal access mechanism (c.f. associative computers). * End-To-End Memory Network: make the supervision indirect, supervision signal on output * [[Key-Value Memory Network]]: makes matchings more relavant, easy to do pointers. * Forward Prediction Memory Network * [[Recurrent Entity Network]] ! Sparse Memory Access * [[Scaling Memory-Augmented Neural Networks with Sparse Reads and Writes|https://arxiv.org/abs/1610.09027]] ** restrict read write size to constant ** kNN memory matching ! Code [[fb MemNN|https://github.com/facebook/MemNN]] ! Remarks Maybe not the most convenient for all tasks: e.g. we search real computers using many more mechanisms (text, trees, read/write time, title/tags).
[[link|https://arxiv.org/abs/1703.00837]] Use loss gradients as meta information. 2 types of loss functions: * representation loss for good representation learner criteria * main loss for the input task objective Slow training with [[REINFORCE]]. Generates fast weights at two time-scales by operating in meta space. To integrate fast weights with slow weights, layer augmentation is used. External memory for rapid learning and generalization.
Deep learning hard to generalize given few examples because there is no optimization techniques dedicated to this kind of problems. I will include ''rapid learning'' and ''continual learning'' literatures here.
''Meta-learning'' suggests we learn in 2 steps:
* acquire knowledge within each separate task (fast)
* extract information across all tasks (slow)
The goal of metal-learner is to acquire knowledge of different tasks. Consider a training algorithm
* input: training set $D_{train} = \{(X_t, Y_t)\}^T_{t=1}$
* output: parameters $\theta$ of model $M$
* obejctive: good performance on test set $D_{test} = (X, Y)$
Desire a meta-learning algorithm
* input: meta-training set $\mathcal D_{meta-train} = \{(D_{train}^{n}, D_{test}^n)\}_{n=1}^N$, where $D_{train}^{n}$ is a set of training set. Slow learning can be done by i.e. [[REINFORCE]].
* output: parameters $\Theta$ representing a training algorithm
* objective: good performance on meta-test set $\mathcal D_{meta-test} = \{(D_{train}^{n'}, D_{test}^{n'})\}_{n'=1}^{N'}$
! Bibs
* [[Optimization as a Model for Few-Shot Learning]]
* [[Synthetic Gradients]]
* [[Meta-Learning with Temporal Convolutions|https://arxiv.org/abs/1707.03141]]
* [[Meta Networks]]
* OpenAI's [[Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments|https://arxiv.org/abs/1710.03641]]
** [[RoboSumo|https://github.com/openai/robosumo]]
! Rapid learning
* [[One-shot Learning]]
* [[Zero-shot Learning]]
* probabilistic programming approach (pen stroke generation)
* memory-based approach and trained NTM
!! Meta optimizer
* Controlling fast weight update rate: [[Overcoming catastrophic forgetting in NNs]]
* Use fast weights to replace soft attention mechanism
[[paper|https://arxiv.org/abs/1710.09412]]
The mixup training loss is
$$
\mathcal L(\theta)=\mathbb E_{x,y}\mathbb E_{\lambda\sim\beta(0.1)}\mathcal l(\lambda x_1+(1-\lambda)x_2,\lambda y_1+(1-\lambda)y_2)
$$
seems plausible if net is linear. I think this will linearize the classification surface in some way.
If we assume linear loss:
$$
\mathcal l(x, py_1+(1-p)y_2) = p\mathcal l(x, y_1)+(1-p)\mathcal l(x, y_2)
$$
we can simply the loss into
$$
\mathcal L(\theta)=\mathbb E_{x,y}\mathbb E_{\lambda\sim\beta(\alpha, \alpha+1)}\mathcal l(\lambda x+(1-\lambda)x',y)
$$
When mixup mixes up the empirical distribution of the two classes, it turns them into continuous distributions with perfectly overlapping support. It works because:
* the training loss becomes better defined such that the Bayes-optimal solution is unique and easier to find consistently. The class-conditional distributions end up having fully overlapping support. But proof of this being good solution?
* data augmentation turns the training distribution closer to the test distribution. The ideal generalization gap can be seen as Bregman divergence between $p_{test}$ and $p_{aug}$, up to a constant.
This works better than instance noise for GAN. Also works against adversarial examples because some samples generated around the neighborhood might be covering the adversarial examples.
! Teams [[Chicago Cubs]] [[Tampa Bay]] [[White Sox]]
The ground truth function is itself a neural network.
! Notations Truth or full reality is denoted as $f$, and $f(x)$ is the integration over the variable $x$. $g_i(x|\theta)$ denotes the $i$th approximating model. ! Overview of Model Selection Criteria There are two different classes of model selection methods: These have been labeled //efficient// and //consistent//. Under the frequentist paradigm for model selection one generally has three main approaches: # optimization of some selection criteria ## based on some form of mean squared error (e.g., Mallows' $C_p$, 1973) or mean squared prediction error (e.g., PRESS, Allen 1970) ## estimates of K-L information (e.g., TIC and the special cases AIC, AIC$_c$ and QAIC$_c$) ## consistent estimators of $K$, the dimension of the "true model" (e.g., [[BIC|BIC Changepoint]]) # tests of hypotheses # as hoc methods. !! Estimates of K-L Information AIC, AIC$_c$ and QAIC$_c$ are estimates of the relative K-L distance between truth $f(x)$ and the approximating model $g(x)$. * Hypothesis testing of [[Outlier]] * [[Kullback-Leibler divergence]] * [[Akaike's information criterion]] gives same result as [[GLR Changepoint]]. The relationship between it and KL remains unknown. According to Akaike's result, they are the same if the model is K-L best.
Model selection and multimodel inference: a practical information-theoretic approach / Kenneth P. Burnham, David R. Anderson. 2nd ed.
[[link|http://arxiv.org/abs/1608.04980]] At [[ICML'2016 Workshop on Optimization Methods for the Next Generation of Machine Learning|http://optml.lehigh.edu/events/icml2016/]], Bengio gave a talk on [[From Curriculum Learning to Mollifying Networks|http://www.iro.umontreal.ca/~bengioy/talks/ICML-OptWorkshop-24June2016.pdf]]. I failed to find the videos. ; Mollifier : is an infinitely differentialble funtiion that behaves as an approximate identity in the group of convolutions of integrable functions. A mollifier converges to the Dirac fuction if we rescale it appropriately. It controls the loss funtion to make the gradient exists and approximately convex at the beginning.
! Background and Motivation
[[Random forest]]
! Mondrian Forests
Mondrian process + Random forests: which can give same result as offline in a batch way
[[Mondrian process]]
!! Mondrian process distribution over $\mathcal T$
Use $\mathcal {MP}(\lambda, [l_1, u_1], \dots, [l_d, u_d])$ as prior over decision trees $p(\mathcal T|X)$, where the range is given by $X$.
Self-consistency: $p(\mathcal T|X)$ is restriction to range of $X$
Online learning: unveil the decision trees on a larger range, conditional MP
!! Online learning
* the size and lifetime of a node control probability of new splits being introduced
* self-consistent hierarchical Bayesian prior (Pitman-Yor) on the leaf parameters
A stochastic process over binary hierarchical axis-aligned partitions of $\mathbb R^d$.
$\mathcal{MP}(\lambda,[l_1, u_1],[l_2, u_2])$
# Draw $\Delta_\epsilon$ from exponential with rate $u_1-l_1+u_2-l_2$
# if $\Delta_\epsilon > \lambda$ stop,
# else, sample a split
#* split dimension: choose dimension $j$ with prob $\propto u_j-l_j$
#* split location: choose cut location uniformly from $[l_j, u_j]$
#* recurse on left and right subtrees with parameter $\lambda-\Delta_\epsilon$
self-consistency of MP:
The restriction has distribution $\mathcal{MP}(\lambda,[l'_1, u'_1],[l'_2, u'_2])$
* [[Gibbs Sampling]]
* [[Hamilton Monte Carlo]]
* [[Langevin MCMC|https://arxiv.org/abs/1707.03663]]
* [[Stochastic Gradient Descent as Approximate Bayesian Inference]]
* [[Stochastic Gradient MCMC]]
* Adaptive MCMC
* SMC
* Approximate Bayesian Computation
To identify the regions of parameter space that dominate expectations we need to consider the behavior of both the density and the volume.
Random Walk Metropolis: $a(q'|q) = \min\left(1, \frac{\pi(q')}{\pi(q)}\right)$. The guess-and-check strategy of Random Walk Metropolis is doomed to fail in high-dimensional spaces where there are an exponential number of directions in which to guess but only a singular number of directions that stay within the typical set and pass the check.
! Controlling variance
* Importance Sampling
* Quasi Monte Carlo
* Rao-Blackwellization
[[Official COCO API|https://github.com/pdollar/coco]]
! Improving Speech Recognition In [30], Seltzer and Droppo proposed to improve the recognition accuracy of the DNN-HMM system on the TIMIT phone recognition task [10] by adding a secondary task to the training of DNNs. For example, the digit recognition can be enhanced by training the network to simultaneously * classify the digits * enhance the noisy speech * recognize the gender of the speaker The secondary tasks they investigated are: * Phone label task * State context task * Phone context task Their results indicate that adding phone label classification as the secondary task does not affect the primary task’s result. This is understandable since the phone label does not provide any additional information than the state label already used in the primary task. ! Recognizing both Phonemes and Graphemes [4] trains DNN with triphone model and trigrapheme model of the same language.
[[Albums]]
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
@inproceedings{nakashika2013voice,
title={Voice conversion in high-order eigen space using deep belief nets.},
author={Nakashika, Toru and Takashima, Ryoichi and Takiguchi, Tetsuya and Ariki, Yasuo},
booktitle={INTERSPEECH},
pages={369--372},
year={2013}
}
[[blog|https://blogs.princeton.edu/imabandit/2014/03/06/nesterovs-accelerated-gradient-descent-for-smooth-and-strongly-convex-optimization/]]
$$
\begin{align}
v_t& = \mu v_{t-1}-\eta\nabla E(w_{t-1}+\mu v_{t-1})\\
w_t&=w_{t-1}+v_t
\end{align}
$$
Nesterov solves the serial problem in time $O(\sqrt{\kappa}\log(1/\epsilon))$.
! Bibs * NTM * [[Lie-Access NTM]] * [[Differentiable Neural Computer]] * [[Neural Map: Structured Memory for Deep Reinforcement Learning|https://arxiv.org/abs/1702.08360]]: DNC for 3D navigation RL. ! Remarks * Unified memory is not enough ** NTM: could only 'allocate' memory in contiguous blocks, leading to management problems ** DNC adds links and usage (good enough?) ** Sparse Access Memory works for both NTM and DNC, scale up? ! Applications The model must interact with a symbolic executor through non-differentiable operations to search over a large program space. Differentiable communication? [[Gumbel-Softmax|https://arxiv.org/abs/1611.01144]] trick can to approximate discrete communication decisions with a continuous representation during training. !! [[Semantic Parsing]] !! Program Induction Neural Program Synthesis Tasks: Copy, Grade-school addition, Sorting, Shortest Path. * Neural Turing Machine * Reinforcement Learning Neural Turing Machines * Stack Recurrent Nets * [[Neural Programmer]] * Neural Programmer-Interpreter * Neural GPU * Learning Simple Algorithms from Examples * [[Differentiable Neural Computer]] * [[NPI via Recursion]] * [[Program Induction by Rational Generation: Learning to Solve and Explain Algebraic Word Problems]] * [[Learning Neural Programs To Parse Programs|https://github.com/liuchangacm/neuralparser]] ** predict output by learning the compiler and run the program? what is the point? (m)any applications? !! Bibs * [[Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision]] !! Challenges * Existign neural program architecutures do not generalize well. * No Proof of Generalization
! Bibs * [[Convolutional Sequence to Sequence Learning]] * [[Attention Is All You Need]] ! Architectures ''Kalchbrenner and Blunsom 2013'' CSM Encoder, decode with RNN, predict with a linear softmax output. * Convolutions learn interactions among features in a local context * By stacking them, longer range dependencies can be learnt * Deep ConvNets have a branching structure similar to trees, but not parser is required * To handle variable length, see [[A Convolutional Neural Network for Modelling Sentences|https://arxiv.org/abs/1404.2188]] ''Sutskever et al. 2014'' seq2seq. The hidden state has to remember a lot of information. Following tricks improve performance： * backwards input * decoder ensemble ! Improvements * [[Attention Mechanism]] add attention to seq2seq translation: +11 BLEU ! Toolkit * [[OpenNMT|https://github.com/opennmt/opennmt]] * [[Nematus|https://github.com/EdinburghNLP/nematus]]: Attention-based encoder-decoder model from EdinburghNLP
[[Bahdanau et al. 2014|https://arxiv.org/abs/1409.0473]]
Pseudo code:
```
F = EncodeAsMatrix(f)
e_0 = <s>
s_0 = w (Learned initial state; Original paper uses Uh_1)
t = 0
while e_t != </s>
t = t+1
r_t = Vs_{t-1}
u_t = v^T tahh(WF + r_t) (computing this matrix product independent from states can be moved outside the loop to speedup)
a_t = softmax(u_t)
c_t = Fa_t
s_t = RNN(s_{t-1}, [e_{t-1};c_t]) (e is a learned embedding)
y_t = softmax(Ps_t+b) (P and b are learned parameters)
e_t|e_{<t} ~ Categorical(y_t)
```
! Cross entropy loss function
The cross entropy objective function for a single training pair $(x, y)$ is
$$
-\sum_{k=1}^K1\{y=k\}\log \hat y_k,
$$
where $K$ is the number of output classes, and $\hat y_k$ is the probability that the model assigns to the input example taking on label $k$.
Cross entropy is a convex approximation to the ideal 0-1 loss for classification.
!! The modified cross entropy
$$
E = -\sum_{j=1}^m(t^j\log\frac{y^j}{t^j}+(1-t^j)\log\frac{1-y^j}{1-t^j}).
$$
! Activation functions
; Activation Funtion
: An activation function is a function $h: \mathcal R\rightarrow\mathcal R$ that is differentiable almost everywhere.
!! The sigmoid
* Standard logistic function $f(x) = 1/(1+e^{-x})$
* Hyperbolic tangent $f(x) = \tanh(x)$
Symmetric sigmoids often converge faster than standard logistic function. A recommended sigmoid is $f(x) = 1.7159 \tanh(\frac 2 3 x)$ for centralized and rotated inputs. The variance of the outputs will also be close to 1 because the ''effective gain'' of the sigmoid is roughly 1 over its useful range:
* $f(\pm)=\pm 1$
* the second derivative is a maximum at $x = 1$
* the effective gain is close to 1
It is also said that target value should be chosen at the point of the maximum second derivative on the sigmoid so as to avoid saturating the output units. Otherwise, outputs would be on the tails of the sigmoid and makes the weights extremely large and stop updates. The uncertainty cannot be identified as well.
[[Noisy Activation Fucntions]]
``` bib missing ```
[[paper|https://arxiv.org/abs/1511.04834]] Neural Programmer learns to create programs without needing examples of correct programs.
! Papers
* [[Instance Normalization: The Missing Ingredient for Fast Stylization|https://arxiv.org/abs/1607.08022]]
** claims replacing batch normalization with instance normalization significantly improves the quality of feedforward style transfer models.
* [[Neural Style Representations and the Large-Scale Classification of Artistic Style|https://arxiv.org/abs/1611.05368]]
** for classification
* [[Scribbler: Controlling Deep Image Synthesis with Sketch and Color|https://arxiv.org/abs/1612.00835]]
** color sketches by training GAN on generated inputs
** brilliant idea and result
* [[A Learned Representation For Artistic Style|http://arxiv.org/abs/1610.07629]]
** conditional instance normalization, surprising result, why should this work?
* [[Prisma's Texture Networks|https://github.com/DmitryUlyanov/texture_nets]]: an improved implementation
* [[Deep Photo Style Transfer|https://arxiv.org/abs/1703.07511]]
** preserve reality by making sure transformation is locally affine in color space
** Style loss term updated with segmentation mask
! Basic
!! Objective
In its original formulation, the neural algorithm of artistic style proceeds as follows: starting from some innitialization of $p$ (e.g. $c$, or some random initialization), the algorithm adapts $p$ to minimize the loss function
$$
\cal L(s, c, p) = \lambda_s\cal L_s(p) + \lambda_c\cal L_c(p),
$$
where $\cal L_s(p)$ is the style loss, $\cal L_c(p)$ is the content loss. Given a set of "style layers" $\cal S$ and a set of "content layers" $\cal C$, the style and content losses are themselves defined as
$$
\cal L_s(p)=\sum_{i\in\cal S}\frac{1}{U_i}\|G(\phi_i(p))-G(\phi_i(s))\|^2_F
$$
$$
\cal L_c(p)=\sum_{j\in\cal C}\frac{1}{U_j}\|\phi_j(p)-\phi_j(s)\|^2_2
$$
where $\phi_l(x)$ are the classifier activations at layer $l$, $U_l$ is the total number of units at layer $l$ and $G(\phi_l(x))$ is the Gram matrix associated with the layer $l$ activations.
!! Fast neural style
A style transfer network $T$ takes a content image $c$ as input and output pastiche image $p$ directly. With this we can achieve real-time style transfer. The downside is one net is tied to one specific style.
!! Style representation
The straightforward solution is to condition the net on the style $s$: $p = T(c, s)$. A very surprising fact about the role of normalization in style transfer networks: to model a style, @@color:#859900;it is sufficient to specialize scaling and shifting parameters after normalization to each specific style@@. In other words, all convolutional weights of a style transfer network can be shared across many styles, and it is sufficient to tune parameters for an affine transformation after normalization for each style.
Conditioning on a style is achieved as follows:
$$
z = \gamma_s\left(\frac{x-\mu}{\sigma}\right)+\beta_s
$$
! Implementations
* Fast neural style: [[github|https://github.com/jcjohnson/fast-neural-style]].
** Use perceptual loss to guide image generation
** Downsample and then upsample
** Don't like VAE, no 1-d representation
** First realtime implementation
** written in lua
* Google's [[Supercharging Style Transfer|https://research.googleblog.com/2016/10/supercharging-style-transfer.html]]
** No code released
* Real otf one: [[AdaIN|https://github.com/xunhuang1995/AdaIN-style]]
components * manager: provides weak supervision * programmer: seq2seq * computer: symbolic non-differentiable Lisp interpreter contributions # augment seq2seq model with a key-variable memory # generalize the weakly supervised semantic parsing framework #* execute partial program #* prune search space # REINFORCE + iterative maximum likelihood training process
# Computing for neuroscience
## Euro Brain Project
# Neuroscience for computing
BBD: interaction with real env
Human brain is parallel: simulating takes 1000 times longer
postsynapc potential: EPSP (+) / IPSP (-)
A single neuron receives potentials from roughly 10,000 synapses. (Signal is binary?)
Neural models with temporal dynamics. Leaky Integrate-and-Fire model (LIF).
!! Neural Information Encoding
Coding Schemes
!!! Rate coding
$$
r=\frac{n_{sp}}{T}
$$
easy to measure firing rates. neglects all the information possibly contained in the exact timing of the spikes. In real life information can be carried within milisecs.
# Temporal coding
$$
s(t) = \sum_i\delta(t-t_i)
$$
Spike train. Temporal patterns.
# Latency encoding: the earlier fire, stronger.
# Phase encoding
# Population coding: joint activites of a number of neurons
!!! Encoding models
Temporal coding by controlling exact time of spike
!! Synapic Learning Algorithms
Pre-synapse-post
# Spike Timing Dependent Plasticity (STDP)
## long-term potentiation
## long-time depression
!! Integrated Model of Coding and Learning
# Latency and phase encoding
## The original image is partitioned into RFs
## Encoding in RF1
## Compressed
!! Learning, Memory and Embodied Cognition
!! Neuro-Cognitive Robot
! External Memory RNN memory is stored in the vector of hidden activations * fragile ** tends to be overwritten by new information ** vanishing gradients leverages by gates * computation grows with memory size ** $O(N^2)$ ** read a whole book once into memory and operate on that * hard-coded locations make @@color:#859900;indirection (and hence variables)@@ hard External memory * less fragile, only some memory is touched at each step * indirection is possible: memory ''content'' is independent of ''location'' * separates ''computation'' from ''memory'' [[Differentiable Neural Computer]] is the new toy. Finding new ways of accessing memory. 2 directions * [[Memory Networks]]: simple and SOTA * [[Neural Abstract Machines]]: for complex structured tasks ! Learning When to Halt * Problem: The number of steps of computation an RNN gets before emitting an output is determined by the length fo the input sequence, not the difficulty of the task. * Solution: Train the network to learn how long to 'think' before it 'acts'. ** separate computation time from data time. ** [[Adaptive Computation Time with RNNs|https://arxiv.org/abs/1603.08983]]: reveal the difficulty of data. ! Beyond BPTT * Problems ** Memory cost increases with sequence length ** Weight update frequency decreases ** The better RNNs get, the longer the sequences we train them on * Solutions ** Truncated backprop (misses long range interactions) ** RTRL (too expensive) ** Approximate/local [[RTRL|Real-Time Recurrent Learning]] (promising) ** [[Synthetic Gradients]] (drastic)
If the null and alternative hypotheses are point hypotheses, the Neyman-Pearson lemma establishes that there exist [[UMP|5.3.1 UMP and UMPU tests]] test procedures and that they are of the form
$$
\varphi_\pi(x) = \left\{ \begin{array}{ll}
1 & \mbox{if } f(x|\theta_1)<kf(x|\theta_0),\\
0 & \mbox{otherwise},
\end{array}\right.
$$
$k$ being related to the selected significance level $\alpha$.
proof: Lehmann (1986, p. 79)
* [[NIPS 2015]] * [[NIPS 2016]] ! NIPS 2017 * Convolutional Gaussian Process ** replacing matrix mult in conv with gaussian kernel
* [[Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets]] * [[Gaussian process modulated renewal processes]] !! Deep Reinforcement Learning Workshop * [[Atari Games Reinforcement Learning UMich]] * Faster Deep Reinforcement Learning ** Online Hogwild!-style asynchronous updates
!! Sources * [[Spotlight Videos|http://nuit-blanche.blogspot.com/2016/12/spotlight-videos-at-nips2016.html]] * [[Videos|http://nuit-blanche.blogspot.hk/2017/01/the-nips2016-videos-are-out.html]] !! Deep Generative Models * [[Divergence Classed GANs]] !! NLP * Word Movers Distance ** provided a way of summarizing the difference between documents using their embeddings. For tasks that are supervised (e.g., text classification) this can be taken one step further. The Supervised Word Movers Distance (paper) performs affine transformations and re-weightings to provide class separation, leading to efficient state-of-the-art performance. !! Deep learning applications * SURGE: Surface Regularized Geometry Estimation from a Single Image ** CNN outputs depth, normal, plane and edge. Embed surface planar information to regularize the surface with a multitask objective function. * [[DeepMath — Deep Sequence Models for Premise Selection|https://arxiv.org/abs/1606.04442]] ** Deep automated theorem proving !! CNN * Natual-Parameter Networks: A Class of Probabilistic Neural Networks ** Model activation functions with different distributions * CNNpack: Packing Convolutional Neural Networks in the Frequency Domain. ** prune conv filters after DCT. idea is simple, and sure it will work. But how can this help training and model structure? * [[Value Iteration Networks|https://arxiv.org/abs/1602.02867]] ** such models include a differentiable “planning module” which allows networks to make plans and better generalize to unseen domains. !! RNN * [[Sequential Neural Models with Stochastic Layers|https://arxiv.org/abs/1605.07571]] ** combines ideas from State Space Models (formally best in class for stochastic sequences like audio) and RNNs, leveraging the best of both worlds. * [[Phased LSTM: Accelerating Recurrent Network Training for Long or Event-based Sequences|https://arxiv.org/abs/1610.09513]] ** adds a “time gate” to LSTMs which greatly improves optimization and performance for long sequence data. !! Bayesian * Cooperative Graphical Models ** For a large class of high-order probabilistic models: *** new variational lower and upper bounds *** efficient inference algorithms *** thorough empirical evaluation * Synthesis of MCMC and Belief Propagation ** [[Computing Partition Functions of Graphical Models]] * @@color:#859900;Attend, Infer, Repeat: Fast Scene Understanding with Generative Models@@ ** Presents an inspiring approach to understanding scenes in an image. Using Bayesian and Variational Inference, the authors construct models that understand the number, location, and type of objects in a picture without any supervision. We are intrigued, as their models can reason/infer about distributions outside of training examples. The models do suffer from specification needs, but none-the-less provide interesting avenues for exploration. !! Algorithms * On Regularizing Rademacher Observation Losses ** Adversarial network for different measure regularization * [[Fast and Provably Good Seeding for k-Means]], exciting result, looking forward to the oral. !! Theory * [[Scaled Bregman Theorem]]
[[Stanford NLP Course]] * [[NLP Data]] ! Topics * Perceiving and representing text: [[Word Embedding]] ** handling unknown words ** tokenize? spelling? characters? bytes? ** smaller perceptual units need more complex models, but have fewer OOVs (doubt) * [[Text Classification]] * [[Semantic Parsing]] * Natual language generation ** [[Language Modelling]] ** [[Conditional Language Modelling]] * Analytic applications ** topic modelling ** linguistic analysis (discourse, semantics, syntax, morphology) * [[CNN for NLP]] ! Tools * [[LSTMVis|https://github.com/HendrikStrobelt/LSTMVis]] ! Task & State-of-the-art techniques * Question answering (babl): Strongly Supervised MemNN (Weston et al. 2015) 93.3%; Dynamic Memory Network 93.6% * Sentiment Analysis (SST): Tree-LSTMs (Tai et al. 2015); DMN 88.5% * Part of speech tagging (PTB-WSJ): Bi-directional LSTM-CRP (Huang et al. 2015); The performance is always dominated by the size of high quality data. <<< Probably the most successful concept is to use distributed representation of words. For example, neural network based language models significantly outperform N-gram models. <<< [[Efficient Estimation of Word Representations in Vector Space|Word2Vec]] ! Sentence model is to analysis and represent the semantic content of a sentence for purposes of classification or generation. * sentiment analysis * paraphrase detection * entailment recognition * summarisation * discourse analysis * grounded language learning * image retrieval !! Reviews * Composition based methods for ** vector representation of words or ** tied to particular syntactic relations or word types * automatically extracted logical forms * neural networks @@color:#d33682; ''advantages'': can be trained to obtain feature by predicting the context. can be powerful classifier/sentence generator @@ ** neural BoW or bag-of-//n//-grams ** recursive nets ** time-delay nets based on convolutional operations
Size of vocabulary: speech 20K, PTB 50K, Google 1T This is what Baidu used in //End-to-End Speech Recognition in English and Mandarin// * Language model: Kneser-Ney smoothed character level 5-gram model. * English: 850mil n-grams trained with KenLM on Common Crawl Repository * Chinese: 2bil n-grams ! Chinese vs English Chinese characters are more similar to English syllables than English characters. In speech, there are 14.1 chars/s in English, 3.3 chars/s in Mandarin. Shannon entropy per char 4.9 bits in English, 12.6 bits in Mandarin. ! Datasets Lee collected [[these datasets|http://www.cs.cornell.edu/home/llee/data/]]. * [[Google Research Datasets|https://github.com/google-research-datasets]] ** [[Online Discussion|https://github.com/google-research-datasets/coarse-discourse]] !! Language Modelling * Penn Treebank: from WSJ, very small and has been heavily processd * Billion Word corpus： Train and test sentences from the same articles and overlap in time * WikiText: better option !! QA * [[Facebook ParlAI|https://github.com/facebookresearch/ParlAI]] ** bAbI and wiki stuff ** SQuAD !! [[Sentense Classification Datasets]]
! Bibs * [[On the naturalness of software|http://earlbarr.com/publications/naturalness_cacm.pdf]]: start with language model * [[Machine Learning for Big Code and Naturalness|https://ml4code.github.io/papers.html]] ! Introduction <<< ''The naturalness hypothesis.'' Software is a form of human communication; software corporahave similar statistical properties to natural language corpora; and these properties can be exploited to build better software engineering tools <<< * [[SE+NLP proposal]] ! Directions * Language model * Learning over execution traces or flows * [[Source Code Generating Models]] * Representation Model * Pattern Mining: find code idioms * Debugging * Traceability * Code completion ** [[Learning Python Code Suggestion with a Sparse Pointer Network|https://github.com/uclmr/pycodesuggest]] * Inferring code conventions * Code defects * Code migration, porting and clones * Optimizing an algorithm in simulation
; Saturation : An activation function $h(x)$ with derivative $h'$ is said to be right saturate if its limit as $x\rightarrow\infty$ is zero. ; Hard and Soft Saturation : Let $c$ be a constant such that $x > c$ implies $h'(x) = 0$, etc.
! ReLU
ReLU is the abbreviation of Rectified Linear Units. This is a layer of neurons that use the non-saturating activation function $f(x) = \max(0, x)$. It increases the nonlinear properties of the decision function and of the overall network without affecting the receptive fields of the convolution layer.
Other functions are used to increase nonlinearity. For example the saturating hyperbolic tangent $f(x) = \tanh(x)$, $f(x) = |\tanh(x)|$, and the sigmoid function $f(x) = (1 + e ^{-x})^{-1}$. Compared to tanh units, the advantage of ReLU is that the neural network trains several times faster.
''advantages'':
# enforces hard zeros in representation.
# avoids vanishing gradient somehow.
! Variations
* [[ELU|https://arxiv.org/abs/1511.07289]]: openai uses this on ResNet VAEs for IAF.
* Casella G, Robert C P. Rao-Blackwellisation of sampling schemes[J]. Biometrika, 1996, 83(1): 81-94. ([[BibTeX|casella1996rao.txt]]) * Harati Nejad Torbati A H, Picone J, Sobel M. Applications of Dirichlet Process Mixtures to speaker adaptation[C]. Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on. IEEE, 2012: 4321-4324. ([[BibTeX|harati12applications.txt]]) * Rosen-Zvi M, Griffiths T, Steyvers M, et al. The author-topic model for authors and documents[C]. Proceedings of the 20th conference on Uncertainty in artificial intelligence. AUAI Press, 2004: 487-494. ([[BibTeX|rosen2004author.txt]]) * Sudderth E B, Torralba A, Freeman W T, et al. [[Describing visual scenes using transformed objects and parts[J]|Transformed DP]]. International Journal of Computer Vision, 2008, 77(1-3): 291-330. ([[BibTeX|sudderth08describing.txt]])
! Bibs
* [[L2 Regularization versus Batch and Weight Normalization|https://arxiv.org/abs/1706.05350]]
** $L_2$ regularization, in combination with these normalization layers, has no regularization effect, rather strongly influences the learning rate
** But learning rate in return is a regularization
! Introduction
$$
y_{NN}(X;\mathbf w, b) = g(X\mathbf w + b)
$$
* [[Batch Normalization]]: $y_{BN}(X;\mathbf w, \gamma, \beta) = g(\frac{X\mathbf w - \mu(X\mathbf w)}{\sigma(X\mathbf w)}\gamma + \beta)$
* [[Weight Normalization]]: $y_{WN}(X;\mathbf w, \gamma, \beta) = g(\frac{X\mathbf w}{\|\mathbf w\|_2}\gamma + \beta)$
* [[Layer Normalization|https://arxiv.org/abs/1607.06450]]: $y_{BN}(\mathbf x;W, \gamma, \beta) = g(\frac{\mathbf xW - \mu(\mathbf xW)}{\sigma(\mathbf xW)}\gamma + \beta)$
** instead of taking the statistics of a single unit over a whole batch of inputs, they are taken for a single input over all units in a layer
[[Making Neural Programming Architectures Generalize via Recursion|https://arxiv.org/abs/1704.06611]] Challenges: * Generalization to more complex inputs * Proof of generalization Approach: * Instantiation: incorporate recursion into Neural Programmer-Interpreter * Training methods: As a first step, strong supervision with explicit execution traces to learn a recursive neural program ! NPI * input: environment observation * list of predefined functions * NPI controller: A LSTM takes observation and caller func/args as inputs, modifies states and emits next operation NPI is trained with execution traces divided by caller functions.
Girshick et al.'s Region-CNN framework is a combination of region proposals and CNN features. It has three broad stages: # generating object proposals # extracting CNN feature maps of each proposal # filtering the proposal through class specific SVMs The bottleneck of performance of R-CNN is extracting feature maps for each region. In [[Fast R-CNN|Fast-RCNN]], they obtain $100\times$ speedup over R-CNN by computing the CNN features once and pooling them locally for each proposal; they also streamline the last two stages of R-CNN into a single multi-task learning problem. After that, the bottleneck becomes region-proposal stage. [[Lenc et al.|http://arxiv.org/abs/1506.06981]] drop the region proposal and use a constant set of regions learnt through K-means clustering on the PASCAL VOC data. [[Faster RCNN by Ren et al.|http://arxiv.org/abs/1506.01497]] starts from a fixed set of proposal, but refined them prior to detection by using a Region Proposal Network which shares weights with the later detection network and streamlines the multi-stage R-CNN framework. ! Examples # [[Google digit recognition]] # [[CNN for OCR]] # [[Face Detection]] # [[Semantic Segmentation]] ! Models !! Regressor # [[Faster R-CNN]] # [[darknet You Only Look Once (YOLO)|http://arxiv.org/abs/1506.02640]] # [[Single Shot Multibox Detector (SSD)|http://arxiv.org/abs/1512.02325]] # [[Inside Out Net (ION)|http://arxiv.org/abs/1512.04143]] # [[Speed/accuracy trade-offs for modern convolutional object detectors|https://arxiv.org/abs/1611.10012]] ## FRCNN accurate, SSD fast. ## SSD does not work well on small objects? or the information is inferred else where? # [[ARC-FCN|https://arxiv.org/abs/1612.00534]] # [[R-FCN|http://arxiv.org/abs/1605.06409]] ## [[Implementation|https://github.com/Orpine/py-R-FCN]] * [[RON]] !! Iterative # [[AttentionNet|http://arxiv.org/abs/1506.07704]] # [[G-CNN|http://arxiv.org/abs/1512.07729]] !! Other * [[The More You Know: Using Knowledge Graphs for Image Classification|https://arxiv.org/abs/1612.04844]] ** Graph Gated NN: use propagation like recurrent nets. ** Graph Search Neural Network !! Implementation # [[ReInspect|http://arxiv.org/abs/1506.04878]]: [[tf|https://github.com/Russell91/TensorBox]] ! Data * [[MS COCO]] ! Topics * [[Dealing with Context in Detection]] * Hard sampling ** [[OHEM|https://arxiv.org/abs/1604.03540]] ** [[A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection|https://arxiv.org/abs/1704.03414]]
! Dataset * 10k real internet images * 15k synthetic images simulating online ads in Chinese * 20k synthetic images simulating text in the wild in English ! Architecture * Horizontal BiLSTM after feature map * Faster RCNN for character feature extraction * RFCN for bounding box feature extraction
[[link|https://arxiv.org/abs/1609.04836]], [[code|https://github.com/keskarnitish/large-batch-training]], [[reddit discussion|https://www.reddit.com/r/MachineLearning/comments/53lexr/160904836v1_on_largebatch_training_for_deep/]] According to [[Hybrid Deterministic-Stochastic Methods for Data Fitting|https://arxiv.org/abs/1104.2373]] and [[Tail bounds for stochastic approximation|https://arxiv.org/abs/1304.5586]], there is also a notion of "sharpness" of minima which is appears as the condition number of the problem. Very relavent to a 1997 Schmidhuber's paper: [[Flat Minima|http://www.bioinf.jku.at/publications/older/3304.pdf]]. Further reading on opposite case: [[Sharp Minima Can Generalize For Deep Nets|https://arxiv.org/abs/1703.04933]]
There are at least exponentially many global minima for a neural net. Since permuating the nodes in one layer does not change the loss. Finding such points is not easy. Before certain techniques such as momentum came out, those nets were considered impossible to learn. Thanks to the constantly envolving hardwares and libraries, we do not have to worry about training time //that much// at least for convnets. Empirically, the non-convexity of neural nets seems not to be an issue. In practice, SGD works pretty well in optimizing very large networks even though the problem is proved to be NP-hard. However, researchers never stop studying the loss surface of deep neural nets and searching for better optimization strategies. [[This paper|https://arxiv.org/abs/1605.07110]] has been renewed on ArXiv recently, which leads me to [[this discussion|https://news.ycombinator.com/item?id=11765111]]. Following are what I find interesting. ! Why SGD works? [Choromaska et al, AISTATS'15] (also [Dauphin et al, ICML'15] use tools from Statistical Physics to explain the behavior of stochastic gradient methods when training deep neural networks. This offers a macroscopic explanation of why SGD "works", and gives a characterization of the network depth. The model is strongly simplified, and convolution is not considered. !! Saddle points We start from discussing saddle points, the vast majority of critical points on the error surfaces of neural networks. <<< Here we argue, ... that a deeper and more profound difficulty originates from the proliferation of saddle points, not local minima, especially in high dimensional problems of practical interest. Such saddle points are surrounded by high error plateaus that can dramatically slow down learning, and give the illusory impression of the existence of a local minimum. <<< Dauphin et al, [[Identifying and attacking the saddle point problem in high-dimensional non-convex optimization|http://arxiv.org/abs/1406.2572]] The authors introduce saddle-free Newton method which requires the estimation of Hessian. They connect the loss function of a deep net to a high-dimensional Gaussian random field. They show that critical points with high training error are exponentially likely to be saddle points with many negative directions, and all local minima are likely to have error that is very close to that of the global minimum. (Described in [[Entropy-SGD: Biasing Gradient Descent Into Wide Valleys|https://arxiv.org/abs/1611.01838]].) The convergence of gradient descent is affected by the proliferation of saddle points surrounded by high error plateaus --- as opposed to multiple local minima. <<< The time spent by diffusion is inversely proportional to the smallest negative eigenvalue of the Hessian at a saddle point <<< Kramer's law <<< It is believed that for many problems including learning deep nets, almost all local minimum have very similar function value to the global optimum, and hence finding a local minimum is good enough. <<< Rong Ge, [[Escaping from Saddle Points|http://www.offconvex.org/2016/03/22/saddlepoints/]] As the model grows deeper, local minima have loss closer to global minima. On the other hand, we do not care about global minimum because it often leads to overfitting. Saddle points exist along the paths between local minima, most objective functions have exponentially many of those. However, first order optimization algorithms may get stuck at saddle points. Strict saddle points can be escaped and global minima can be achieved in polynomial time [[(Ge et al., 2015)|http://arxiv.org/abs/1503.02101]]. Stochastic gradient introduces noise and help to push the current point away from saddle points. Non-convex problems can have ''degenerate saddle points'', whose Hessian is p.s.d. and have 0 eigenvalues. The performance of SGD on these kind of tasks is still not well studied. To conclude this part, AFAIK, we should care more about escaping from saddle point. And gradient based methods can do a better job than second-order methods in practice. !! Spin-glass Hamiltonian [[Charles Martin: Why Does Deep Learning Works?|https://charlesmartin14.wordpress.com/2015/03/25/why-does-deep-learning-work/]] Both papers mentioned above use ideas from statistical physics and spin-glass models. !!! Hamiltonians Statistical physicists refer to $H_x(y)\equiv-\ln p(y|x)$ as the ''Hamiltonian'', quantifying the energy of $y$ given the parameter $x$. And $\mu\equiv -\ln p$ as ''self-information''. We can rewrite Bayes' formula as: $$ p(y) = \sigma(-H(y)-\mu) $$ We can see the features yield by a neural net as Hamiltonian and the softmax computes the classification probability. !!! Spin-glass <<< The long-term behavior of certain neural network models are governed by the statistical mechanism of infinite-range Ising spin-glass Hamiltonians <<< LeCun et. al., [[The Loss Surfaces of Multilayer Networks, 2015|https://arxiv.org/abs/1412.0233]] In this paper, he tries to explain the optimization paradigm with spin-glass theory. A bolder hypothesis is that deep networks are spin funnels. And as the net gets larger, the funnel gets sharper. If this is true, our major concern should be to avoid over-training rather than the convexity of the network. ! What does the minima look like? !! No poor local minima <<< Deep Learning Networks are (@@color:#859900;roughly@@) Spin Funnels <<< a tweak from Charles' post [[Research at Google and Stanford|https://arxiv.org/abs/1412.6544]] confirms that the Deep Learning Energy Landscapes appear to be roughly convex. Finally we arrive at the paper itself. Nets are optimized well by local gradient methods and seems not to be affected by local minima. The author claims that every local minimum is a global minimum and "bad" saddle points (degenerated ones) exists for deeper nets. Thm 2.3 gives clear result on linear networks. The main result Thm 3.2 generalizes [[Choromanska et al, 2015|https://arxiv.org/abs/1412.0233]]'s idea for nonlinear network relies on 4 (seemingly strong) assumptions: # The dimensionality of the output is smaller than the input. # The inputs are random and decorrelated. # A connection in the network is activated or not is random with the same probability of success across the network. (ReLU thresholding happens randomly.) # The network activations are independent of the input, the weights and each other. They relax the majority of the asssumptions, which is very promising, but leave a weaker condition A1u-m and A5u-m [[(from reddit post)|https://www.reddit.com/r/MachineLearning/comments/4ktqeu/160507110_deep_learning_without_poor_local_minima/]]. Recently DeepMind came up with [[another paper|https://arxiv.org/abs/1611.06310]] claiming the assumptions are too strong for real data. And devised counter examples with finite datatets for rectified MLPs. For finite sized models/datasets, one does not have a globally good behavior of learning regardless of the model size. Even though deep learning energy landscapes appear to be roughly convex, or as this post referred to, local minimal free, a deep model has to include more engineering details to aid its convergence. Problems such as covariance shift and overfitting still have to be handled by engineering techniques. !! Arriving on flatter minima <<< large-batch methods tend to converge to sharp minimizers of the training and testing functions -- and that sharp minima lead to poorer generalization. In contrast, small-batch methods consistently converge to flat minimizers, and our experiments support a commonly held view that this is due to the inherent noise in the gradient estimation. <<< [[On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima]] ! Should 2-nd order methods ever work? Basiclly no. Because the Hessian vector product require very low variance estimation, which leads to batch size larger than 1000. But [[some rare cases|https://www.reddit.com/r/MachineLearning/comments/599wbr/project_i_accidentally_wrote_a_quasinewton_lbfgs/]] happen when 2nd order methods with small batch size works.
[[link|https://arxiv.org/abs/1702.00317]] SGD stalling has often been attributed to its sensitivity to the conditioning of the problem. But the reason should be more complicated.
! Bibs * [[Exact solutions to the nonlinear dynamics of learning in deep linear neural networks|https://arxiv.org/abs/1312.6120]] * [[Exponential expressivity in deep neural networks through transient chaos|https://arxiv.org/abs/1606.05340]] * [[On the Expressive Power of Deep Neural Networks|https://arxiv.org/abs/1606.05336]] * [[Deep Information Propagation|https://arxiv.org/abs/1611.01232]] ! Summary We have combined Riemannian geometry with dynamical mean field theory to study the emergent deterministic properties of signal propagation in deep nonlinear nets. We derived analytic recursion relations for Euclidean length, correlations, curvature, and Grassmannian length as simple input manifolds propagate forward through the network. We obtain an excellent quantitative match between theory and simulations Our results reveal the existence of a transient chaotic phase in which the network expands input manifolds without straightening them out, leading to "space filling" curves that explore many dimensions while turning at a constant rate. The number of turns grows exponentially with depth. Such exponential growth does not happen with width in a shallow net. Chaotic deep random networks can also take exponentially curved N-1 Dimensional decision boundaries in the input and flatten them into Hyperplane decision boundaries in the final layer: exponential disentangling! An order to chaos phase transition governs the dynamics of random deep networks, often used for initialization. Not all networks at the edge of chaos - with neither vanishing nor exploding gradients are created equal. The entire Jacobian singular value distribution, and not just its second moment impacts learning speed. We used introduced free probability theory to deep learning to compute this entire distribution. We found tanh networks with orthogonal weights have well conditioned Jacobians, but ReLU networks with orthogonal weights, or any network with Gaussian weights does not. Correspondingly, we found that with orthogonal weights, tanh networks learn Faster than ReLU networks.
[[blog|https://terrytao.wordpress.com/2017/07/11/on-the-universality-of-potential-well-dynamics/]] ''Theorem 1'' A smooth compact non-singular dynamics $(M, X)$ can be embedded smoothly in a potential well system iff it admits a strongly adapted 1-form. The if direction is mainly a comsequence of the Nash embedding theorem. The only if direction
* Siamese networks for one-shot classification * end2end differentiable nearest neighbor method for one-shot learning * LSTM-based one-shot optimizer ** [[Attentive Recurrent Comparators]] * DeepMind NIPS 2016: [[Matching Networks for One Shot Learning|https://arxiv.org/abs/1606.04080]] ** This model uses recent advances in attention and memory to achieve state-of-the-art performance classifying ImageNet images using only a single example from a class. However, we do not know what assumptions the network is making to classify these images.
! Theory
* [[Optimization Algorithms]]
* [[On the universality of potential well dynamics]]
! Convex Optimization
!! Bib
* [[Convex Optimization: Algorithms and Complexity|https://arxiv.org/abs/1405.4980]]
* [[Smooth Distributed Convex Optimization]]
! Non-Convex Optimization
!! Some properties to look for in non-convex optimization
References
* [[Geometry of linearized neural networks|http://blogs.princeton.edu/imabandit/2016/11/13/geometry-of-linearized-neural-networks/]]
* [[Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition|https://arxiv.org/abs/1608.04636]]
We say that a function $f$ admits ''first order optimality'' if all critical points of $f$ are global minima. in particular with first order optimality one has that gradient descent converges to the global minimum, and with second order optimality this is also true provided that @@color:#859900;one avoids saddle points@@. To obtain rates of convergence it can be useful to make more quantitive statements, e.g. $\alpha$-Polyak:
$$
\|\nabla f(x)\|^2\ge\alpha(f(x)-f^*).
$$
Clearly $\alpha$-Polyak implies first order optimality, also implies linear convergence rate. A variant of this condition is $\alpha$-weak-quasi-convexity:
$$
\langle\nabla f(x), x-x^*\rangle\ge\alpha(f(x)-f^*)
$$
in which case gradient descent converges at the slow non-smooth rate $1/\sqrt{t}$.
! Deep Learning
* [[On Optimization in Deep Learning]]
* [[Identity Matters in Deep Learning]]
* [[Optimizing RNN]]
* [[Escaping Saddle Points]]
! Tricks
According to chapter 1 of [[Neural Networks: Tricks of the Trade]]
* substract the means from the input variables
* normalize the variances of the input variables
* decorrelate the input variables
* use a separate learning rate for each weight
! First Order Methods
!! Videos
* [[Optimization for Machine Learning|https://simons.berkeley.edu/talks/elad-hazan-01-23-2017-1]]
* [[Theory of accelerated methods]]
!! Bib
* [[Adam: A Method for Stochastic Optimization|https://arxiv.org/abs/1412.6980]]
** When data features are sparse and bounded gradients, the adaptive method can achieve $O(\log d(\sqrt T)$, an improvement over $O(\sqrt{dT})$ for the non-adaptive method.
* [[AdaDelta]]
* [[A Variational Analysis of Stochastic Gradient Algorithms]]
* [[Entropy-SGD|https://arxiv.org/abs/1611.01838]]
** minus free energy from loss and solve with [[Stochastic Gradient Langevin Dynamics]].
!! Algorithms
* [[Stochastic Gradient Descent]]
* [[Nesterov's Accelerated Gradient Descent]]
* [[Follow-The-Regularized-Leader]]
* AdaGrad
!! Remarks
As is tested out by [[Karpathy|http://cs.stanford.edu/people/karpathy/convnetjs/demo/trainers.html]], Adagrad/Adadelta are "safer" because they don't depend so strongly on setting of learning rates (with Adadelta being slightly better), but well-tuned SGD+Momentum almost always converges faster and at better final values.
Adagrad was more stable (and less prone to crappy hyper-parameter choices) than Adam or constant or Barzilai-Borwein
! [[Second Order Methods]]
! Reference
* Learning to learn by gradient descent by gradient descent
! Algorithm
Use LSTM to guide the classifier. SGD resembles the update for the cell state in an LSTM
$$
c_t = f_t\odot c_{t-1} + i_t\odot \tilde c_t
$$
we set cell state be the model parameters $c_{t-1} = \theta_{t-1}$, input gate asa learning rate, $i_t=\alpha_t$, and candidate cell state be the negative gradient $\tilde c_t = -\nabla_{\theta_{t-1}}\mathcal L_t$.
* Meta-training: train learner
* Meta-testing: train meta-learner and best initialization
# Use LSTM to model parameter dynamics during training
#* LSTM parameters are shares across $M$'s parameters. Learning an update rule applied to each parameter independently, but each parameter has its own history.
#* Learn $c_0$, like learning $M$'s initialization
# Inputs to meta-learning LSTM are the loss and gradient of learner
#* preprocess by Andrychowicz et al (2016) to ensure loss and gradient are at the same scale
# Train LSTM according to test sets
#* ignore gradients through the inputs of the LSTM.
! Tricks in training
Parameter sharing across all coordinates of the learner gradient, but each has its own hidden and cell states. This is implemented by batching the loss and gradient along there dimensions. @@color:#859900;Batch size should be really large.@@
* Gradients and losses are normalized the same way as in reference.
* BP don't pass meta-learner.
* Init meta-learner's params small
! Remarks
There are works [[handling catastrophic forgetting|Overcoming catastrophic forgetting in NNs]]. Should think about it.
Resembles a sequential Gradient synthesizer.
refs
* [[Geometry of linearized neural networks|https://blogs.princeton.edu/imabandit/2016/11/13/geometry-of-linearized-neural-networks/]]
* Gradient Descent Learns Linear Dynamical Systems
** [[blog|http://www.offconvex.org/2016/10/13/gradient-descent-learns-dynamical-systems/]]
** [[paper|https://arxiv.org/abs/1609.05191]]
* [[Lecture Notes on Linear System Theory|http://control.ee.ethz.ch/~ifalst/docs/LectureNotes.pdf]]
The simplest version of a recurrent neural network is as follows. It is a mapping of the form $(x_1,\dots,x_T) \mapsto (y_1,\dots,y_T)$ (we are thinking of doing sequence to sequence prediction). In these networks the hidden state is updated as $h_{t+1} = \sigma_1(A h_{t} + B x_{t})$ (with $h_1=0$) and the output is $y_t = \sigma_2(C h_t + D x_t)$. With assumption that $(x_t)$ is an i.i.d. isotropic sequence we can decouple $D$ from $A, B, C$ and can be ignored because it's convex. We can consider the idealized risk:
$$
(\hat{A},\hat{B}, \hat{C}) \mapsto \sum_{k=0}^{+\infty} \|\hat{C} \hat{A}^{k} \hat{B} - C A^{k} B\|_F^2 .
$$
Consider the series $r_k = C A^k B$ and its Fourier transform:
$$
G(\theta) = \sum_{k=0}^{+\infty} r_k \exp(i k \theta) = C (\sum_{k=0}^{+\infty} (\exp(i \theta) A)^k) B = C(\mathrm{I} - \exp(i \theta) A)^{-1} B .
$$
By Parseval’s theorem the idealized risk is equal to the $L_2$ distance between $G$ and $\hat{G}$ (i.e. $\int_{[-\pi, \pi]} \|G(\theta)-\hat{G}(\theta)\|_F^2 d\theta$). We will now show that under appropriate further assumptions, for any $\theta$ that $\|G(\theta) - \hat{G}(\theta)\|_F^2$ is weakly-quasi-convex in $(\hat{A},\hat{B},\hat{C})$ (in particular this shows that the idealized risk is weakly-quasi-convex).
<<<
''The big assumption''<br>
the system is a “single-input single-output” model, that is both $x_t$ and $y_t$ are scalar.
<<<
In Section 9.2 of the lecture notes, it is proved that if a single-inupt single-output system $(A,B,C,D)$ satisfies @@color:#859900;$[B, AB, \dots, A^{n-1}B]\in \mathbb{R}^{n\times n}$ is of full rank@@, then there exists an invertible matrix $T$ such that $(TAT^{-1}, TB, CT^{-1}, D)$ is an equivalent system with the canonical controllable form where $B=(0,\dots,0,1)$, $C=(c_1,\dots,c_n)$ and $A$ has zeros everywhere except on the upper diagonal where it has ones and on the last row where it has $a_n,\dots, a_1$. This kind of system is called controllable system (for some other reason).
<<<
''Theorem'' [Hardt, Ma, Recht 2016]<br>
Let $C(a) := \{z^n + a_1 z^{n-1} + \dots + a_n , z \in \mathbb{C}, |z|=1\}$ and assume there is some cone $\mathcal{C} \subset \mathbb{C}^2$ of angle less than $\pi/2-\alpha$ such that $C(a) \subset \mathcal{C}$. Then the idealized risk is $\alpha$-weakly-quasi-convex on the set of $\hat{a}$ such that $C(\hat{a}) \subset \mathcal{C}$.
<<<
The gradients of the RNN are easy to compute with BPTT, RNNs are difficult to train, especially on problems with long-range temporal dependencies due to their nonlinear iterative nature. The derivative of the loss function at one time can be exponentially large w.r.t. the hidden activations at a much earlier time. ! Vanishing gradient problem Here is a second order approach: [[Thesis Training RNN]]. Not considered a good solution now. Merity has [[a blog post|https://smerity.com/articles/2016/orthogonal_init.html]] about using orthogonal initialization to avoid explosion and vanishing in propagation, and has referred to some optimization papers. ! [[Optimization Properties of Linearized RNN]]
[[start from here|http://doc.norang.ca/org-mode.html]] I have created the most simple .org file under `~/Documents` ! Ideas * tag is for tasks. * fast data/time
```python
#!/usr/bin/python
#
# peirce_dev.py
# created 16 Jul 2013
# updated 23 Oct 2014
#
#### MODULES ####
import numpy
import scipy.special
#### FUNCTION ####
def peirce_dev(N, n, m):
"""
Name: peirce_dev
Input: - int, total number of observations (N)
- int, number of outliers to be removed (n)
- int, number of model unknowns (m)
Output: float, squared error threshold (x2)
Features: Returns the squared threshold error deviation for outlier
identification using Peirce's criterion based on Gould's
methodology
"""
# Assign floats to input variables:
N = float(N)
n = float(n)
m = float(m)
#
# Check number of observations:
if N > 1:
# Calculate Q (Nth root of Gould's equation B):
Q = (n**(n/N)*(N - n)**((N - n)/N))/N
#
# Initialize R values (as floats)
Rnew = 1.0
Rold = 0.0 # <- Necessary to prompt while loop
#
# Start iteration to converge on R:
while ( abs(Rnew - Rold) > (N*2.0e-16) ):
# Calculate Lamda
# (1/(N-n)th root of Gould's equation A'):
ldiv = Rnew**n
if ldiv == 0:
ldiv = 1.0e-6
Lamda = ((Q**N)/(ldiv))**(1.0/(N - n))
#
# Calculate x-squared (Gould's equation C):
x2 = 1.0 + (N - m - n)/n*(1.0 - Lamda**2.0)
#
# If x2 goes negative, return 0:
if x2 < 0:
x2 = 0.0
Rold = Rnew
else:
# Use x-squared to update R (Gould's equation D):
Rold = Rnew
Rnew = (
numpy.exp((x2 - 1)/2.0)*
scipy.special.erfc(numpy.sqrt(x2)/numpy.sqrt(2.0))
)
#
else:
x2 = 0.0
return x2
```
One question that outlier detection aims to answer can be phrased: <<< Given $n$ independent random variables from a common, but unknown, distribution $\mu$, does a new input $X$ belong to the support of $\mu$? <<<
! EWC
[[link|http://www.pnas.org/content/early/2017/03/13/1611835114.abstract]]
Elastic weight consolidation is best described as online sequential (diagonalised) Laplace approximation. This is similar to [[assumed density filtering|http://publications.aston.ac.uk/1444/1/NCRG_99_029.pdf]] the precursor to expectation-propagation. Laplace approximation takes a probability density $p$ and approximates it with a Gaussian. By Bernstein-von Misestype convergence theorems, the posterior will converge to the Laplace. EWC use a diagonalised Hessian for variance estimation.
For the first task, the Hessian would be the Fisher information from task $A$, $F^A$, plus the Hessian of the log prior $\log p(\theta)$.
Bayesian inference wouldn't suffer from catastrophic forgetting which haunts optimization-based methods. But the full posterior is intractable.
* EWC approximated Bayesian computation
The problem is begins at the third task, the right approximation
$$
\log p(\theta|\mathcal D_A, \mathcal D_B)\approx -\sum_i(F^A+F^B)_{i, i}(\theta_i-\theta_i^{A, B})^2
$$
The penalty around $\theta^A$ has already been taken account.
EWC further required the knowledge of the task it is performing. The tasks here can be different minibatches of the same task.
! Generative Replay
Use a GAN to generate samples of previous tasks.
Done: * add user to `audio` group * installed `jack2` Failed because cannot connect to internal server with `(use 'overtone.live)`
! MI Team reading schedule !! Guidelines # The lecturer is encouraged to pick his own material, even beyond the list or our recent study fields, as long as he finds his topic more helpful to the group. If so, create your own entry in the list beforehand. # The lecturer is suggested to upload the slides to [[this git repo|https://g.hz.netease.com/mi-tools/mi-course/tree/dev/seminar]] before seminar. # The scribe should keep the notes of the seminar for later reference. A nice script format is `.pdf` or `.html`. Choosing between LaTeX/Markdown as the markup language according to the lecture content is strongly recommended. You may download the LaTeX template from [[here|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/template.zip]]. !! Rotation |!Lecturer |!Scribe | |郭贺 |杨旭东 | |张晓博 |陈昊 | |侯章军 |李一夫 | |周立峰 |郭贺 | |杨旭东 |张晓博 | |陈昊 |侯章军 | |刘丽娟 |周立峰 | |李一夫 |刘丽娟 | !! Listed papers |!Subject |!Title |!Conf |!Lecturer |!(Planned) Date |!Materials | |Theory |[[Understanding Deep Convolutional Networks|Understanding Deep ConvNets (Scattering Repr)]] | PTA 2016| xiaobo| 7/26/2016|| |Theory |[[Provable Bounds for Learning Some Deep Representations|http://arxiv.org/abs/1310.6343]] | ICML 2014|||| |Theory |[[Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift|Batch Normalization]] | JMLR 2015| 李一夫| 8/2/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/16.8.2Batch_Normalization.pptx]]| |Theory |[[Rethinking the Inception Architecture for Computer Vision (Inception-v3)|http://arxiv.org/abs/1512.00567]] |ArXiv 12/2015| 刘丽娟| 8/23/2016|[[slides]]| |Theory |[[Understanding and Improving Convolutional Neural Networks via Concatenated Rectified Linear Units|Concatenated Rectified Linear Units]] | ICML 2016|||| |Theory |[[Noisy Activation Functions|http://arxiv.org/abs/1603.00391]] | ArXiv 3/2016|||| |Theory |[[Large-Margin Softmax Loss for Convolutional Neural Networks|http://jmlr.org/proceedings/papers/v48/liud16.pdf]] | ICML 2016|||| |Theory |[[Layer Normalization|https://arxiv.org/abs/1607.06450]]| ArXiv 7/2016|||| |Tricks |[[Efficient BackProp|http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf]]| 1992|||| |Tricks |[[Mollifying Networks]]| ArXiv 8/2016|||| |Others |[[Gradient-based Hyperparameter Optimization through Reversible Learning]] | ICML 2015|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/hypergrad-talk.pdf]]| |Others |[[Synthesized Classifiers for Zero-Shot Learning|https://arxiv.org/abs/1603.00550]] | CVPR 2016|||[[vgg slides]]| |Others |[[Kernel Mean Embedding of Distributions: A Review and Beyonds|http://arxiv.org/abs/1605.09522]] | ArXiv 5/2016| 陈昊| 7/19/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/20160719_kernel_tricks.pdf]]| |Others |[[Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition|http://arxiv.org/abs/1503.02101]] | ICML 2016|||| |Vision |[[Deep Face Recognition|http://www.robots.ox.ac.uk:5000/~vgg/publications/2015/Parkhi15/parkhi15.pdf]] [[FaceNet: A Unified Embedding for Face Recognition and Clustering|http://120.52.73.12/www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Schroff_FaceNet_A_Unified_2015_CVPR_paper.pdf]] [[Deeply learned face representations are sparse, selective, and robust|http://120.52.73.11/www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Sun_Deeply_Learned_Face_2015_CVPR_paper.pdf]] | BMVC 2015| 杨旭东| 6/21/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/85ffa6afd1cc7c897e0ab7b07020228a787d3ef8/seminar/20160621_Face_Recognition.pptx]] [[notes]]| |Vision |[[Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks|Faster R-CNN]] | NIPS 2015| 郭贺| 7/5/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/Object_Detection.pptx]] [[notes]] | |Vision |[[Going Deeper With Convolutions (GoogLeNet)|GoogLeNet]] | CVPR 2015| xiaobo| 6/7/2016|[[notes|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/lecture-notes/seminar1.pdf]]| |Vision |[[Augmenting Supervised Neural Networks with Unsupervised Objectives for Large-scale Image Classification|http://jmlr.org/proceedings/papers/v48/zhangc16.pdf]] | ICML 2016|||| |Vision |[[Learning End-to-end Video Classification with Rank-Pooling|http://jmlr.org/proceedings/papers/v48/fernando16.pdf]] | ICML 2016|||| |Vision |[[Bottom-Up and Top-Down Reasoning With Hierarchical Rectified Gaussians|https://arxiv.org/abs/1507.05699]] | CVPR 2016|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/RG21Jul16_VGG.pdf]]| |Vision |[[DenseCap: Fully Convolutional Localization Networks for Dense Captioning|http://cs.stanford.edu/people/karpathy/densecap/]] | CVPR 2016|||| |Vision |[[G-CNN: an Iterative Grid Based Object Detector|http://arxiv.org/abs/1512.07729]] | CVPR 2016|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/GCNN.pdf]]| |Vision |[[Learning Deep Features for Discriminative Localization|https://github.com/jazzsaxmafia/Weakly_detector]] | CVPR 2016| 郭贺| 5/31/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/16.5.31.guohe.pptx]] [[notes]]| |Vision |[[NetVLAD: CNN Architecture for Weakly Supervised Place Recognition|http://arxiv.org/abs/1511.07247]] | CVPR 2016|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/NetVLAD.pptx]]| |Vision |[[Newtonian Scene Understanding: Unfolding the Dynamics of Objects in Static Images|http://arxiv.org/abs/1511.04048]] | CVPR 2016|||[[vgg slides|http://www.robots.ox.ac.uk/~vgg/rg/slides/RG21Jul16_VGG.pdf]]| |Vision |[[Synthetic Data for Text Localisation in Natural Images]]| CVPR 2016| 陈昊| 6/14/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/20160614_Reading_Text_in_Natural_Images.pptx]] [[notes|Synthetic Data for Text Localisation in Natural Images]]| |Vision |[[WarpNet: Weakly Supervised Matching for Single-View Construction|https://arxiv.org/abs/1604.05592]] | CVPR 2016|||| |Vision |[[Identity Mappings in Deep Residual Networks|https://arxiv.org/abs/1603.05027]] | ArXiv 7/2016|||| |Vision |[[T-CNN: Tubelets with Convolutional Neural Networks|http://gitxiv.com/posts/do59FBaD68smgdpF6/t-cnn-tubelets-with-convolutional-neural-networks]] | ArXiv 5/2016|||| |Video |[[Deep learning for detecting multiple space-time action tubes in videos|http://www.robots.ox.ac.uk/~vgg/rg/papers/saha16bmvc.pdf]] | BMVC 2016|||| |Video |[[Multi-region two-stream R-CNN for action detection|http://www.robots.ox.ac.uk/~vgg/rg/papers/peng16eccv.pdf]] | ECCV 2016|||| |NLP |[[Distributed Representations of Words and Phrases and their Compositionality(word2vec)|Word2Vec]] | NIPS 2013| 周立峰| 9/27/2016|| |NLP |[[Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling|https://arxiv.org/abs/1412.3555]] | NIPS 2014| xiaobo| 8/30/2016|| |NLP |[[Convolutional Neural Networks for Sentence Classification]] | EMNLP 2014| 周立峰| 7/5/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/Convolutional-Neural-networks-for-sentence-classification.pptx]] [[notes]]| |NLP |[[A Neural Conversational Model|https://github.com/macournoyer/neuralconvo]] | ICML 2015|||| |NLP |[[Virtual Adversarial Training for Semi-Supervised Text Classification|http://arxiv.org/abs/1605.07725]] | ArXiv 5/2016| 周立峰| 8/9/2016|| |Vision |[[Recent Advances in Convolutional Neural Networks|http://arxiv.org/abs/1512.07108]] | ArXiv 12/2015| 郭贺| 8/16/2016|[[slides|https://g.hz.netease.com/mi-tools/mi-course/blob/dev/seminar/2016_8_16_CNN_survey_gh.pptx]] [[notes]]| |RNN |[[LSTM: A Search Space Odyssey|http://arxiv.org/abs/1503.04069]]| ArXiv 3/2015| 侯章军| 6/28/2016 (posponded to 7/5)|[[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/20160705_lstm.pptx]] [[notes|Long short-term memory]]| |RNN |[[Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations]] | ArXiv 6/2016|||| |RNN |[[Learning to learn by gradient descent by gradient descent|https://arxiv.org/abs/1606.04474]] | ArXiv 6/2016|||| |RL |[[Action-Conditional Video Prediction using Deep Networks in Atari Games|http://arxiv.org/abs/1507.08750]] | NIPS 2015|||| |RL |[[Asynchronous Methods for Deep Reinforcement Learning|https://arxiv.org/abs/1602.01783]] | ICML 2016|||| |RL |[[Value Iteration Networks|http://arxiv.org/abs/1602.02867]] | ICML 2016|||| |DL Applications |[[Unsupervised Deep Embedding for Clustering Analysis|http://jmlr.org/proceedings/papers/v48/xieb16.pdf]] | ICML 2016|||| |DL Applications |[[Learning Convolutional Neural Networks for Graphs|http://jmlr.org/proceedings/papers/v48/niepert16.pdf]] | ICML 2016| ||| |DL Applications |[[Deep Speech 2 : End-to-End Speech Recognition in English and Mandarin |http://jmlr.org/proceedings/papers/v48/amodei16.pdf]] | ICML 2016|||| !! Other talks # 6/21/2016, Boosting, 李一夫, [[slides|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/lecture-notes/20160621.pptx]] [[notes|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/lecture-notes/seminar4.pdf]] # 10/9/2016, Survey: CNN for Video Classification, 陈昊, [[slides|https://docs.google.com/presentation/d/1iAWg2vwUe7CI0AcaMVfIPxDfjW-o1KEOZVsVrqsEMis/edit?usp=sharing]]
Music~ <iframe style="border: 0; width: 100%; height: 120px;" src="https://bandcamp.com/EmbeddedPlayer/album=1327228511/size=large/bgcol=ffffff/linkcol=0687f5/tracklist=false/artwork=small/transparent=true/" seamless><a href="http://wormwood-official.bandcamp.com/album/ghostlands-wounds-from-a-bleeding-earth">Ghostlands - Wounds From a Bleeding Earth by Wormwood</a></iframe> ! MI Team reading schedule !! Guidelines # The lecturer is encouraged to pick his own material, even beyond the list or our recent study fields, as long as he finds his topic more helpful to the group. If so, create your own entry in the list beforehand. You can find some interesting papers from #* [[Arxiv Sanity Preserver|http://www.arxiv-sanity.com/]] #* [[DeepMind Publications|https://deepmind.com/research/publications/]] #* [[FAIR Publications|https://research.fb.com/publications/]] #* [[Google Brain Publications|https://research.google.com/pubs/BrainTeam.html]] # The lecturer is suggested to upload the slides to [[this git repo|https://g.hz.netease.com/mi-tools/mi-course/tree/dev/seminar]] before seminar. # The scribe should keep the notes of the seminar for later reference. A nice script format is `.pdf` or `.html`. Choosing between LaTeX/Markdown as the markup language according to the lecture content is strongly recommended. You may download the LaTeX template from [[here|https://g.hz.netease.com/mi-tools/mi-course/raw/dev/seminar/template.zip]]. !! Rotation |!Lecturer |!Scribe | |郭贺 |杨旭东 | |胡孟 |侯章军 | |陈昊 |胡孟 | |汪丰 |魏凯峰 | |刘智雯 |周立峰 | |杨旭东 |刘智雯 | |邸新汉 |陈昊 | |侯章军 |邸新汉 | |刘丽娟 |曽杨 | |周立峰 |郭贺 | |魏凯峰 |刘丽娟 | |曽杨 |汪丰 | !! Listed papers Let's start from some 2017 conference orals. Each paper has plenty of discussion online to help with understanding. |!Subject |!Title |!Conference |!Lecturer |!(Planned) Date |!Materials | |Vision |Learning Transferable Architectures for Scalable Image Recognition | arXiv 2017| | || |Vision |Dual Path Networks | arXiv 2017| | || |Vision |Attention-based Extraction of Structured Information from Street View Imagery | arXiv 2017|汪丰 |7/18 || |Vision |[[Interleaved Group Convolutions for Deep Neural Networks|https://arxiv.org/abs/1707.02725]] | ICCV 2017|侯章军 | || |Vision |Feature Pyramid Networks for Object Detection | CVPR 2017|胡孟 |7/4|| |Vision |A-Fast-RCNN: Hard positive generation via adversary for object detection | CVPR 2017|周立峰 |7/18 || |Vision |Fully Convolutional Instance-aware Semantic Segmentation | CVPR 2017|胡孟 |8/1 |[[GitHub|https://github.com/msracver/FCIS]]| |Vision |YOLO9000: Better, Faster, Stronger | CVPR 2017|魏凯峰 |8/15 || |Vision |Look Closer to See Better: Recurrent Attention Convolutional Neural Network for Fine-Grained Image Recognition | CVPR 2017|刘丽娟 | || |Vision |UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision using Diverse Datasets and Limited Memory | CVPR 2017| | || |Vision |Network Dissection: Quantifying Interpretability of Deep Visual Representations | CVPR 2017|杨旭东 | || |Vision |AGA: Attribute Guided Augmentation | CVPR 2017| | |[[arxiv|https://arxiv.org/pdf/1612.02559.pdf]],[[Github-Torch|https://github.com/rkwitt/GuidedAugmentation]]| |Vision |Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data | ICLR 2017| | |[[arxiv|https://arxiv.org/abs/1610.05755]][[No Open Source|..]]| |Vision |End-to-end Optimized Image Compression | ICLR 2017| | || |Vision |Amortised MAP Inference for Image Super-resolution | ICLR 2017| | || |Modelling |Neural Architecture Search with Reinforcement Learning | ICLR 2017|邸新汉 |10/24 |[[arxiv|https://arxiv.org/abs/1611.01578]],[[github-chainer|https://github.com/nutszebra/neural_architecture_search_with_reinforcement_learning_appendix_a]],[[DenseNet-Compared-Github|https://github.com/liuzhuang13/DenseNet]]| |GAN |Towards Principled Methods for Training Generative Adversarial Networks | ICLR 2017|邸新汉 |7/25 |[[WGAN|https://arxiv.org/abs/1701.07875]],[[Github-Tensorflow|https://github.com/shekkizh/WassersteinGAN.tensorflow]]| |GAN |Coulomb GANs: Provably Optimal Nash Equilibria via Potential Fields | arXiv 2017| | | | |NLP |Learning End-to-End Goal-Oriented Dialog | ICLR 2017| | || |Theory |Probabilistic Deep Learning | DALI 2017|陈昊 |7/11 |<li>[[slides|https://docs.google.com/presentation/d/1DQpGu7sgnh787Hqjs1cBq485rbuZnylqcWefiCJr8PQ/edit?usp=sharing]]</li><li>[[Statistical Measures]]</li><li>[[fGAN]]</li><li>[[Variational Autoencoder]]</li>| |Theory |Global Optimality in Neural Network Training | ICLR 2017| | || |Theory |[[On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima]] | ICLR 2017| | || |RL |Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic | CVPR 2017| | |[[arxiv|https://arxiv.org/abs/1611.02247]],[[Github-Tensorflow-rllab++|https://github.com/shaneshixiang/rllabplusplus]]| |RL |Learning to Act by Predicting the Future | ICLR 2017| | || |RL |Reinforcement Learning with Unsupervised Auxiliary Tasks | ICLR 2017| | |[[arxiv|https://arxiv.org/abs/1611.05397]],[[Github-Tensorflow+DeepMind+Environment|https://github.com/miyosuda/unreal]]| ! Previous Sessions * [[Deep Learning Seminar]] * [[Paper Reading Group 2016]]
[[Conferences]]
! Reading List
!! Audio
!!! Speech
* [[Emotional Speaker Recognition Thesis]]
* [[Bayesian GMM]]
* [[Salient Acoustic Event Detection]]
* [[Speech Synthesis]]
* [[Sytle & Content]]
[[Feature Extraction]]
!!! [[Scattering Representation]]
!!!! Unfinished
<$list filter="[!has[draft.of]tag[Scattering Representation]!tag[done]sort[created]]">
<$checkbox tag="done"> <$link to={{!!title}}><$view field="title"/></$link></$checkbox>
</$list>
!!!! Finished
<$list filter="[!has[draft.of]tag[Scattering Representation]tag[done]sort[created]]">
<$checkbox tag="done"> ~~<$link to={{!!title}}><$view field="title"/></$link>~~</$checkbox>
</$list>
!! Machine Learning
[[Nonparametric Bayes]]
!!! [[NIPS]]
!!! [[ICML]]
!!!! Unfinished
<$list filter="[!has[draft.of]tag[NIPS]!tag[done]sort[created]]">
<$checkbox tag="done"> <$link to={{!!title}}><$view field="title"/></$link></$checkbox>
</$list>
!!!! Finished
<$list filter="[!has[draft.of]tag[Scattering Representation]tag[done]sort[created]]">
<$checkbox tag="done"> ~~<$link to={{!!title}}><$view field="title"/></$link>~~</$checkbox>
</$list>
! Bibliographies
* [[Nonparametric Bayes|Nonparametric Bayes Bib]]
* [[Superiorization|http://math.haifa.ac.il/YAIR/bib-superiorization-censor.html]]
* [[Deep Learning|Deep Learning Bib]]
* [[Audio Signal Processing]]
* 10.1 [[Variational Inference]] * 10.2 [[Variational Mixture of Gaussians]]
[[definition|https://en.wikipedia.org/wiki/Perplexity]]
Perplexity is sometimes used as a measure of how hard a prediction problem is. This is not always accurate. If you have two choices, one with probability 0.9, then your chances of a correct guess are 90 percent using the optimal strategy. The perplexity is $2^{−0.9 \log_2 0.9 - 0.1 \log_2 0.1}= 1.38$. The inverse of the perplexity (which, in the case of the fair k-sided die, represents the probability of guessing correctly), is 1/1.38 = 0.72, not 0.9.
The perplexity is the exponentiation of the entropy, which is a more clearcut quantity. The entropy is a measure of the expected, or "average", number of bits required to encode the outcome of the random variable, using a theoretical optimal variable-length code, cf. the next section. It can equivalently be regarded as the expected information gain from learning the outcome of the random variable, where information is measured in bits.
* [[paper|http://jmlr.org/proceedings/papers/v48/diamos16.pdf]] * [[reddit|https://www.reddit.com/r/MachineLearning/comments/4pezja/persistent_rnns_stashing_recurrent_weights_onchip/]] The peak floating point throughput of a TitanX is 6.144 TFLOP/s. A straightforward implementation of a RNN using GEMM operations achieves 0.099 TFLOP/s at a layer size of 1152 using Nervana Systems GEMM kernels at a mini-batch size of 4. Our initial Persistent RNN implementation with the same layer and mini-batch size achieves over 2.8 TFLOP/s resulting in a 30x speedup. Synchronization between GPU processors cores is typically achieved implicitly between dependent kernel calls in both CUDA and OpenCL development frameworks. However, this mechanism for synchronization between timesteps requires launching a new kernel that forces the weights to be reloaded from off-chip memory. This causes the synchronization latency of dependent kernels to be approximately 6-10x larger than the time spent performing the math operations for a single timestep, and this cannot be overlapped with computation. We address this problem with an optimized implementation of a global barrier that can be completely overlapped with the math operations for a single timestep. You can synchronize thread blocks using atomic operations in global memory, as long as you take the weak memory model into account. The hardest part is providing a guarantee of concurrency to more than one thread block. There are multiple ways to solve this problem, but we implemented cooperative multithreading where threads will yield the machine if too many synchronizations fail. CUDA doesn't expose a way to yield. Our approach saves a continuation in GPU memory and then aborts the thread block. Code on the CPU monitors the continuation memory and restarts the kernel if any thread blocks fail. We size the kernel so that this is unlikely to happen, and in practice it occurs extremely rarely.
[[link|https://arxiv.org/abs/1609.04802]] * Lower SNR than SRResNet * The tweak on perceptual ResNet might be useful ! Models Generator does not downsample
! Bibs * [[Pixel-RNN|https://arxiv.org/abs/1601.06759]] * [[Conditional Image Generation with PixelCNN Decoders|https://arxiv.org/abs/1606.05328]] ** use horizontal stack with skip connection for current row, vertical stack for previous rows. no blind spot. ** 1xn and nxn masked convolution can be implemented by half-sized convs followed by a shift in pixels by padding and cropping. ** [[tensorflow implementation|https://github.com/anantzoid/Conditional-PixelCNN-decoder]] * Generating interpretable images with controllable structure * ! Implementations * Pixel-CNN++ ** OpenAI implementation with python3/tf. quite memory consuming (even though optimized from original implementation by logistic mixture sampling of pixels), 12G takes 12 in a batch. ** [[tensorflow implementation|https://github.com/openai/pixel-cnn]] * [[fast-pixel-cnn|https://github.com/PrajitR/fast-pixel-cnn]]: speed up with cache trick for [[Dilated Convolution]] ! Gated conv * [[Conditional Image Generation with PixelCNN Decoders|https://arxiv.org/abs/1606.05328]]
A discrete random variable $X$ is said to have a Poisson distribution with parameter $\lambda > 0$, if, for $k = 0, 1, 2, \ldots$, the probability mass function of $X$ is given by:
$
\!f(k; \lambda)= \Pr(X=k)= \frac{\lambda^k e^{-\lambda}}{k!},
$
where
* $e$ is Euler's number ($e = 2.71828\dots$)
* $k!$ is the factorial of $k$.
The positive real number $\lambda$ is equal to the expected value of $X$ and also to its variance
$$
\lambda=\operatorname{E}(X)=\operatorname{Var}(X).
$$
The Poisson distribution can be applied to systems with a large number of possible events, each of which is rare. How many such events will occur during a fixed time interval? Under the right circumstances, this is a random number with a Poisson distribution.
! Definition
Total loss:
$$
J(\theta)=E_{\tau\sim\pi_\theta(\tau)}[r(\tau)] = \int\pi_\theta(\tau)r(\tau)d\tau
$$
policy function is assumed to be Markovian:
$$
\pi_\theta(\tau) = p(s_1)\prod_{t=1}^T\pi_\theta(a_t|s_t)p(s_{t+1}|s_t, a_t)
$$
When we take the gradient, we ignore $p(s_1)$ and $p(s_{t+1}|s_t, a_t)$ term. We make the common log trick $\frac{\nabla x}{x}=\nabla\log x$ to allow sampling from paths $\tau$:
$$
\nabla_\theta J(\theta)=E_{\tau\sim\pi_\theta(\tau)}[(\sum_{t=1}^T\nabla_\theta\log\pi_\theta(a_t|s_t))(\sum_{t=1}^Tr(s_t, a_t))]
$$
with [[REINFORCE]], we sample $\tau^i$ from $\pi_\theta(a_t|s_t)$ and iteratively update $\theta\leftarrow\theta+\alpha\nabla_\theta J(\theta)$. This is valid even when:
* $r$ is discontinuous and/or unknown.
* Sample space (of paths) is a discrete set.
Policy can be generated by a neural network:
$$
\pi_\theta(a_t|s_t) = \mathcal N(f(s_t); \Sigma)
$$
We can then do back propagation on the model.
The problem with policy gradient is that it is hard to choose learning rate and convergence is slow due to high variance.
We should introduce causality because policy at time $t'$ cannot effect reward at time $t$ when $t<t'$.
! Comparisons
* Often $\pi$ can be simpler than $Q$ or $V$, e.g., robotic grasp
* $V$: doesn't precribe actions
** Would need dynamics model (+ compute 1 Bellman back-up)
* $Q$: need to be able to efficiently solve $\arg\max_u Q_\theta(s, u)$
** Challenge for continuous / high-dimensional action spaces
*** NAF: Gu, Lillicrap, Sutskever, Levine ICML 2016
*** Input Convex NNs: Amos, Xu, Kolter arXiv 2016
*** Deep Energy Q: Haarnoja, Tang, Abbeel, Levine, ICML 2017
Policy optimization:
* More compatible with rich architectures (including recurrence)
* More versatile
* More compatible with auxiliary objectives
Dynamic programming:
* More compatible with exploration and off-policy learning
* More sample-efficient when they work
! Improvements
* [[AlphaGo Zero]]
! Bibs
* Classic papers
** Simple statistical gradient-following algorithms for connectionist reinforcement learning: introduces REINFORCE algorithm
** Infinite-horizon policy-gradient estimation: temporally decomposed policy gradient
** Reinforcement learning of motor skills with policy gradients: very accessible overview of optimal baselines and natural gradient
* Deep RL
** Guided policy search: deep RL with importance sampled policy gradient
** Trust region policy optimization: deep RL with natural policy gradient and adaptive step size
** Proximal policy optimization algorithms: deep RL with importance sampled policy gradient
In order to reduce variance, pooling layers compute the max or average value of a particular feature over a region of the image. This will ensure that the same result will be obtained, even when image features have small translations. This is an important operation for object classification and detection.
```css
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3 /* pool over a 3x3 region */
stride: 2 /* step two pixels (in the bottom blob) between pooling regions */
}
}
```
$$
z_n = \text{subsample}(x, g)[n] = g(x_{(n-1)m+1:nm})
$$
! Basic
!! Max pooling
$$
g(x) = \max(x), \frac{\partial g}{\partial x_i} = 1_{x_i = \max(x)}
$$
In backpropagation, the backward pass for a $\max(x, y)$ operation has a simple interpretation as only routing the gradient to the input that had the highest value in the forward pass.
!! $L^p$ pooling
$$
g(x) = \|x\|_p = \left(\sum_{k=1}^m|x_k|^p\right)^{1/p}, \frac{\partial g}{\partial x_i} = \left(\sum_{k=1}^m|x_k|^p\right)^{1/p-1}|x_i|^{p-1}
$$
Mean pooling is a special case where $p=1$.
But i am confused by [[this post|http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/]].
! Recent developments
* [[Fractional Max-Pooling|http://arxiv.org/abs/1412.6071]] suggests a method for performing the pooling operation with filters smaller than 2x2. This is done by randomly generating pooling regions with a combination of 1x1, 1x2, 2x1 or 2x2 filters to tile the input activation map. The grids are generated randomly on each forward pass, and at test time the predictions can be averaged across several grids.
* [[Striving for Simplicity: The All Convolutional Net|http://arxiv.org/abs/1412.6806]] proposes to discard the pooling layer in favor of architecture that only consists of repeated CONV layers. To reduce the size of the representation they suggest using larger stride in CONV layer once in a while.
* Parts-based pooling: detect ROIs and concat pooled features for each part to form the image representation. Used in fine-grained classification and image retrieval.
Due to the aggressive reduction in the size of the representation (which is helpful only for smaller datasets to control overfitting), the trend in the literature is towards discarding the pooling layer in modern ConvNets.
! Bibs * [[Stochastic Portfolio Theory]] * Non-Euclidean metrics: [[Geometric Learning and Filtering in Finance|https://arxiv.org/abs/1710.05829]] ! Theory Markowitz modern portfolio theory (1959): * ''Trade-off the return and risk:'' one should focus on the trade-off between expected return and the risk measured by the standard deviation * This leads to a quadratic optimization with linear constraints. * The efficient portforlio is an affine function of the expected return. Capital Asset Pricing Model (CAPM) generalizes Markowitz by involving a riskless bond.
The most common definition of PCA, due to Hotelling (1933), is that, for a set of observed $d$-dimensional data vectors $\{t_n\}$, the $J$ principal axes $w_j$, are those orthonormal axes onto which the retrained variance under projection is maximal. It can be shown that the vectors $w_j$ are given by the $q$ dominant eigenvectors of the sample covariance matrix
$$
S = \sum_n(t_n-\bar t)(t_n-\bar t)^\top/N
$$
such that
$$
Sw_j = \lambda_jw_j.$$
The vector $x_n = W^\top(t_n-\bar t)$ is thus a $q$-dimensional reduced representation of the observed vector $t_n$.
A complementary property of PCA, and that most closely related to the original discussions of Pearson (1901), is that the projection onto the pricipal subspace minimizes the squared reconstruction error $\sum\|t_n-\bar t\|^2$.
The principle states that, subject to precisely stated prior data, the probability distribution which best represents the current state of knowledge is the one with largest entropy. The principle of maximum entropy expresses a claim of maximum ignorance. The selected distribution is the one that makes the least claim to being informed beyond the stated prior data, i.e., admitting the most ignorance beyond the stated prior data. [[Pitman-Koopman theorem]] states that the necessary and sufficient condition for a sampling distribution to admit sufficient statistics of bounded dimension is that it have the general form of a maximum entropy distribution.
Priors have traditionally belonged to one of two classes: informative priors and uninformative priors. ! Informative Priors Ibrahim and Chen indtroduced the power prior. The power prior is a class of informative prior distribution that takes previous data and resuts into account. ! Weakly Informative Priors Weakly Informative Prior distributions use prior information for regularization and stabilization, providing enough prior information to prevent results that contradict our knowledge or problems such as an algorithmic failure to explore the state-space.
* [[Statistical Measures]] * [[Inference Methods]] There are 2 types of genrative models: * [[Fully-observed Models]]: model from data directly * [[Latent Variable Models]] ** prescribed models: use likelihood and assume observation noise ** Implicit models: likelihood free
! The
The classical MAP assumes the speaker supervector has prior
$$
m_s = m + Dz_s
$$
where $m$ is the UBM supervector, $D$ is a block diagonal matrix (assuming mixture components are independent) with each block $F\times F$ (often set as diagonal, as GMM model has diagonal covariances) and $z_s$ is drawn from $N(0, I)$, assuming supervectors of different speakers are independent.
Provided that $d$ is non-singular, classical MAP adaptation is guaranteed to be asymptotically equivalent to ''speaker-dependent training''. When the number of mixture components is large, a large amount of enrollment data are needed. \cite{Kenny05jointfactor}.
The simplest neighborhood preserving emotional model is to add the emotion supervector to the speaker dependent $s$. Which is the same as JFA. The basic assumption of JFA is that a speaker and channel dependent supervector of means $m_s$ can be decomposed into:
$$
m_s = s + c, \quad s = m + Vy + Dz, \text{ and }c = Ux.
$$
Here $x$ and $y$ are also drawn from standard Gaussian. To implement MAP estimation on $m_s$, we have to know the posterior of hidden variables $x$, $y$ and $z$. To precisely calculate ''the posterior'' of $m_s$, all recordings have to be processed since they share the same $y_s$ and $z_s$. Sequential estimation algorithm also works if we apply ''speaker-dependent hyperparameters'' $m_s$.
''Remark'': The hyperparameters $m$, $v$, $d$ can be seen as sparse matrix thus related techniques can be introduced.
!! Similarity with sparse representation
We rewrite the sparse representation problem with JFA notations:
$$
! Estimation (Training the PCA System)
\cite{Kenny2005Factor} proved MAP point estimate of $y$ and $z$ is sufficient but $x$ is integrated. \cite{Glembek2009Comparison} used even more simplified point estimation of $x$ and linear scoring function and showed comparable results.
''Remark'': Currently the point estimate, integration scoring either work. I guess the emotion matrix learning is problematic. Per-speaker projection of merged emotion factors shows the speaker-emotion factors are very random.
! Scoring
We compute the log-likelihood ratio between the target speaker model s and the UBM.
!! Log-Likelihood
The classical frame-by-frame GMM log-likelihood evaluation is given by:
$$
\log P(\chi|s) = \sum_{t=1}^T\log\sum_{c=1}^Cw_cN(o_t;\mu_c, \Sigma_c),
$$
The conditional likelihood of the test utterance $P(\chi|s, x)$. Lemma 1 in \cite{kenny2005eigenvoice} using the ''Baum-Welch statistics'' extracted from the utterance. Marginal $P(\chi|s)$ can be calculated by integrating out the standard normal distribution. See Proposition 2 in \cite{kenny2005eigenvoice} for the closed form expression. The expression for the likelihood function is (19) in \cite{kenny2007joint}.
In practice the speaker supervector $s$ has to be estimated for the hypothesized speaker. $Cov(s, s)$ in introduced into $tr(\Sigma^{-1}S_s$ term to diminish the likelihood value inversely proportional to the amount of the speaker's enrollment data.
!! Integrating over Channel Distribution
(13) in \cite{kenny2007joint}:
$$
P(\chi|s) = \int P(\chi|s, x)N(x;0, I)dx \quad (1)
$$
Integrating out the channel is computationally infeasible, so $x$ is estimated with MAP. But it seems to be feasible for the emotional case.
By using fixed alignment of fames of Gaussians, the log-likelihood can be approximated using ''the GMM EM auxiliary function'', a lower bound to (1):
$$
\log \tilde P(\chi|s) = \sum_{c=1}^CN_c\log\frac{1}{2\pi^{F/2}|\Sigma_c|^{1/2}}-\frac 1 2\text{tr}(\Sigma^{-1}S_s)-\frac 1 2\log|L|+\frac 1 2\|L^{-1/2}U^*\Sigma^{-1}F_s\|^2.
$$
The definition of $N$, $F_s$ and $S_s$ can be found in (14-19) in \cite{kenny2007joint}. $N_c$, the number of data assigned to component $c$ is equal for UBM and the target model so the first term can be canceled out in LLR computation.
In the second term, $S_s$ is the second order moment of $\chi$ around speaker $s$,
$$
S_s = S - 2\text{diag}(Fs^*)+\text{diag}(Nss^*),
$$
where $S$ is the block diagonal second order stats and independent of speaker, thus along with third term $L = I+U^*\Sigma^{-1}NU$, is also canceled out.
$L^{-1/2}$ in the fourth term is the inverse of the Cholesky decomposition of $L$. The scoring function we care about is
$$
Q_{int}(\chi|s) = \text{tr}(\Sigma^{-1}\text{diag}(Fs^*))-\frac 1 2\text{tr}(\Sigma^{-1}\text{diag}(Nss^*))+\frac 1 2\|L^{-1/2}U^*\Sigma^{-1}F_s\|^2.
$$
!! Channel Point Estimate
If we knew the channel factor $x$, there would be no need for integrating over the whole distribution of x, but only use the point estimate in LLR. The formula is adopted from thm 1 in \cite{Kenny05jointfactor}
The log likelihood ratio formula is adopted from \cite{Kenny05jointfactor}.
$$
\log \tilde P(\chi|s) = \sum_{c=1}^CN_c\log\frac{1}{2\pi^{F/2}|\Sigma_c|^{1/2}}-\frac 1 2\text{tr}(\Sigma^{-1}S) + M^*\Sigma^{-1}F + \frac 1 2M^*N\Sigma^{-1}M,
$$
where $M$ is $m_s$ in (1). Leading to scoring function
$$
Q_x(\chi|s, x) = M^*\Sigma^{-1}F + \frac 1 2M^*N\Sigma^{-1}M
$$
hence
$$
LLR_x(\chi|s) = Q_x(\chi|s, x_s) - Q_s(\chi|UBM, x_{UBM})
$$
$$
LLR_x(\chi|s) = Q_x(\chi|s, x_s) - Q_s(\chi|UBM, x_{UBM})
$$
!! Linear Scoring
\cite{Dalmasso2009Loquendo} assumes the channel shift is identical to test recordings and UBM. The channel factor $x$ for utterance $\chi$ are estimated using UBM:
$$
LLR_{LPT}(\chi|s) = Q_x(\chi|s, x_{UBM}) - Q_s(\chi|UBM, x_{UBM})
$$
Build FSA from execution traces. FSA generation (specification miner): * Automatic mining of specifications from invocation traces and method invariants * A framework for the evaluation of specification miners based on finite state machines
Operations: * Id, Add, Subtract, Multiply, Divide, Power, Log, Sqrt, Sine, Cosine, Tangent, Factorial, nchoosek. * Radians Degrees convertion * Str Float convertion The instruction $z_i$ is a tuple consisting of * an operation ($o_i$) * an ordered sequence of its arguments ($\mathbf a_i$) * a decision about where its results will be placed($r_i$), to output or memory * the result of applying the operation to its arguments ($v_i$). Controller generates 3 kind of instructions * softmax * copy input * copy output TODO: convert to a "scratch paper" input (stack, heap, tape etc like?)
[[link|https://openreview.net/forum?id=ry_sjFqgx]] * A significantly different approach than the currently accepted NN methods. * Character level generation requires delicate rules and logic for the generation. ! The DSL * SimpleProgram ** Move: left/write and find ** Write: write where? context? more like a read *** Hash: write hash between current pos and last write *** Dist: dist between current pos and last write * SwitchProgram: select subprograms (functions?) ** guard ** branch conditions and programs to use * StateProgram: The memory block, one integer (for one feature)? ** modified at each input character ! DNC as DSL * Simple ** random access ** links work for PREV_CHAR ** dist not obvious but is this important? * Switch **
* [[Languages]] * [[Tools]] * [[Snippets]] * [[Distributed System]] * [[Software Research]]
Imperative: Fortran Object-oriented: C++
[[Snippets|Python Snippets]]
Display a number with leading zeros
```python
"%02d" % (1,)
'{num:02d}'.format(num=i)
```
! Machine learning
[[Outlier]]
I have replaced old `virtualenv2` with the new `virtualenv` package on my ArchBox so as to unify the location of pip packages installed by different virtual environments. To trigger python2 virtualenv ``` source ~/virtualenvs/python2/bin/activate ```
We want to omit policy gradient and choose best action directly. But run into problems like need to know $p(s'|s, a)$ and need to represent $V(s)$. So we approximate $E[V(s')]$ with $\max_{a'}Q_\phi(s_i', a_i')$. By this we skip simulation of actions.
* pros: works even for off-policy samples; only one network
* cons: no convergence guarantees
Q-function is the maximum discounted future reward when we perform action $a$ in state $s$, $Q(s_t, a_t) = \max R_{t+1}$
''Bellman equation'': maximum future reword for this state and action is the immediate reward plus maximum future reward for the next state. $Q(s, a) = r + \gamma\max_{a'}Q(s', a')$. @@color:#859900;The maximization step leads to en overestimatio bias.@@
In practice, many of the states are very rarely visited. (maybe a good memory management could speed this process up.) By passing the state through a deep neural net, we can get all Q-values for all actions available immediately.
The network can be optimized with simple squared error loss:
$$
L = \frac 1 2[r + \max_{a'}Q(s', a')-Q(s, a)]^2
$$
Approximation of Q-values using non-linear functions is unstable and takes a week on a single GPU:
* sequential states are strongly correlated
* target value is always changing
* Q-learning is not gradient descent
''Experience replay'' draws minibatches from previous input to break the similarity of subsequent training samples. In ''$\epsilon$-greedy exploration'', with probability $\epsilon$ we choose a random action, otherwise go with the highest Q-value.
! Deep Q-Networks
At each step, based on the current state, the agent selects an action $\epsilon$-greedily w.r.t. the action values, and adds a transition $(S_t, A_t, R_{t+1}, \gamma_{t+1}, S_{t+1})$ to a replay memory buffer, that holds the lat ''million'' transitions. The parameters of the neural network are optimized by using SGD to minimize the loss
$$
(R_{t+1}+\gamma_{t+1}\max_{a'}q_{\bar\theta}(S_{t+1}, a')-q_\theta(S_t, A_t))^2
$$
where $t$ is a time step randomly picked from the replay memory. $\theta$ is the parameters of the //online network//, $\bar\theta$ is a periodic copy which is not directly optimized. This keeps the target function from changing too quickly.
!! Extensions to DQN
[[Rainbow: Combining Improvements in Deep Reinforcement Learning|https://arxiv.org/abs/1710.02298]]
''[[Double Q-learning|https://arxiv.org/abs/1509.06461]]'': maximization performed for the bootstrap target, $\theta$ for the selection of the action and $\bar\theta$ for its evaluation.
$$
(R_{t+1}+\gamma_{t+1}q_{\bar\theta}(S_{t+1}, \arg \max_{a'}q_{\theta}(S_{t+1}, a'))-q_\theta(S_t, A_t))^2
$$
''Prioritized replay'' samples transitions with probability $p_t$ relative to the last encountered absolute //TD error//:
$$
p_t\approx|R_{t+1}+\gamma_{t+1}\max_{a'}q_{\bar\theta}(S_{t+1}, a')-q_\theta(S_t, A_t)|^\omega
$$
where $\omega$ is a hyper-parameter that determines the shape of the distribution. When $\omega=1$, this is the absolute Bellman error.
''Dueling networks'' features two streams of computation, the value and advantage streams, sharing a convolutional encoder, and merged by a special aggregator:
$$
q_\theta(s, a) = v_\eta(f_\xi(s)) + a_\psi(f_\xi(s), a) - \frac{\sum_{a'}a_\psi(f_\xi(s), a')}{N_{\text{actions}}}
$$
By factorizing the architecture of the network, we can improve the result ''a lot''.
''Multi-step learning'' can often lead to faster learning. Forward-view //multi-step// targets can be used. We define the trancated n-step return $R_t^{(n)}$ as $\sum_{k=0}^{n-1}\gamma_t^kR_{t+k+1}$. The alternative loss is
$$
(R_t^{(n)}+\gamma_t^{(n)}\max_{a'}q_{\bar\theta}(S_{t+n}, a')-q_\theta(S_t, A_t))^2
$$
''Distributed RL''
''Noisy Nets'' propose a noisy linear layer that combines a deterministic and noisy stream. Sometimes, many actions must be executed to collect the first reward. Adding noise to network parameters is good for exploration. Simple approach is like elementwise linear noise layer.
! Bibs
* Classic
** Learning from delayed rewards: introduces Q-learning
** Neural fitted Q-iteration: batch-mode Q-learning with neural networks
* Deep RL
** Deep auto-encoder neural networks in reinforcement learning: early image-based Q-learning method using autoencoders to construct embeddings
** Human-level control through deep reinforcement learning: Q-learning with convolutional networks for playing Atari.
** Deep reinforcement learning with double Q-learning: a very effective trick to improve performance of deep Q-learning.
** Continuous control with deep reinforcement learning: continuous Q-learning with actor network for approximate maximization.
** Continuous deep Q-learning with model-based acceleration: continuous Q-learning with action-quadratic value functions.
** Dueling network architectures for deep reinforcement learning: separates value and advantage estimation in Q-function.
* [[blog|http://metamind.io/research/new-neural-network-building-block-allows-faster-and-more-accurate-text-understanding/]]
! 2D variance: spatial qrnn
Given an input sequence $\mathbf X\in\mathbb R^{W\times H\times n}$, spatial qrnn performs convolutions with a bank of $m$ filters, producing a sequence $\mathbf Z\in\mathbb R^{W\times H\times m}$.
To adopt the original function to 2-dimensional images, fo:
$$
\begin{align}
c_t &= \frac 1 2 f_t\odot(c_{t-1}+c'_{t+1}) + (1-f_t)z_t\\
h_t &= o_t\odot c_t
\end{align}
$$
f:
$$
h_t = \frac 1 2 f_t\odot(h_{t-1}+h'_{t+1})+(1-f_t)z_t
$$
! Implementations
* [[tensorflow|https://github.com/Kyubyong/quasi-rnn]]
! Open Domain * [[Reading Wikipedia to Answer Open-Domain Questions|https://arxiv.org/abs/1704.00051]]: Useful datasets and benchmarks provided ! Examples * Machine Comprehension by Text-to-Text Neural Question Generation * [[A Unified Query-based Generative Model for Question Generation and Question Answering|https://arxiv.org/abs/1709.01058]] * [[Attention-based CNN Matching Net|https://arxiv.org/abs/1709.05036]]: a quite naive convolutional attention implementation for making choices ! SOTA !! Seq2seq * Encoder ** BiLSTM, read document $D$, generate augmented document sequence $\mathbf h^d$ ** BiLSTM, read query $Q$ and subset $\mathbf h^d$, generate encoding $\mathbf h^q$ (original paper used answer as query so that words came from document) ** compute initial state for decoder * Decoder ** Location, pointer network over document tokens, softmax $\mathbf\alpha$, compute context vector $\mathbf v$ ** Shortlist, reads $\mathbf v$, softmax over answer vocabulary $\mathbf o$ ** Source switching network (2-layer MLP) enables model to interpolate between these distributions ! Bibs * [[DrQA]] * [[R-Net]]: SOTA on SQuAD
! Implementation Attempts to implement based on ParlAI and PyTorch. A torch version of [[match-lstm and pointer network|https://github.com/shuohangwang/SeqMatchSeq]] should be helpful. !! Encoder * character-level embedding !! Attention * gated attention-based RNN: corner stone but kind of complicated. Wang & Jiang's match-lstm. !! Output Layer * [working] Wang & Jiang's adoption of Pointer Network !! Visualization * Attention weights $c_t$. !! Other Ideas * DeepMind releases a [[Relation network|https://arxiv.org/abs/1706.01427]], which is similar to attention, in a sum fashion, should be considered.
''Goal'': predict label $y_*$ for test data $x_*$
Random forest:
* Ensemble of randomized decision trees
* sota real world prediction: robust and paralizable
$$
p(y_*|x_*) = \frac{1}{M}\sum_m p(y_*|x_*,\mathcal T_m, X, Y)
$$
* Breiman;s RF: Bagging + subsamples
*Extremely RF: random $k$ sample and choose the best split
** no bagging
** labels do not guide ERT-1
Pros: fast, no overfitting
Cons: not possible to train incrementally
! Definitions
A ''Rao–Blackwell estimator'' $\delta_1(X)$ of an unobservable quantity $\theta$ is the conditional expected value $E(\delta(X)\mid T(X))$ of some estimator $\delta(X)$ given a sufficient statistic $T(X)$. Call $\delta(X)$ the "original estimator" and $\delta_1(X)$ the "improved estimator". It is important that the improved estimator be observable, i.e. that it not depend on $\theta$. Generally, the conditional expected value of one function of these data given another function of these data does depend on $\theta$, but the very definition of sufficiency given above entails that this one does not.
! The theorem
!! Mean-squared-error version
One case of Rao–Blackwell theorem states:
<<<
The mean squared error of the Rao–Blackwell estimator does not exceed that of the original estimator.
<<<
In other words
$$
\operatorname{E}((\delta_1(X)-\theta)^2)\leq
\operatorname{E}((\delta(X)-\theta)^2).\,\!
$$
The essential tools of the proof besides the definition above are the law of total expectation and the fact that for any random variable $Y$, $E(Y^2)$ cannot be less than $[E(Y)]^2$. That inequality is a case of Jensen's inequality, although it may also be shown to follow instantly from the frequently mentioned fact that
$$
0 \leq \operatorname{Var}(Y) = \operatorname{E}((Y-\operatorname{E}(Y))^2) =
\operatorname{E}(Y^2)-(\operatorname{E}(Y))^2.\,\!
$$
!! Convex loss generalization
The more general version of the Rao–Blackwell theorem speaks of the "expected loss" or risk function:
$$
\operatorname{E}(L(\delta_1(X)))\leq \operatorname{E}(L(\delta(X)))\,\!
$$
where the "loss function" $L$ may be any convex function. For the proof of the more general version, Jensen's inequality cannot be dispensed with.
Radial basis function (RBF) networks are feed-forward networks that have universal approximation properties. Their main advantage is the simplicity of computation of the network paramters. They typically have three layers: an input layer, a hidden layer with a non-linear RBF activation function and a linear output layer. The input can be modeled as a vector of real numbers $\mathbf{x} \in \mathbb{R}^n$. The output of the network is then a scalar function of the input vector, $\varphi : \mathbb{R}^n \to \mathbb{R}$, and is given by
$$
\varphi(\mathbf{x}) = \sum_{i=1}^N a_i \rho(||\mathbf{x}-\mathbf{c}_i||)
$$
where $N$ is the number of neurons in the hidden layer, $\mathbf c_i$ is the center vector for neuron $i$, and $a_i$ is the weight of neuron $i$ in the linear output neuron. $\rho$ is a function that depend only on the distance form a center vector radially symmetric about the input, hence the name radial basis function. The norm is typically taken to be Euclidean, but Mahalanobis distance can do better (?) and the radial basis function is commonly taken to be Gaussian
$$
\rho \big ( \left \Vert \mathbf{x} - \mathbf{c}_i \right \Vert \big ) = \exp \left[ -\beta \left \Vert \mathbf{x} - \mathbf{c}_i \right \Vert ^2 \right].
$$
The Gaussian basis functions are local to the center vector in the sense that
$$
\lim_{||x|| \to \infty}\rho(\left \Vert \mathbf{x} - \mathbf{c}_i \right \Vert) = 0
$$
i.e. changing parameters of one neuron has only a small effect for input values that are far away from the center of that neuron.
[[link|http://dhruvp.github.io/2015/03/07/re-frame/]] and [[previous reagent tutorial|Reagent]] The project is based on [[reagent|http://dhruvp.github.io/2015/02/23/mailchimip-clojure/]] and is in cljs I came to a problem in step 2 that phones-component requires a for to generate the view but the for takes 5 arguments. Problem solved by changing the for sentence. But now the page just does not show anything.
[[link|https://arxiv.org/abs/1412.1842]] ! End-to-end text spotting pipeline [img[end2end_text_spotting_pipeline.png]] ! Text Detection This operates in a low-precision, high-recall mode – rather than using a single word location proposal, we carry a sufficiently high number of candidates through several stages of our pipeline.
[[Papers]] [[Books]] [[Talks]] [[Courses]]
! HelloThere
{{HelloThere}}
Atoms are one of the few mutable data structure in Clojure. Reagent extends a Clojure Atom by ensuring that whenever an Atom is mutated, any component that uses it is rerendered ! Example [[link|http://dhruvp.github.io/2015/02/23/mailchimip-clojure/]] got problem with map??
Papers reviewed in this post: # A Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time Series Data ! The problem We want the outlier to be detected immediately after it appears. And the data is spouted in a non-stationary way. Online, a.k.a, a priori, change-point detection with !
* [[Online Natural Gradient as a Kalman Filter|https://arxiv.org/abs/1703.00209]]
[[paper|https://arxiv.org/abs/1612.03969]]
! Introduction RNN is a very deep feedforward neural newwork that has a layer for each timestep. Its weights are shared across time. ! Model structure * [[Sequential Models]] * Regularisation ** don't put drop on recurrent connections, on non-recurrent ones. [[Recurrent neural network regularization. Zaremba et al.|https://arxiv.org/abs/1409.2329]] ** [[A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. Gal and Ghahramani|https://arxiv.org/abs/1512.05287]] advocate tying the recurrent dropout mask and sampling at evaluation time. ** [[Variational Dropout]] ! Topics * ICLR 2017 Talk: [[New Directions for RNNs]] * [[Meta-Learning]] * [[RNN Performance Optimization]] ! Training * [[Optimizing RNN]] * RTRL ** Approximate: [[Training recurrent networks online without backtracking|https://arxiv.org/abs/1507.07680]] ** Local: original LSTM paper * [[Synthetic Gradients]] * [[Waybackprop]] * [[Training Recurrent Networks Online Without Backtracking]] ! Tools * [[RNNVis|https://github.com/myaooo/RNNVis]] tf 0.12 visualization
! Motivation Using an undirected model for the interaction between the hidden and visible variables helps to capture many of the regularities that cannot be modeled efficiently by HMMs. This ensures that the contribution of the likelihood term to the posterior over the hidden variables is approximately ''factorial'', which greatly facilitates inference. Properties of this model family: # Componential hidden state (exponentially large state space) # Non-linear dynamics and multimodal predictions # Simple on-line filtering procedure # Efficient learning algorithm (maximum likelihood is intractable) # Simple to learn multiple layers of hidden variables Models that use latent variables to propagate information through time can be divided into two classes depending on whether there is an efficient procedure for inferring the exact posterior distribution: # tractable models: linear dynamic systems, HMM # intractable models
[[link|https://www.vicarious.com/2017/10/26/common-sense-cortex-and-captcha/]] RCN integrates and builds upon various ideas from compositional models: * heirarchical composition * gradual building of invariances * lateral connections for selectivity * contour surfaces factorization ** surfaces modeled using pairwise CRF, smoothness of variations of surface properties. ** contours modeled using a compositional hierarchy of features * joint-explanation based parsing Inferenced with Belief propagation.
Stochasticity of the transformation parameters $\theta$
REINFORCE approximates the expectation with independent samples from the policy distribution, yielding the policy gradient
$$
\nabla\mathcal L_{RL}\approx\sum_{t=1}\nabla\log\pi(\hat y_t|\hat y_{<t}, D, A)\frac{R(\hat Y)-\mu_R}{\sigma_R}
$$
Reparametrization can help backpropagate through the samples. (rewrite samples from another simple distribution) to lower the variance.
! Tutorials
* [[WildML: Learning Reinforcement Learning|http://www.wildml.com/2016/10/learning-reinforcement-learning/]]
* [[Simple Reinforcement Learning with Tensorflow|https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0]]
* [[Deep RL Bootcamp 2017 Berkeley|http://nuit-blanche.blogspot.hk/2017/10/slides-and-videos-deep-rl-bootcamp-26.html]]
! Basic
!! Theorems
<<<
''Theorem'' [Value iteration converges]<br>
At convergence, we have found the optimal value function $V^*$ for the discounted infinite horizon problem, which satisfies the Bellman equations
$$
\forall S\in S:\qquad V^*(s) = \max_a\sum_{s'}P(s'|s, a)[R(s, a, s')+\gamma V^*(s')]
$$
<<<
''Temporal Difference Learning:'' Policy evaluation for the current policy $\pi_k$
$$
V^{\pi_k}_{i+1}(s) \leftarrow \sum_{s'}P(s'|s, \pi_k(s))[R(s, \pi_k(s), s')+\gamma V^{\pi_k}_{i}(s')]
$$
Policy improvements: find the best action according to one-step look-ahead
$$
\pi_{k+1}(s) \leftarrow \arg\max_a\sum_{s'}P(s'|s, a)[R(s, a, s')+\gamma V^{\pi_k}(s')]
$$
Tabular methods cannot scale.
@@color:#859900;How to update it as a network?@@
<<<
''Theorem'' [Policy iteration converges]<br>
Policy iteration is guaranteed to converge and at convergence, the current policy and its value function ar the optimal policy and the optimal value function.
<<<
We sample states $\epsilon$-greedily.
!! RL agents
* value based, has a value function where policy can be inferred.
* policy based, work in decision space, more direct. Policy Graident.
* [[Actor-Critic Method]]: stores policy and value
* model free/based
!! Anatomy of a RL algorithm
Iterate these 3 steps：
# Generate samples (i.e. run the policy)
# Fit a model to estimate return
#* MC policy gradient: compute $\hat Q = \sum_{t'=t}^T\gamma^{t'-t}r_{t'}$
#* [[Actor-Critic Method]], [[Q-Learning]]: fit $Q_\phi(s, a)$
#* Model-based: estimate $p(s'|s, a)$
# Improve the policy
#* [[Policy Gradient]]: $\theta\leftarrow\theta+\alpha\nabla_\theta J(\theta)$
#* Q-learning: $\pi(s) = \arg\max Q_\phi(s, a)$
#* Model-based: optimize $\pi_\theta(a|s)$
!! Tricks
* Freezing target network
* Error clipping, reward clipping
* Experience replay
!! Training steps
On training half-cheetah, each one is 10x easier than previous. (each episodes is 1000 steps)
* gradient-free methods, e.g. evolution strategies, CMA, etc.
* fully online methods, e.g. A3C, 100,000 episodes ~15 days
* policy gradient methods, e.g. TRPO, 10,000 episodes
* relay buffer value estimation, e.g. Q-learning, DDPG, NAF, etc., 1,000 episodes
* model-based deep RL, e.g. guided policy search
* model-based shallow RL, e.g. PILCO
! Bibs
* [[Faster Deep Reinforcement Learning]]
* [[Playing Doom with SLAM-Augmented Deep Reinforcement Learning|https://arxiv.org/abs/1612.00380]]
* [[Learning to Communicate|https://blog.openai.com/learning-to-communicate/]]: differentiable language for RL
* [[Curiosity-driven Exploration by Self-supervised Prediction|https://pathak22.github.io/noreward-rl/]]: training with sparse reward
! Topics
* [[Imitation Learning]]
* [[Meta-Learning]]
* [[Noisy Networks for Exploration|https://arxiv.org/abs/1706.10295]]
* [[Soft Optimality]]
! Implementations
Environments
* [[CommAI-env|https://github.com/facebookresearch/CommAI-env]]
* [[Universe|https://openai.com/blog/universe/]] with [[Gym|https://gym.openai.com/]]
* [[DeepMind Lab|https://github.com/deepmind/lab]]
* [[Facebook ELF|https://github.com/facebookresearch/ELF]]
Renewal processes are generalizations of the Poisson process on the real line whose intervals are drawn i.i.d. from some distribution.
! Formal definition
Let $S_1 , S_2 , S_3 , S_4 , S_5, \ldots$ be a sequence of positive independent identically distributed random variables such that
$$
0 < \mathbb{E}[S_i] < \infty.
$$
We refer to the random variable $S_i$ as the "$i$th" holding time.
Define for each $n > 0$:
$$
J_n = \sum_{i=1}^n S_i,
$$
each $J_n$ referred to as the "$n$th" //jump time// and the intervals $[J_n,J_{n+1}]$ being called renewal intervals.
Then the random variable $(X_t)_{t\geq0}$ given by
$$
X_t = \sum^{\infty}_{n=1} \mathbb{I}_{\{J_n \leq t\}}=\sup \left\{\, n: J_n \leq t\, \right\}
$$
(where $\mathbb{I}$ is the indicator function) represents the number of jumps that have occurred by time $t$, and is called a ''renewal process''.
! Special Cases
* [[Homogeneous Poisson Process]]
* Modulated Renewal Process
! Theory
!! Curse of Dimensionality
In a finite-dimensional, bounded space, all metrics are equivalent:
<<<
For each $x\in\Omega$, exists constants $c, C$ such that $\forall x'\in\Omega$, $cd(x, x')\le\tilde d(x, x')\le Cd(x, x')$.
<<<
But as the dimension increases, metrics start to diverge. In particular, the Euclidean distance in high-dimensional spaces is typically a poor measure of similarity for practical purposes. Local decisions around training do not extend to the whole space.
To avoid the curse of dimensionality, we need a guiding principle that plays well with our data (images, sounds, etc.) We need features that linearize intra-class variability and perserve inter-class variability.
!! Generalization Error
It is easy to construct discriminative features:
* Using a Gaussian RBF, it suffices to let $\sigma\rightarrow0$.
* The estimator converges to the nearest neighbor classifier:
$$
\hat f(x) = f(x_{i(x)}), i(x) = \arg\underset{i}{\min}\|x-x_i\|
$$
While it may be easy to correctly classify our training examples, we do not necessarily improve our generalization error:
$$
\mathbb E_{(x, y)}(\mathcal l(\hat f(x), y))
$$
!! Linearization
We want to obtain a representation $\Phi(x)$ such that
$$
\hat f(x) = \text{sign}(a^\top\Phi(x)+b)
$$
is a good approximation of $f(x)$. Thus $f(x)$ is approximately linearized by $\Phi(x)$:
$$
f(x) \approx \text{sign}(a^\top\Phi(x)+b)
$$
In particular, we should have
$$
a^\top(\Phi(x)-\Phi(x') = 0 \Rightarrow f(x)=f(x').
$$
!! Invariance and Symmetry
A global symmetry is an operator $\varphi\in Aut(\Omega)$ that leaves $f$ invariant:
$$
\forall x\in\Omega, f(\varphi(x)) = f(x)
$$
They can be absorbed by $\Phi$ to varying degrees:
* ''Invariants'': $\Phi(\varphi(x)) = \Phi(x)$ for each $x$.
* ''Covariants'': $\Phi(\varphi(x)) = A_\varphi\Phi(x)$ for each $x$, where $A_\varphi$ is "simpler" than $\varphi$.
[[Image Representation Learning]]
! Algorithms
[[t-Distributed Stochastic Neighbor Embedding]]
! Quick Facts
We define a feature map $\phi:\mathcal X\rightarrow\mathcal H$, for $\mathcal H$ an RKHS, $\varphi$ corresponds to a kernel $k: \mathcal X\times\mathcal X\rightarrow \mathbb R$ by $k(x, y) = \langle\varphi(x), \varphi(y)\rangle_{\mathcal H}$. For any p.s.d. $k$, a matching $\mathcal H$ and $\phi$ exist:
* e.g. we can use the Gaussian kernal: $k(x, y) = \exp(-\frac{1}{2\sigma^2}\|x-y\|^2)$
The **reproducing property** assures: $\forall f\in\mathcal H, f(x) = \langle f, \varphi(x)\rangle_{\mathcal H}$.
! Definition
; RKHS
: We say that $\mathcal H$ is a reproducing kernel Hilbert space of functions $f:\mathcal X\rightarrow\mathcal Y$, when for any $y\in\mathcal Y$ and $x\in\mathcal X$ the linear functional which maps $f\in\mathcal H$ to $(y,f(x))$ is continuous on $\mathcal H$.
We conclude from the [[Reisz Lemma]] that, for every $x\in\mathcal X$ and $y\in\mathcal Y$, there is a linear operator $K_x:\mathcal Y\rightarrow\mathcal H$ such that
$$
(y, f(x))=\langle K_xy,f\rangle.
$$
For every $x, t\in\mathcal X$ we also introduce the linear operator $K(x, t):\mathcal Y\rightarrow\mathcal Y$ defined, for every $y\in\mathcal Y$, by
$$
K(x,t)y:=(K_ty)(x).
$$
Residual connection were introduced by He et al. in [[Deep Residual Learning for Image Recognition|https://arxiv.org/abs/1512.03385]] in which they give convincing theoretical and practical evidence for the advantages of utilizing additive merging of signals both for image recognition, and especially for object detection. * [[Wide Residual Network|http://arxiv.org/abs/1605.07146]] * [[ResNeXt: Aggregated Residual Transformations for Deep Neural Networks|https://arxiv.org/abs/1611.05431]] ! Bibs * [[Highway Connections]] * [[Identity Matters in Deep Learning]] ! Repos [[resnet.torch|https://github.com/facebook/fb.resnet.torch]]
RBMs can learn excellent generative models and RBM plays an important role in the training of Deep Belief Networks, as a good initialization for the FNN.
! Theory
!! Formalization
The RBM is a parameterized family of probability distributions over binary vectors. It defines a joint dsitribution over $v\in\{0, 1\}^{N_v}$ and $h\in\{0, 1\}^{N_h}$ via the following equation
$$
P(v, h) = \frac{\exp(h^\top Wv+v^\top b_v + h^\top b_h)}{Z(\theta)}
$$
The partition function $Z(\theta)$ is an sum of exponentially many terms and cannot be efficiently approximated to a constant multiplicative factor. The probability of the visible units is computed by marginalizing over the hidden units, also the probability of observing the data $X$, given the weights $W$:
$$p(X|W) = p(v) = \frac{1}{Z(v, h)}\sum_he^{-E(v, h)}$$
We can break the likelihood into 2 parts:
$$
\mathcal L = \ln p(v) = \ln\sum_he^{-E(v, h)}-\ln Z(v, h)
$$
//Clamped// ''Free Energy'' $F^c(v)$ and the standard ''Free Energy'' $F(v, h)$. First one is easy to evaluate in the RBM formalism, whereas $F(v, h)$ is computationally intractable. Knowing the $Z(v, h)$ is //like// knowing the equilibrium distribution function, and methods like RBMs appear to approximate it in some form or another.
!! Training
RBMs are usually trained via Contastive Divergence. The Energy funtion, being quadratic, lets us readily factor $Z$ using a mean field approximation, leading to simple expressions for the conditional probabilities:
$$
\begin{align}
p(h_i=1|v) &= \sigma(b_i+W_ih)\\
p(v_i=1|h) &= \sigma(a_i+W_iv)
\end{align}
$$
and weight update rule
$$
dw = \langle v, h\rangle_+-\langle v, h\rangle_\infty
$$
$\langle v, h\rangle_\infty$ is evaluated in the limit of infinite sampling, at the so-called equilibrium distribution. But we don't take the infinite limit.
CD approximates the (mean field) Free Energy by running only 1 (or more) steps of Gibbs Sampling.
! Applications
RBMs are basicly a solved problem. 10 years ago, we use them to pretrain deep supervised nets. They still outperform VAEs on simple datasets. RBM research continues in areas like semi-supervised deep learning with deep hybrid architectures, temperature dependence, infinitely deep RBMs, etc.
! Partition Functions related with Quantum Chemistry
refs
* [[Improving RBMs with physical chemistry|https://charlesmartin14.wordpress.com/2016/10/21/improving-rbms-with-physical-chemistry/]]
<<<
Knowing the Parittion function $Z(v, h)$ is not the same as knowing the distribution $p(v)$.
<<< [[Why Does Deep and Cheap Learning Work so Well?]]
This is a small but important technical oversight. Scaling Energy does not change $Z(v, h)$. A connection between RBMs and Kadanoff VRG approach is explained in [[Charlse's post|https://charlesmartin14.wordpress.com/2015/04/01/why-deep-learning-works-ii-the-renormalization-group/]]. A more detailed connection between deep learning and Statistical Mechanics is shown [[here|An exact mapping between the Variational Renormalization Group and Deep Learning]].
<<<
RG procedure must preserve more information about the distribution than the free energy.
<<< [[Comment on “Why does deep and cheap learning work so well?”|http://arxiv.org/pdf/1609.03541v1.pdf]]
Also in the training of RBMs, the idea of preserving Free Energy, via either a ''trace condition'' or say, by layer normalization, is the important point. In variational RG, on the renomalization operator $T(v, h)$:
$$
\text{Tr}_he^{T(v, h)}=1
$$
similar to ''Size-Consistency and/or Size-Extensivity'' in Quantum Chemistry.
! Bibs
* [[Recurrent Temporal RBM]]
! Bibs * [[Understanding deep learning requires rethinking generalization|https://arxiv.org/abs/1611.03530]] * [[Convergence properties of the randomized extended Gauss-Seidel and Kaczmarz methods|https://arxiv.org/abs/1503.08235]] * [[Train faster, generalize better: Stability of stochastic gradient descent|https://arxiv.org/abs/1509.01240]] ! Summary By conventional wisdom, if the model happens to fit the training set exactly, then your model is probably not simple enough, meaning it will not fit the test set very well. According to Ben, this conventional wisdom is wrong. He demonstrates this by allowing the number of parameters to far exceed the size of the training set, and in doing so, he fit the training set exactly, and yet he still managed to fit the test set well. He suggested that generalization was successful here because stochastic gradient descent implicitly regularizes. For reference, in the linear case, stochastic gradient descent (aka the randomized Kaczmarz method) finds the solution of minimal 2-norm, and it converges faster when the optimal solution has smaller 2-norm. Along these lines, Ben has some work to demonstrate that even in the nonlinear case, fast convergence implies generalization.
! Computation requirement For LSTM * [[GEMM]] (input 2HxB, output 4HxB): for 3 gates (i, f, o) and cell ** #FLOPS = 16HHB ** Bytes through memory (fp32) = 4(8HH + 2HB + 4HB) ** flops:byte = 2HB:3B+4H, typically H>>B ** from roofline model, the efficient batchsize for H=2048 on P100 is 32 * Pointwise operations: tanh, sigmoid, add, pointwise mult ! Network Level Optimizations # Reducing memory traffic #* for small batch size, we reduce the amount of times we load matrix #* by unrolling over time, freeing input, hidden dependencies #* [[Persistent RNNs]]: keep recurrent matrix on-chip. impressive speedup, constraint by on-chip memory # Reducing overheads #* A naive implementation of pointwise operations is by separate [[GPU Kernel]]s: setup overhead and bandwidth are problem #* Fuse into one kernel # Increasing parallelism ! refs * [[Optimizing Performance of Recurrent Neural Networks on GPUs|https://arxiv.org/abs/1604.01946]] * [[Optimizing Recurrent Neural Networks in cuDNN 5|https://devblogs.nvidia.com/parallelforall/optimizing-recurrent-neural-networks-cudnn-5/]]
! Feature map sizes To adopt original strides: * 1/8 from stage2_unit4: _plus6 * 1/16 from res4: _plus12 * 1/32 from res5: _plus15 * 1/64 from conv6:
@inproceedings{rosen2004author,
title={The author-topic model for authors and documents},
author={Rosen-Zvi, Michal and Griffiths, Thomas and Steyvers, Mark and Smyth, Padhraic},
booktitle={Proceedings of the 20th conference on Uncertainty in artificial intelligence},
pages={487--494},
year={2004},
organization={AUAI Press}
}
[[Mondrian forests]]
[[paper|https://arxiv.org/abs/1607.00360]]
The Bregman distortion is the residual of Taylor expansion of a function:
$$
R(x\|y) = \varphi(x) - \varphi(y)-(x-y)^\top\nabla\varphi(y)
$$
when $\varphi$ is not convex, this value can be positive, negative or zero. When $\varphi$ is convex, the Bregman divergence $D_\varphi(x\|y)$ has some good properties:
* triangle equality
* dual symmetry w.r.t. convex conjugate
* population minimizer = average
We can extend this with a differentiable function $g$. We define a new generator
$$
\breve{\varphi}\doteq g(x)\varphi\left(\frac{x}{g(x)}\right)
$$
in terms of the perspective transform of $\varphi$. It turns out the perspective transform of the divergence
$$
g(x)D_\varphi\left(\frac{x}{g(x)}\parallel\frac{y}{g(y)}\right) = D_{\breve\varphi}(x\|y)
$$
equals the divergence of the perspective transform iff $g$ is affine or $\varphi(x)$ is (restricted) positive homogenuos of degree 1. $\breve\varphi$ does not have to be convex.
! Variables * `filt_opt`: filter bank options ** `Q` ** `J`: the maximal wavelet scale ! Functions !! wavelet_factory_1d.m <<< Each filter bank is then used to create a layer operator, using the function WAVELET_LAYER_1D. <<< translation-invariant representations in one dimension
! Details
[[Understanding Deep ConvNets (Scattering Repr)]]
! Paper Summary
* [[Group Invariant Scattering]]
* [[Scattering Representation for Recognition]]
* [[Scattering Representation of Modulated Sounds]]
* [[Linear Time Complexity Deep Fourier Scattering Network and Extension to Nonlinear Invariants|http://openreview.net/pdf?id=SJiFvr9el]]
** $|c|^2$ as higher order nonlinearity. FFT computation is derived.
! Notes of the Talk
There was a very interesting [[talk on Techion|https://www.youtube.com/watch?v=wHhYvtnY2zI]]. I recently found my research is getting more and more related to it so I decide to sort it out.
Considering classification of high dimensional signals $x$. Dimension can be as large as $10^6$ for images. Interpolation methods that works well in low dimension meets problem in high dimensional counterparts. Because you cannot cover the whole very large space with samples anymore and the samples from the same class could be very far way from each other.
That leads to the need of dimension reduction. If the signal can be represented with low dimensional subsets $\Omega$, then there would be no curse of dimensionality. Find a good $\Omega$ turns to be problems like manifold learning or sparsity dictionary representations. However complex signals like image and audio have very correlated information that more or less influence the result, and thus such a subspace have to be deduced with the knowledge of $f$, the classification goal. That makes the problem supervised.
Let formulate the volume reduction problem. The signal domain $\Omega$ is reduced with a contractive operator $\Phi$ (non-linear) so that $\Phi(\Omega)$ could be a much smaller volume. A key element of this process is that $f$ should maintain regularity, i.e.,
$$
|f(x) - f(x')| \leq C\|\Phi(x) - \Phi(x')\|
$$
which is called Lipschitz regularity. This insures the reduced space preserves discriminability. The way of doing that is to build $\Phi(x)$ by contracting along directions where $f(x)$ is invariant: reduce intra-class variability.
On the other hand, we also want to increase the dimension so that original data can be easier to classify (e.g. by linear functions).
Deep neural networks have shown ability to do both things well. Typical deep neural nets combine linear and non-linear transformations and iterate between them and finally separate the output with linear classification. The impressive part of the work is the scale, a neural network can have billions of parameters.
The result on ImageNet containing $10^6$ images and $10^3$ classes is very impressive. The error rate is below $17\%$, very close to human ability. Speech recognition has not been moved for twenty years and now new speech recognition systems are implemented with neural nets. Now many companies are hiring people developing this kind of systems.
The question is why does it work. For the first two layers of image and audio classifications are learned with gradient descent, back propagation algorithms which looks like wavelets.
Let take an example in digit recognition, because the physical invariant is known. After a translation of rotations and small deformations, the sample remains in the same class. FYI, speech recognition requires subspace with time-frequecy translation and deformation invariance.
Ways to impose translate invariance is normalizing by zero mass or killing the face of Fourier transform. Diffeomorphisms is a little bit of different. We require Lipschitz stability,
$$
\forall\tau, \|\Phi(x_\tau) - \Phi(x)\|\leq C \underset{t}{\sup}|\nabla\tau(t)|\|x\|
$$
where $x_\tau(t) = x(t-\tau(t))$ is a small deformation of $x$ and the supremum is the diffeomorphism metric. This means the change of $\Phi(x)$ should be of the order of the deformation. If we move the distribution or perform Fourier transform, it will not be stable.
To deal with deformation, we have to use wavelets. Wavelets are functions that are localized, if we slightly deform a wavelet, we still have something that looks like a wavelet. To understand complex wavelet, think about a Gaussian modulated by sin and cos. We have a real part and an imaginary part,
$$
\psi(t) = \psi^a(t)+i\psi^b(t).
$$
And dilate it with
$$
\psi_\lambda(t)=2^{-j}\psi(2^{-j}t)
$$
with $\lambda = 2^{-j}$. The wavelet transformation averages the low frequencies and carries the high frequencies by their coefficients. The energy of wavelet transform will restore the original because it is unitary:
$$
\|Wx\|^2 = \|x\|^2.
$$
For scale and separation in 2D, the rotated and dilated wavelet is
$$
\psi_\lambda(t) = 2^{-j}\psi(2^{-j}r_\theta t)
$$
The wavelet can separate different different information within different scale.
To compute the invariance, we take a signal $x$ and make it as uniform as possible. The only linear function that is translation invariant is constant. To make a signal locally translation invariant, we locally average a signal by $x\star\phi(x)$. We take the modulus of the wavelet $|Wx|$
$$
|x\star\psi_{\lambda_t}(t)| = \sqrt{|x\star\psi^a_{\lambda_t}(t)|^2 + |x\star\psi^b_{\lambda_t}(t)|^2}
$$
which is unitary and contractive:
$$
\||W|x - |Wx'|\|\leq \|x-x'\|.
$$
The norm is preserved. The idea is to get the average of low frequencies and get the envelop of high frequencies, then we'll get a new set of invariant coefficients.
Put is all together, to get the first layer,
# we first compute the average of the image,
# then the high frequencies by wavelet transform,
# and take the modulus of the coefficients.
Now we compute the wavelet coefficients of these modulus, and there will be many images in the second layer of the network. The iteration goes on and on. The outcome is a convolution network. While compressing $x$ with the modulus, we keep increasing the dimensionality.
For other specific complex cases, we have to learn the invariant first. Basicly any contractive operator $|W_k|$ that preserves the norm can be used. The problem is how to learn it with small support.
Haar filtering is an orthogonal transformation computed by pairing the even and odd coefficients, adding and subtracting them. If we cascade it, we get the Haar wavelet. So we can learn the contraction by finding the right pairing that don't bring closer the points from different classes. Iin the unsupervised case, make sure points don't collapse (data aren't compressed too much), which is equivalent to minimize the mixed l-2/l-1 sparsity norm.
The structure resembles a [[convolutional network|Convolutional Neural Network]], instead of learning the [[filter banks|Filter Bank]], [[wavelets|Wavelet Transform]] are used to capture transform invariant features.
/media/mi/data/pornographic/JPEGImages/for_testing/special/c3ce04eb0f2bae8fa553679f4f7214f8.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/01715686bca1e8740fe29277ad16a24f.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/474a400031fc456fac9439baeba8f98a.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/download.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/4a2cd9ea449dcf3715ef329f78782884.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/9f5bdd5d0e52fc0e315023295fd2f51f.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/e2972c60395b8e9281634cb1cb1d5b33.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/d40a3bdd8955a0d0343ece7c44f3a803.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/16c202908f2757ea9b6cee8526083a37.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/4b245ba2f9271059fb2cf8d95c473fff.jpg /media/mi/data/pornographic/JPEGImages/for_testing/special/07856506a23a6f133aa0155d3575d4df.jpg
! Tasks and data * Large code corpus ** Karpathy et al. 2015 Linux Kernel Dataset ** [[Learning from "Big Code"|http://learnbigcode.github.io/]] * human annotators, clean but homogeneous ** Project Euler dataset (pseudo-code as description) ** Parallel Django dataset (pseudo-code as description): This dataset includes all source code of Python web framework Django with line-by-line English annotation. *** Diverse, spanning a wide variety of real-world use case like string manipulation, IO operations and exception handling. ** [[card2code|https://github.com/deepmind/card2code]]: DeepMind generates code for MTG and HS cards ** [[WikiSQL|https://github.com/salesforce/WikiSQL]] * Assembled from user-generated descriptions ** StackOverflow: ACL 2016 Summarizing Source Code using a Neural Attention Model ** IFTTT: Language to code: Learning semantic parsers for if-this-then-that recipes ** Docstring: [[code-docstring-corpus|https://github.com/EdinburghNLP/code-docstring-corpus]] * Debugging ** Defects4J: a curated corpus of bugs (in total 395) ** 41 AspectJ bugs are from the iBugs dataset which were collected by Dallmeier and Zimmermann. (code before and after fix) plus some others collected by David Lo's lab. several hundreds in total ** Eclipse, etc 3k+ instances. Related file but not the exact line. * Code review ** match best reviewer given descriptions * Social media ** find related tweets (keywords or social network mining stuff) ** text analysis of blogs, StackOverflow posts, feature descriptions Make use of community: Open-source software systems such as Linux,MySQL, Django, Ant, and OpenEJB have become ubiquitous. These systems publicly expose not just source code, but also meta-data concerning authorship, bug-fixes, and review processes.
! Introduction
Reading comprehension and question answering in real world is enormously difficult to handle for computer. Teaching intelligent natural languages is one of the cornerstones to solve AI. If we could build models to comprehend natural languages, it wouldn't be thus far to build intelligent systems and understand human mind. And it has huge value for consumers.
Recurrent Neural Networks (RNN) trained by Back Propagation are up to now the closest imitation to what brain does. Wesbros et al.~\cite{werbos2016regular} carried out an empirical study to find their relationships. These are the models on our smartphones recognizing and answering our questions when we talk to Siri and Google Assistant. This subject is so challenging and promising that it has attracted some of the best research teams in AI fields such as DeepMind, Facebook Research and OpenAI. Mikolov et al.~\cite{mikolov2015roadmap} pose different intelligent tasks as dialogue, or Question Answering (QA), where the learner should develop memory, perform reasoning accordingly and understand indirect supervision.
Software society offers valuable datasets for NLP. Online code corpora like GitHub, question answering forums like StackOverflow and documentations of various softwares and programming tools are highly structured and rich in content. In return, training an agent who can understand Software Engineering (SE) materials can help develop SE theory and tools. We have seen applications of NLP in code refactoring, language models for code~\cite{dam2016deep} and information retrieval of answers, projects and documentation. But compared with other deep learning applications, this field is still growing and requires more research, tasks and well-established datasets.
! Research Questions and Aims
The ultimate goal of this project is to train an agent to be able to interact with humans, solving specific SE problems. However, this is too big a problem to solve, I decide to design incremental tasks and solve them in an iterative manner. Basic tasks are for example comment sentiment analysis and locating callings in code, basic fact Q\&A in documentation. More advanced tasks are like code completion, similar snippets retrieval and automatic problem solving or even let the program code itself. I will identify what tasks can be solved with current technologies and what are the preliminary tasks we have to solve before the ultimate problem. Although these tasks seem to be distantly related, answering the following questions is important to attack all of them.
# ''How to achieve indirect supervision through dialogue?'' Dialogue is human communication tool. This is how issues and knowledge are discussed on GitHub and StackOverflow and where we would like the agent to learn. But we have to design simpler tasks and rewarding mechanisms for the learner to understand normal feedback before the agent can learn from arbitrary forms of communication happening on these online communities.
# ''How to represent context and let the agent memorize it?'' Main stream NLP algorithms always use a RNN as an encoder of the context~\cite{cho2014learning} and use attention mechanism to associate certain parts of context to related queries~\cite{bahdanau2014neural}. In these 2 years, there are many advanced memory mechanisms and models being proposed. I will review them in the next part and show how to incorporate these structures to solve SE problems.
# ''How can we enhance current models?'' Sequence-to-sequence (seq2seq) models based on RNN~\cite{sutskever2014sequence} are widely used in Neural Machine Translation (NMT) and Automatic Speech Recognition (ASR). They have a chain-like structure. However, in computational linguistics and compiler theory, we use a different tree-like structure, the grammar tree, which shares similarities with another kind of deep model, the Recursive Neural Network. Combining Recurrent and Recursive Neural Networks we arrive at a structure-to-structure modeling. For example we can use Neural Tensor Network~\cite{socher2013reasoning} to do a syntactic to semantic tree reasoning.
! Related Work
!! Goal-Directed [[Dialog]]
* [[Learning End-to-End Goal-Oriented Dialog|https://arxiv.org/abs/1605.07683]] test on (6) Dialog bAbI task.
!! Program Induction
Popular program induction algorithm is [[Neural Abstract Machines]]
!! Memory Networks
''Seq2seq''~\cite{sutskever2014sequence,cho2014learning} learning is one of the most widely used models in NLP. In the original papers, an encoder RNN reads the input and generates a context vector $\mathbf c$ from the last hidden unit. Then $\mathbf c$ is connected to another RNN called the decoder, which generates the output sequence. Bahdanau et al.~\cite{bahdanau2014neural} use a sequence as the context and propose \textbf{attention mechanism} to adapt the weights of context associated with certain output stage.
More recent studies enable the intelligent learner to handle long and short term memories and apply them to reasoning for tasks such as language understanding and dialogue. The agent takes the following inputs:
* A ''query'' $q$ is the last utterance the speaker said in a general dialogue setting, or the question in a QA setting.
* A ''memory'' vector $\mathbf m$ is the knowledge of the agent plus the dialogue history. The knowledge could be as large as the whole code base or documentation as long as the model is powerful enough to scale to that.
The agent use these modules to handle the inputs:
* An ''encoder'' converts $q$ into a vector. This is a RNN in \cite{cho2014learning} or a simpler word embedding~\cite{mikolov2013efficient}.
* A ''memory module'' $M$ finds the best part of $\mathbf m$ related to $q$. This is called the \textbf{addressing} stage.
* A ''controller module'' $C$ sends $q$ to $M$ and reads back the relevant memory, adds that to the state. In practice we always cycle this process to enable complicate reasoning.
* A ''decoder'' generates output from the final states.
The classic Memory Network~\cite{weston2014memory} is trained in a fully supervised setting, labels of the best part of memory is given at every stage of the memory addressing. Its follow-up End-to-End Memory Network~\cite{sukhbaatar2015end} use soft attention for memory addressing to relax the supervision. This enables to train the attention in back propagation and the only supervision we need is on the output. Using one memory unit to match both the query and the state limits the expressiveness. By splitting the memory into a key-value pair, [[Millor et al.|Key-Value Memory Network]] encodes prior knowledge and gets better results. [[Recurrent Entity Network]] demonstrates how we can enhance the memory unit by letting the agent learn how to read and write the memory to track the facts. Weston~\cite{weston2016dialog} generalize this model to unsupervised learning, by adding a new stage to generate answers and predict replies.
This kind of model is tested on various tasks. For example, basic reasoning on 20 bAbI tasks~\cite{weston2015towards}, reading children's books~\cite{hill2015goldilocks}, understanding real dialogues from movies~\cite{dodge2015evaluating}, etc. All of the tasks plus some others can be found at the bAbI project~\footnote{https://research.fb.com/projects/babi/}. Machine comprehension is another important task, related corpora are CNN and Daily Mail datasets~\footnote{https://github.com/deepmind/rc-data}.
! Theoretical framework and methods
Considering the development of current NLP society, I sort these SE-related tasks by their difficulty.
''Code based reasoning.'' Like in the 20 bAbI task, we can teach our learner to understand the syntactic structure of code and query about the variables, operands or outputs. Of course we can achieve this with the compiler or a corresponding REPL, here we want to build a model to learn the programming language by itself. Applications of this system include code refactoring, auto-generation and translation between natural languages and programming languages.
Seq2seq~\cite{sutskever2014sequence} is state-of-the-art in solving this task. In theory, there exists finite RNNs that are Turing complete~\cite{siegelmann1992computational}, the question is can we train one with back propagation. To add structured information and build complicated models, I will evaluate the encoding performance of different sequence models, such as autoregressive models, RNN, recursive nets and generalize the model into a struct2struct fashion. We can utilize previous structure learning schemes such as \cite{huang2013learning} and \cite{socher2013reasoning}. Programming languages offer stricter grammars and more convenient parsing tools than natural language, which can be converted to supervision on the encoder training.
''Semi-close domain QA.'' In this stage, we no longer restrict the agent to answering fixed tasks but only specify a topic. we train a chatbot to simulate human responses to certain questions. There are already some datasets dedicated to this task. Google used IT troubleshooting chat logs to train their famous conversational agent~\cite{vinyals2015neural}. Lowe et al.~\cite{lowe2015ubuntu} released the Ubuntu Dialogue Corpus containing 1M dialogues on Ubuntu-related problems. Now the challenge is not only to generate answers which seem plausible, but really make sense. Thus we can lower the cost of labor in some software service areas.
Memory network is suitable for this task. To have more flexible memory, I will reach out to Neural Turing Machine~\cite{graves2014neural}, Dynamic Memory Network~\cite{kumar2015ask} and [[Differentiable Neural Computer]] for insights.
''Reading comprehension.'' Then we push the agent's reading and reasoning ability to the limit by letting it read longer and more complex documents. We pose a question to the agent and give it some documents as reference. Then the agent should locate related passages and summarize the answer. Microsoft released a general purpose reading comprehension dataset called MS MARCO~\cite{nguyen2016ms} and provided several benchmarks run by some of the current learning algorithms. We can migrate this case to SE scenario, where we search answers from software documents to users queries.
MS MARCO provides a [[leaderboard|http://www.msmarco.org/leaders.aspx]] to keep track of the best algorithms on this task. The current leaders are Prediction~\cite{wang2016machine} and ReasoNet~\cite{shen2016reasonet}. Prediction use match-LSTM~\cite{wang2015learning} to match the query to informative passages and a modified Pointer Network~\cite{kumar2015ask,trischler2016natural} for answer generation. ReasoNet is a memory network with a termination state.
''The great convergence.'' If we can solve previous individual problems, we are ready to combine these studies to create a universal Software Engineering bot, handling real world SE problems referring to some of the most popular communities. Such an agent could find answering to project, software or language specific questions and help us with development, documenting and quality assurance.
2nd order methods are seldom used in deep learning mainly because we cannot approximate Hessian within batches. SDG has some advantages in stability.
! Newton Algorithm
Assuming a quadratic loss function $E$ we can compute the weight update along the lines of
$$
\Delta w = \eta\left(\frac{\partial^2E}{\partial W^2}\right)^{-1}\frac{\partial E}{\partial W} = \eta H^{-1}\frac{\partial E}{\partial w}
$$
The algorithm itself is unusable for neural nets because the Hessian is not positive definite and the algorithm will diverge.
! Conjugate Gradient
It works only for batch learning. The descent direction $\rho_k$ at iteration $k$ is given as
$$
\rho_k = -\nabla E(w_k) + \beta_k\rho_{k-1}
$$
where the choice of $\beta_k$ can be done either according to Fletcher and Reeves or Polak and Ribiere. Two directions $\rho_k$ and $\rho_{k-1}$ are defined as conjugate if
$$
\rho_k^\top H\rho_{k-1} = 0
$$
i.e. conjugate directions are orthogonal directions in the space of an identy Hessian matrix.
! Quasi-Newton
This kind of method iteratively computes an estimate of the inverse of Hssian and is $O(N^2)$. The estimated inverse Hessian $M$ is positive definite and the search direction is set to:
$$
\rho(t) = M(t)\nabla E(w(t))
$$
The most successful Quasi-Newton approach is BFGS, which updates the estimate as:
$$
M(t) = M(t-1)\left(1+\frac{\phi^\top M\phi}{\delta^\top\phi}\right)\frac{\delta\delta^\top}{\delta^\top\phi}-\left(\frac{\delta\phi^\top M+M\phi\delta^\top}{\delta^\top\phi}\right).
$$
where $\phi$ and $\delta$ are $N\times 1$ vectors:
$$
\begin{align}
\phi &= \nabla E(w(t)) - \nabla E(w(t-1))\\
\delta &= w(t)-w(t-1).
\end{align}
$$
! Gaussian-Newton and Levenberg Marquardt
''They work only for [[mean square error loss functions|Neural Net Functions]]''. They use the square Jacobi approximation for Hessian:
$$
\Delta w = \left(\sum_p \frac{\partial f(w,x_p)^\top}{\partial w}\frac{\partial f(w,x_p)}{\partial w}\right)^{-1}\nabla E(w)
$$
The Leven Marquardt method has a regularization parameter $\mu$ that prevents it from blowing up, if some eigenvalues are small
$$
\Delta w = \left(\sum_p \frac{\partial f(w,x_p)^\top}{\partial w}\frac{\partial f(w,x_p)}{\partial w}+\mu I\right)^{-1}\nabla E(w)
$$
A similar procedure also works with Kullback-Leibler cost and is called Natural Gradient.
[[Block-diagonal LM]] used the diagonal approximation of the Jacobi product in LM algorithm for RNN.
! [[Computing Hessian]]
! [[Apply to Multilayer Nets]]
Tactics * Detect: intrusion, service denial, message integrity, delay * Resist: actors, limit access, encrypt data, validate input, separate entities * React: revoke access, lock computer, inform actors * Recover: maintain audit trail, restore Security patterns * identity management * authentication * access control * secure process management * web services security * cloud computing Frameworks * Spring Security * Apache Shiro * JGuard * [[ZK|https://www.zkoss.org/]]: protects xss, ddos and cross-site request forgery attacks
* Train from manually ''annotated programs'' and avoiding program execution at training time. * Train model with backprop: limited discrete operations ** low-level memory ** require memory to be differentiable ! refs * introduced a floating parser to address the large amount of variation in utterances and table schema for semantic parsing on web tables. * [[Seq2SQL|https://arxiv.org/abs/1709.00103]] ** attention on aggregation op, select column and where clause ** policy gradient training to deal with condition's order issue. @@color:#859900;Policy net is closely related to synthetic gradient@@ ** WikiSQL dataset
! Bib * [[Multi-Scale Context Aggregation by Dilated Convolutions|https://arxiv.org/abs/1511.07122]] ** 2015 paper still state-of-the-art result ** [[Dilated Convolution]]s * [[The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation|http://arxiv.org/abs/1611.09326v2]] ** similar architecture as PixelCNN++ * Fully Convolutional Instance-aware Semantic Segmentation ** 2016 COCO winner ** R-FCN for segmentation ** [[github|https://github.com/daijifeng001/TA-FCN]] code will come up
* [[''MR'': sentence polarity dataset|http://www.cs.cornell.edu/people/pabo/movie-review-data/]], altogether 12662 sent. * [[''Subj'': Subjectivity dataset|http://www.cs.cornell.edu/people/pabo/movie-review-data/]], 10000 sent., subject part of ''MR''. * [[''SST'': Stanford Sentiment Treebank|http://nlp.stanford.edu/sentiment/treebank.html]], 9645 sent. Small but well organized with detailed structure. * [[''TREC'': Question classification dataset|http://cogcomp.cs.illinois.edu/Data/QA/QC/]], 15500 sent. (?), if overlapped, use the last one. * [[''CR'': Customer review dataset|https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html]], see Data Sets section. * [[''MPQA'': Opinion polarity dataset|http://mpqa.cs.pitt.edu/corpora/mpqa_corpus/]], 535 articles. * [[''Opi'': Opinosis Dataset|https://archive.ics.uci.edu/ml/datasets/Opinosis+Opinion+%26frasl%3B+Review]] 51 topics with each 100 sent., which comprises sentences extracted from user reviews on a given topic, e.g. "sound quality of ipod nano". Author's homepage won't work, pointing to UCI site. * [[''Irony''|https://github.com/bwallace/ACL-2014-irony]] 16,006 sent. from reddit.
! Papers # [[Robust Scene Text Recognition with Automatic Rectification|http://arxiv.org/abs/1603.03915]] # [[An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition|http://arxiv.org/abs/1507.05717]] ! Github # [[Convolutional Recurrent Neural Network|https://github.com/bgshih/crnn]] # [[Attention OCR|https://github.com/da03/Attention-OCR]]
! Models * [[Hopfield Net]] * [[LSTM|Long short-term memory]] * Tapes * Arrays * Stacks * Restrictions on recurrent weight matrix ** uRNN ** scoRNN !! Advanced Structures * Faster RNN ** [[Quasi-RNN]] ** [[SRU]] * [[Attention Mechanism]] * Memory networks ** External Memory *** [[Memory Networks]] *** [[Neural Machine Translation by Jointly Learning to Align and Translate|https://arxiv.org/abs/1409.0473]] *** (Stack) RNNs with attention *** Dynamic Mem. Nets * [[Neural Abstract Machines]] * Associative memory ** [[Using Fast Weights to Attend to the Recent Past]] * Adaptive Computation Time ** [[Adaptive Computation Time for Recurrent Neural Networks|https://arxiv.org/abs/1603.08983]] ** [[tf implementation|https://github.com/DeNeutoy/act-tensorflow]], pretty slow * Neural Programmer ! Bibs * [[Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets]] * [[Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision|https://arxiv.org/abs/1611.00020]] ** non-differentiable memory optimized with REINFORCE, improved weakly supervised semantic parsing on WebQuestionsSP. * [[Differentiable neural computers|https://deepmind.com/blog/differentiable-neural-computers/]] ** DeepMind's nature paper * [[Tracking the World State with Recurrent Entity Networks|https://openreview.net/forum?id=rJTKKKqeg]] ** [[tensorflow implementation|https://github.com/jimfleming/recurrent-entity-networks]] ** the results aren't easily comparable to DNC Most networks with external memory use some form of ''content-based'' memory access, find the memory closest to some ''key vector'' emitted by the network, return either the memory content or an associated ''value vector''. !! Bibs * [[MemN2N|http://arxiv.org/abs/1604.06045]] * [[Can Active Memory Replace Attention?|https://arxiv.org/abs/1610.08613]] ** Extended Neural GPU with an output tape tensor $p$, which has access to @@color:#859900;all previous outputs@@. ** Use teacher forcing in training.
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
[[link|https://blogs.princeton.edu/imabandit/2017/07/05/smooth-distributed-convex-optimization/]]
! Setting
The goal is to find in a distributed way the optimal "consensus" point
$$
x^*\in \arg\min_{x\in\mathbb R^d}\sum_{v\in V}f_v(x)
$$
where $v$ is a computing unit with access to a private dataset. $f$ is $\beta$-smooth and $\alpha$-strongly convex ($\kappa=\beta/\alpha$ is the condition number). We expect ''linear convergence'', i.e., the scaling of $T_\epsilon$ should be $\log(1/\epsilon)$.
[[Python Snippets]] [[Config Files]] [[System Snippets]]
[[paper|https://arxiv.org/abs/1706.04859]] Optimising neural networks to not only approximate the function's outputs but also the function's derivatives. When the ground truth function is a network: * model compression * model distillation * gradient synthesis Foundations * Hornik proved the universal approximation theorems for neural networks in Sobolev spaces - metric spaces where distances between functions are defined both in terms of their differences in values and differences in values of their derivatives. * Sigmoid network's derivates w.r.t. its inputs can approximate the corresponding derivates of the ground truth function arbirarily well too.
Soft here means entropy regularized. With entropy regularization, we need to alter our notion of a greedy policy, as the optimal policy is stochastic.
$$
V(s') = \text{soft}\max_{a'}Q_\phi(s', a') = \log\int\exp(Q_\phi(s', a'))da'
$$
$\pi(a|s)=\exp(Q_\phi(s, a)-V(s))$ optimizes $\sum_tE_{\pi(s_t, a_t)}[r(s_t, a_t)]+E_{\pi(s_t)}[\mathcal H(\pi(a_t|s_t))]$
The intuition is that $\pi(a|s)\propto\exp(Q_\phi(s, a))$ when $\pi$ minimizes $D_{KL}(\pi(a|s)||\frac1Z\exp(Q(s,a)))$
$$
D_{KL}(\pi(a|s)||\frac1Z\exp(Q(s,a))) = E_{\pi(a|s)}[Q(s, a)]-\mathcal H(\pi)
$$
This method combats premature entropy collapse and is closely related to soft Q-learning.
!! Benefits
* Improve exploration and prevent entropy collapse
* Principled approach to break ties
* Can reduce to hard optimality as reward magnitude increases
* Good model for modeling human bebavior
* Compositionality: $Q_1+Q_2$ is the soft Q-function for $r_1+r_2$
!! Soft Q-learning
! Bibs
* Linearly solvable Markov decision problems: one framework for reasoning about soft optimality.
* General duality between optimal control and estimation: primer on the equivalence between inference and control.
* Optimal control as a graphical model inference problem: frames control as an inference problem in a graphical model.
* Modeling interaction via the principle of maximal causal entropy: connection between soft optimality and maximum entropy modeling.
* On stochastic optimal control and reinforcement learning by approximate inference: temporal difference style algorithm with soft optimality.
* Reinforcement learning with deep energy based models: soft Q-learning algorithm, deep RL with continuous actions and soft optimality
* Bridging the gap between value and policy based reinforcement learning.
* Equivalence between policy gradients and soft Q-learning.
** Soft Q-learning loss gradient can be interpreted as a policy gradient term plus a baseline-error-gradient term, corresponding to policy gradient instantiations such as A3C.
In ''multinomial logistic regression'', if we see the process as a set of independent binary regressions, we run $K-1$ independent binary logistic regression models for $K$ possible outcomes.
$$
\ln\frac{p(y_i=j)}{p(y_i=1)} = \mathbf w^T_j\mathbf x_i+b_j
$$
The probability sum to one so
$$
p(y_i=j) = \frac{\exp(w^T_j\mathbf x_i+b_j)}{1+\sum_{k=2}^K\exp(w^T_k\mathbf x_i+b_k)}
$$
Or we can see it as a log-linear model:
$$
p(y_i=j) = \text{softmax}(j, \mathbf w^T_1\mathbf x_i+b_1, \cdots, \mathbf w^T_K\mathbf x_i+b_K)
$$
The softmax function thus serves as the equivalent of the logistic function in binary logistic regression.
Ahmed E. Hassan * Analyzing a Decade of Linux System Calls ** System calls and related commits are extracted ** analyze the commit type and draw some empirical conclusions ** find patterns in API evolution ** some conclusions about software developments are drawn * Towards a Better Understanding of the Impact of Experimental Components on Defect Prediction ** very nice thesis on defect prediction David Lo Bug localization * Information Retrieval and Spectrum Based Bug Localizatin: Better Together ** use bug report to query defected code parts * Compositional Vector Space Models for Improved Bug Localization ** more data but only searching for file ** solving LDA with GA seems so weird similar to a learn to rank task * Synergizing Specification Miners through Model Fissions and Fusions ** static program analysis for documentation ** construct FSA from execution traces ** fissing FSA to common blocks ** fuse FSA from a set of LTL constraints * Who Should Review This Change? Putting Text and File Location Analyses Together for More Accurate Recommendations ** text recommendation system to suggest best reviewer for certain piece of code ** description field of review request in important, also the changed file directory ** another learn to rank task * What Are the Characteristics of High-Rated Apps? A Case Study on Free Android Applications ** a hypothesis testing on 28 factors of 1,492 apps to the ratings ** didn't find the description but inferring from the dataset and results, I guess they are doing a 2-class classification. * What’s Hot in Software Engineering Twitter Space? ** Social network mining on twitter ** Filter out related tweets, rule based methods * Deep Learning for Just-In-Time Defect Prediction ** a simple classification task with RBM, no gimmicks.
! Bibs * [[A Survey of Machine Learning for Big Code and Naturalness|https://arxiv.org/abs/1709.06182]] ! Methods * Theory first approach: formal, or logico-deductive, approach ** The elegance and rigor of definitions, abstractions, and formal proofs-ofproperties are of dominant concern * Machine learning ! Applications * [[Security]] * program verification ** Behavioral consistency of C and Verilog programs using bounded model checking (2003) * bug finding ** A few billion lines of code later: using static analysis to find bugs in the real world (2010) * refactoring ** The ASTRÉE analyzer (2005) * Knowledge Representation ** HDSKG (SANER 2017) *** Extract relation triples from stackoverflow tagWiki *** Parsing and POS using coreNLP *** Relations discovered with rule based methods *** Filtered with semi-supervised classification (garbage in garbage out?) ** Unsupervised Software-Specific Morphological Forms Inference from Informal Discussions (ICSE 2017) *** Build SE thesaurus and detecting synonyms from unsupervised corpus i.e. Stack Overflow. *** Train a new [[Word Embedding]] (skip-gram) to group similar words *** Discriminate synonyms and abbreviations ** Mining Analogical Libaries in Q&A Discussions (SANER 2016) *** Learn similar libraries (of different langs) from stackoverflow tags *** association rule mining on tag co-occurence *** POS and phrase chunking on tagWiki to classify tags *** [[Word2Vec]] library grouping ** TechLand: Assiting Technology Landscape Inquiries with Insights from Stack Overflow (ICSME2016) *** QA system based on knowledge graph *** similar methods with previous paper *** Queries are stemmed and parsed, greedily match tech terms *** Visualization from tagWiki data and knowledge graph *** Recommondation based on co-occurrence and trends * Neural Language Interface ** Learning to Extract API Mentions from Informal Natural Language Discussions *** matching natural language and APIs, similar to NER *** @@color:red;relative small dataset@@ *** @@color:#859900;broaden the scope to doc2code@@ *** @@color:red;having trouble disambigurating keywords and common words@@ *** @@color:#859900;a pointer network should decide when to copy and when to generate@@ *** Brown clustering and word embeddings quantized features as input of *** semi-supervised CRF classifier * Predicting Semantically Linkable Knowledge in Developer Online Forums via CNN ** Use a siamese net for feature extraction ** @@color:red;sequential information is lost with CNNs@@ ** @@color:#859900;use relation network to model covariance@@ ** @@color:red;relatedness is not linear, should predict with multiheads@@ ** @@color:#859900;triplet loss to learn to rank@@ ! Directions * Make use of community: Open-source software systems such as Linux,MySQL, Django, Ant, and OpenEJB have become ubiquitous. These systems publicly expose not just source code, but also meta-data concerning authorship, bug-fixes, and review processes. * Literate programming: The naturalness hypothesis [[NLP for SE]] * Automatic test cases generation, distributed system testing and bug finding. It would be great we can relate AI with the security community. * [[AI for SE Mind Map]]
* Language Models ** Code fixing *** 2016 Automated Correction for Syntax Errors in Programming Assignments using Recurrent Neural Networks. ** Code generation *** 2016 Learning Python Code Suggestion with a Sparse Pointer Network *** 2016 A deep language model for software code: meaningless paper with a concept software called DeepSoft that never exists *** 2016 [[Neural Code Completion|https://openreview.net/forum?id=rJbPBt9lg]]: rejected ICLR17, read the review and traditional baseline methods *** 2016 [[Program Synthesis for Character Level Language Modeling]] *** 2017 Neural Attribute Machines for Program Generation ** Benchmark synthesis *** 2017 Synthesizing benchmarks for predictive modeling * Code Transducer Models: statistical machine translation or deep learning, evaluated by BLEU ** Code migration ** Pseudocode generation ** Code fixing *** 2016 sk_p: a neural program corrector for MOOCs * Multimodal Models ** Code systhesis: from natural language to code *** 2014 Structured Generative Models of Natural Source Code **** Read for a summary of traditional methods and program induction introduction *** 2015 Bimodal modelling of source code and natural language: also traditional method *** 2016 [[Latent Predictor Networks for Code Generation]] *** 2017 Program synthesis from natural language using recurrent neural networks *** 2017 Abstract Syntax Networks for Code Generation and Semantic Parsing *** 2017 A Syntactic Neural Model for General-Purpose Code Generation **** seq2action2tree permits syntax guarantee, right way to follow **** Sequence model kind of simple. Can external memory improve parent supervision? Is conv encoder helpful? **** Invent code evaluation metric: something like ''Semantic Textual Similarity'' *** Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning **** Policy-based RL helps with the where clause accuracy ** Documentation *** 2016 Summarizing source code using a neural attention model *** 2017 [[A parallel corpus of Python functions and documentation strings for automated code documentation and code generation|https://arxiv.org/abs/1707.02275]]: provides [[code-docstring-corpus|https://github.com/EdinburghNLP/code-docstring-corpus]] and use [[Nematus|https://github.com/EdinburghNLP/nematus]] as baseline model * Program Induction ** [[Neural Abstract Machines]] ** Program by Examples *** [[Glass-Box Program Synthesis: A Machine Learning Approach|https://scirate.com/arxiv/1709.08669]]: search based no ML *** [[Neural Program Meta-Induction|https://arxiv.org/abs/1710.04157]]: DeepMind **** No explicit program representation, task-specific learning, cross-task knowlege sharing **** Conv encoder, lstm decoder ! Metrics * Accuracy: exact match is too strong * BLEU does not measure semanics * Execution accuracy (for sql query results, and maybe #tests passed?) * Static code analysis
国内melpa无法访问，添加下面的代码到`.spacemacs`的`dotspacemacs/user-init()`
```
(setq configuration-layer--elpa-archives
'(("melpa-cn" . "http://elpa.emacs-china.org/melpa/")
("org-cn" . "http://elpa.emacs-china.org/org/")
("gnu-cn" . "http://elpa.emacs-china.org/gnu/")))
```
! Helm
* projectile-ag search: `spc s p`, jump back with `Ctrl-o`
Typical speaker diarization tasks are carried with a two-stage procedure. The first is to segment the audio with some ([[changepoint algorithm|Changepoint algorithms]]). Then the inferred segments are grouped into a set of speaker labels. A common approach is to use hierarchical agglomerative clustering with BIC stopping criterion [chen98]. This scheme suffers from the fact that errors made in the segmentation stage can degrade the performace of the subsequent clustering stage. A number of algorithms iterate between multiple stages of resegmentation and clustering. But agglomerative clustering is extremely sensitive to the specified threshold for clustering merging, with different settings leading to either over- or under-clustering of the segments into speakers. Clustering and segmentation are joint by employing HMMs to capture the repeated returns of speakers. Since the state space is unknown, [meignier00] uses an evolutive-HMM. The HMM is initialized to have one state and speakers are gradually added. [wooters07] starts with a number of states larger than the speakers and iteratively merge them according to metrics based on BIC. At each iteration, [[Viterbi decoding]] is performed to resegment the features of the audio.
A spectral envelope is a curve in the frequency-amplitude plane, derived from a Fourier magnitude spectrum. It describes one point in time (one window, to be precise). ! Spectral envelope correction [[link|http://recherche.ircam.fr/anasyn/schwarz/da/specenv/3_3Spectral_Envelopes.html]] In speech or in the singing voice, the spectral envelope is quite independent of the pitch (see section 2.4 for why this is so). However, if we transpose the vowel in figure 2.23 up by one octave by multiplying the frequencies of all partials by 2 and performing an additive resynthesis, the spectral envelope will necessarily be transposed also. Figure 2.26 shows this effect which sounds quite unnatural (it is sometimes termed the mickey mouse effect ). The unnaturalness comes from the fact that the formants are shifted up one octave, which corresponds to shrinking the vocal tract to half of its length. Obviously, this is not the natural behaviour of the vocal tract. To avoid this, the spectral envelope has to be kept constant, while the partials ''slide'' along it to their new values. This means that the amplitude of a transposed partial is no longer determined by the amplitude of the original partial, but by the value of the spectral envelope at the frequency of the transposed partial, as in figure 2.27. This way, only the partials are shifted, but the spectral envelope and thus the formant locations stay the same, making the vowel sound natural.
! Reports * [[Locally Linear Embedding]] * [[LSTM Research Proposal]] * [[Voice Conversion Research Proposal]] ! [[Speech Experiments]] !! TODO !!! Experiments # Metric based classification #* Theory: [[Accoustic Saliency Summary]] #* Coding: [[Implementing Diarization]] # LLE #* Previous work: [[Emotion Code (Chen Li)]] # ScatNet #* [[Code reading|ScatNet Code]] ! Speaker Recognition Techniques [[JFA & I-Vector]] ! Speech Recognition Techniques !! [[HMM|Hidden Markov Model]] [[Baum-Welch]] [[HDP-HMM Diarization]] !! [[Recurrent Temporal RBM]]
* [[TIMIT]] * [[TIMIT HF]]
! Speaker Variations !! GMM-HMM vocal tract length normalization (VTLN): warps the frequency axis of the filter-bank analysis to account for the fact the locations of vocal-tract resonances vary roughly monotonically with the ''vocal tract length'' of the speaker. The optimal ''warping factor'' is found using EM by repeatedly selecting the best factor given the current model and then updating the model using the selected factor. feature-space Maximum Likelihood Linear Regression (fMLLR): applies an affine transform to the feature vector so that the transformed feature better matches the model. It is typically applied to testing utterance by first generating recognition results using the raw feature and then re-recognizing the speech with the transformed feature, (which can be iterated several times). !! DNN For GMM-HMMs, fMLLR transforms are estimated to maximize the likelihood of the adaptation data given the model. For DNNs, they are optimized to maximize cross entropy (with backpropagation). This procedure is thus referred as feature-space discriminative linear regression (fDLR). Experiments show that DNNs do not depend on these transforms as GMM-HMM and shallow MLPs.
! Problem Given a bunch of training voices and target texts, generate the speech. The output contains spectral features and excitation parameters (pitch and aperiodic energy) and their time derivatives of first and second order. Tools to extract these features include EMIME project and STRAIGHT acoustic analysis. ! HMM/GMM Based Systems * MAP * MLLR * CMLLR derived from ML based optimization in HMM forced alignment In the conventional HMM-based approach, a decision tree based contextual clustering is used to model the relationship between text and corresponding voice features. !! Models Multi-space probability distributions HMM (MSD HMM) to model observation sequences with continuous log F0 (voiced) and discrete symbol (unvoiced). The use of HSMMs allow us to incorporate state duration models explicitly not only in the synthesis part but also in the training part of the system. !! Examples [[HTS]] ! DNN based models [[Qian et al|DNN for parametric TTS]] !! [[Google DNN TTS]] ! Data CMU ARCTIC database of 7 speakers, [[Voice Conversion]]
* [[paper|https://arxiv.org/abs/1709.02755]]
* [[code|https://github.com/taolei87/sru]]
A simplified GRU omitting $h_{t-1}$ in gemms in gates and input value computation. This makes GEMMs parallelizable. Use [[Highway Connections]] and [[Variational Dropout]]
!! CUDA optimization
Merging 3 GEMMs togather. This is a common LSTM speedup. But now we can do whole period at once.
! Implementation
Implemented with CuPy and pynvrtc
!! DrQA example
Different batch structure:
* SRU
** context_id, context_feature, context_tag, context_ent, context_mask, question_id, question_mask, (y_s, y_e,) text, span
**
* parlai
** x1, x1_f, x1_mask, x2, x2_mask, (y_s, y_e,) text, spans
** torch.cuda.ByteTensor of size 30x20
Have auxiliary input of tag and entity: POS and NER
`model.py` modifications:
* change the logger name
* save & load optimizer state dict
* change the dimension of inputs (for POS and NER features): 2 new features
`rnn_reader.py` modifications:
* different input order for question embedding
* add 'pos' and 'ner' features
* support embedding random initialization
`layers.py`
* don't pad during evaluation
* replace LSTM with SRUCell
* dropout implemented within layer
* the output is contiguous, input size could be different?
I migrated the code into ParlAI, still have to figure out the data changes
* where to get the embeddings?
* differences in opt?
! Remarks
* Compare embedding implementation
* Add POS and NER in parlai
* Prepare different initialization methods
[[Case Studies|http://mc-stan.org/users/documentation/case-studies.html]]
! From RNNs to CNNs
Recursive neural networks are a special type of trees. Recursive NN require a parse tree, which is in some way suboptimal because we have separate objective functions that need to find a good parse tree and then apply NN architecture. Ideally we want only raw input and perform a end 2 end learning.
Recurrent NN could do that but fails to capture specific phrases without prefix context (which always modifies our hidden representation) and often capture too much of last words in final vector (in standard RNN, which forgets).
RNN can only get compositional vectors for grammatical phrases. We can instead use CNN to compute every possible phrase. This method is not very linguistically or cognitively plausible but does not introduce much noise.
! Architecture in [[Kim (2014)|Convolutional Neural Networks for Sentence Classification]]
!! Conv
We can use the first layer to compute all bigrams as a convolution over the word vectors. For each pair:
$$
p = \tanh\left(W\left[\begin{array}{c} x \\ y \end{array}\right]+b\right)
$$
Here we are doing more matrix operations than in RNNs, but we can parallelize them. With a softmax classifier on top of this vector and propagate down to all the phrases.
A convolution filter $w\in\mathbb R^{hk}$ goes over $h$ words. Then the value of the neuron is:
$$
c_i = f(w^\top x_{i:i+h-1}+b)
$$
The resulting feature map: $c=[c_1, \cdots, c_{n-h+1}]$. We use zero-padding for short sentense, and for very long sentenses, we can do pooling.
!! [[Pooling Layer]]
Here we introduce the max-over-time pooling. Pooled single number: $\hat c = \max\{\mathbf c\}$. To have more features we can use different window sizes and multiple weights.
!! [[Dropout Layer]]
We randomly mask/dropout/set to 0 some of the feature weights $z$. First create masking vector $r$ of Bernoulli random variables with probability $p$ of being 1. And delete some of our feature $z$:
$$
y = softmax(W^{(S)}(r\circ z)+b)
$$
The idea behind this is we can prevent co-adaptation. If the model is too powerful, we can learn very specific feature constellations. At test time, there is no dropout
!! Regularization
Constrain $l_2$ norms of weight vectors of each class (row in softmax weight $W^{(S)}$ to fixed number $s$.
!! Details and Results
The hyperparameters chosen are:
* ReLU
* window filter size: 3-5
* each filter size has 100 feature maps
* dropout p=0.5
* L2 constraint s for rows of softmax s=3
* mini batch size for SGD training: 50
* word vectors: pre-training with word2vec, k=300
! Similarity between Recursive NN to CNN
* We can set stride size to make CNN word features never overlap, which gives a similar structure as RNN. And to use weighted average pooling instead of max pooling to make every child node contribute to the parent.
* We can tie weights of filters inside vs across different layers in CNN
* Other differences are CNN have multiple filters, and max-pooling.
* In RNN we use an input specific tree.
Computational trade-off.
At the time this note is edited, TensorFlow is still a quite new technology and this seems to be the first decent TensorFlow tutorial. TensorFlow provides primitives for defining functions on tensors and automatically computing their derivatives. ! Deep-Learning Package Choices There are a zoo of deep learning libraries. Each major company and many acedemic labs have their own deep learning libraries. We need to start ask the questions, what lib should we use and why should we use it. The first property we concern is that, when we are writing deep learning models, are we writing configuration files or programs. Caffe, DistBelief and CNTK use files to specify the complicated deep models. This is kind of convenient and easy to copy-and-paste. However, for very deep models, things get exremely heavy. ResNet have 7k lines of configuration file. So we want the model description to be part of our programs and use standard programming structures like for-loops to deal with that. We can use 10 LOC to construct a RNN in tf, while caffe don't let us do that. Numerical computation with high-level languages wrapped over cpp save us from concerning about memory management, etc, and let us focus on the model itself. Lua and Python are the two choices in deep learning area. Lua is a game engine allowing fast calling to C. Torch makes good use of Lua and ties directly down to the Lua numerical system. Python has a richer community and library infrastructure. Theano is a symbolic tensor manipulation package in python. It has been around longer. TensorFlow has more support for building giant models with distributed systems and has support from Google. The downside of TensorFlow is that it is a bit slower. We hope Google can fix that soon. Models from other library should have no problem migrating to TensorFlow. ! What is TensorFlow TensorFlow is numerical packages dealing with math construct called tensors. ; Tensor : are mulilinear maps from vector spaces to the real numbers ($V$ vector space, and $V^*$ dual space) $$ f: V^*\times\cdots V^*\times V\times\cdots V\rightarrow \mathbb R $$ Common to have fixed basis, so a tensor can be represented as a multidimensional array of numbers. TensorFlow is similar to ''numpy'', but with GPU support and automatically compute derivatives. numpy: ```python a = np.zeros((2,2)); b = np.ones((2,2)) np.sum(b, axis=1) # array([ 2., 2.]) a.shape # (2,2) np.reshape(a, (1,4)) # array([[ 0., 0., 0., 0.]]) ``` TensorFlow: ```python tf.InteractiveSession() a = tf.zeros((2,2)); b = tf.ones((2,2)) tf.reduce_sum(b, reduction_indices=1).eval() # array([ 2., 2.], dtype=float32) a.get_shape() # TensorShape([Dimension(2), Dimension(2)]) np.reshape(a, (1,4)).eval() # array([[ 0., 0., 0., 0.]], dtype=float32) ``` A lot of numpy operations can be carried to TensorFlow. But TensorFlow is different that it requires explicit evaluation. Its computations define a ''computation graph'' and `.eval()` runs the graph in the context. While numpy is doing those operations on memory, TensorFlow compiles them at first. ! [[TensorFlow Basics]] ! Examples !! [[Linear Regression in TensorFlow]] ! Comments TensorFlow can be seen as a numpy wrapper, but is less mature, not as many operations are supported. We can convert tensors to numpy arrays and print, e.g. matrices indexing not as convenient, so the natural softmax representation in numpy is not feasible in TensorFlow. TensorBoard could be useful.
[[Lecture 2|Word Vectors]] [[Lecture 7|Stanford Introduction to TensorFlow]] [[Lecture 13|Stanford CNN for NLP]]
A statistical manifold is a Riemannian manifold, each of whose points is a probability distribution. Statistical manifolds provide a setting for the field of [[Information geometry]]. The [[Fisher information metric]] provides a metric on these manifolds. Let $X$ be an orientable manifold, and let $(X,\Sigma,\mu)$ be a measure on $X$. Equivalently, let $(\Omega,\mathcal F,P)$ be a probability space on $\Omega=X$, with [[sigma algebra]] $\mathcal F=\Sigma$ and probability $P=\mu$. The statistical manifold $S(X)$ of $X$ is defined as the space of all measures $\mu$ on $X$ (with the sigma-algebra $\Sigma$ held fixed). Note that this space is infinite-dimensional; it is commonly taken to be a [[Frechet space]]. The points of $S(X)$ are measures. Rather than dealing with an infinite-dimensional space $S(X)$, it is common to work with a finite-dimensional submanifold, defined by considering a set of probability distributions parameterized by some smooth, continuously-varying parameter $\theta$. That is, one considers only those measures that are selected by the parameter. If the parameter $\theta$ is n-dimensional, then, in general, the submanifold will be as well. All finite-dimensional statistical manifolds can be understood in this way
We want to learn a true distribution $Q$, which can be observed from i.i.d. samples. We have a parametric class of models $\mathcal P$ and we have parameters identify an individual model $P$. The task is to navigate through the class of models and find a good one.
We assume $P$ is capable of:
* sample from, (random field models, graphical models are examples cannot sample from)
* compute parameter gradient w.r.t. samples
* strongest assumption is we can compute the likelihood
[[GAN|Generative Adversarial Network]] is a likelihood-free model which can be sampled from. Because many possible input could be mapped to the same output. To compute a pointwise likelihood, we have to find the preimage, which is not tractable.
! Measure the distance
!! Integral Probability Metrics
* Muller, 1997
* Sriperumbunder et al., 2010
Taking the supremum over a difference of expectations.
$$
\gamma_{\mathcal F}(P, Q) = \underset{f\in\mathcal F}{\sup}\left|\int fdP-\int fdQ\right|
$$
Depending on the structure of $\mathcal F$, we get different measures.
* Energy statistics [Szekely, 1997]
* $\mathcal F$ is a unit-ball in a RKHS $\mathcal H$: Kernel [[MMD|Maximum Mean Discrepancy]]
* Wasserstein distance [Cuturi, 2013]
* [[DISCO Net]]s [Bouchacourt et al. 2016]
**Wasserstein Distance**:
$$
W_c(P\|Q):=\inf_{\rho:\rho_X=P, \rho_Y=Q}\int\int c(x, y)d\rho(x, y)
$$
Because we use integral here, we can compute this distance with samples.
!! Proper scoring rules
* [Gneiting and Raftery, 2007]
We take expectation of a score of how well the distribution $P$ fits the realization $x$.
$$
S(P, Q) = \int S(P, x)dQ(x)
$$
$$
S(P, P) \ge S(P, Q), \forall P, Q\in\mathcal P
$$
The scores can be used are:
* log-likelihood, $S(P, x)=\log P(x)$ then this is maximum likelihood
* Bayes score
* Quadratic score, $S(P, x) = 2P(x)-\|P\|^2_2$
* Pseudoshperical score, $S(P, x) = P(x)^{\alpha-1}/\|P\|_{\alpha}^{\alpha-1}$
!! $f$-divergence
* [Ali and Silvey, 1966]
* [[Divergence Classed GANs]]
$f$-divergence is a generalization of KL divergence. Now we require knowing the distribution of both $P$ and $Q$
$$
D_f(P\|Q) = \int f\left(\frac{dP(x)}{dQ(x)}\right)dQ(x)
$$
where $f$ is a convex function of likelihood ratio. $f: \mathbb R\rightarrow\mathbb R\cup\{+\infty\}$. Properties:
# $D_f(P\|Q)\ge 0$ and $D_f(P\|Q) = 0$ when $P=Q$;
# $D_f(P\|Q)=D_g(P\|Q)$ for any $g(x) = f(x) + \lambda(x-1)$
# Taking $f_0(x) := f(x)-(x-1)f'(1)$ is good. It's nonnegative, nonincreasing on $(0, 1]$ and nondecreasing on $[1, \infty)$;
# Taking the convec restriction $f_+(x) = f(x)$ if $x\ge0$ and $\infty$ otherwise.
Examples are
* [[Kullback-Leibler divergence]]: $f = x\log x$
* Reverse KL: $f(x) = -\log x$
* [[Jensen-Shannon divergence]]: $f(x) = -(x+1)\log\frac{x+1}{2}+x\log x$
* Total variation: $f(x) = |x-1|$
* Pearson $\chi^2$
Direct MLE
$$
\max_{\theta\in\mathbb R^d}\frac1m\sum_{i=1}^m\log P_\theta(x^i)
$$
is equivalent to minimizing the KL-divergence $KL(P_r\|P_\theta)$.
! Foundamentals * [[Principle of maximum entropy]] * [[Statistical manifold]] * [[Stochastic Processes]] * [[Graphical Model]] * [[Bayesian]] * [[Influence Function]] The three axioms of Probability theory # $P(A)\ge0$ for all events $A$ # $P(\Omega) = 1$ # $P(A\cup B)=P(A)+P(B)$ for disjoint events $A$ and $B$ In probability theory, in contrast, it is the events and their probabilities that are viewed as being fundamental, with the sample space $\Omega$ being abstracted away as much as possible, and with the random variables and expectations being viewed as derived concepts. [[Free Probability]] ! Distributions * [[Gaussian]] ! [[Probabilistic Models]]
The formula as typically used in applications is
$$
\ln(n!) = n\ln(n) - n +O(\ln(n))
$$
The next term in the O(ln(n)) is (1/2)ln(2πn); a more precise variant of the formula is therefore
$$
n! \sim \sqrt{2 \pi n}\left(\frac{n}{e}\right)^n
$$
Being an asymptotic formula, Stirling's approximation has the property that
$$
\lim_{n \to \infty} \frac{n!}{\sqrt{2\pi n} \left(\frac{n}{e}\right)^n} = 1.
$$
! Videos
* [[Optimization for Machine Learning|https://simons.berkeley.edu/talks/elad-hazan-01-23-2017-1]]
! Theory
* [[Gradient Descent Basics]]
* [[Gradient Descent Properties]]
* [[Stochastic Gradient MCMC]]
* [[On SGD’s Failure in Practice: Characterizing and Overcoming Stalling]]
!! Deep Learning Insights
* [[Escaping Saddle Points]]
* [[Rethinking Generalization]]
!! [[A Variational Analysis of Stochastic Gradient Algorithms|https://arxiv.org/abs/1602.02666]]
[Robbins and Monro 1951] proves SGD reaches the optimum of the function.
!! Low Precision
* [[Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent]]
!! Constant Learning Rate
* [[Stochastic Gradient Descent as Approximate Bayesian Inference]]
Constant SGD can be interpreted as a stochastic process with a stationary distritbution. It can be interpreted as an Ornstein-Uhlenbeck process. Minimizing the KL, we can relate the optimal step size or preconditioning matrix to the Hessian and noise covariance near the optimum. The resulting criteria strongly resemble AdaGrad, RMSProp and classical Fisher scoring.
Using SGD with a constant learning rate allows simultaneous inference of the posterior and optimization of meta-level parameters.
$$
d\theta(t) = -g(\theta)dt+\sqrt{\frac\epsilon S}BdW(t)
$$
where $C = BB^\top$ is the constant noise covariance of the gradient $g$, $\Delta g(\theta)\sim\mathcal N(0, C(\theta))$. This results in a specific kind of stochastic process, the multivariate ''Ornstein-Uhlenbeck'' process:
$$
d\theta(t)=-A\theta(t)dt+B_{\epsilon/S}dW(t)
$$
which has an analytic stationary distribution $q(\theta)$ that is Gaussian $q(\theta)\propto\exp\{-\frac 1 2\theta^\top\Sigma^{-1}\theta\}$. The covariance satisfies
$$
\Sigma A^\top+A\Sigma=\frac\epsilon SBB^\top
$$
The resulting covariance is proportional to the learning rate $\epsilon$ and inversely proportional to the magnitude of $A$ and minibatch size $S$.
* Modify SGD in the simplest way s.t. it becomes an efficient approximate Bayesian sampling algorithm * Construct other sampling algorithms such as preconditioning, momentum or Polyak averaging Constant SGD is a stationary stochastic process centered on the optimum and has a certain convariance structure. It is a hybrid of MC and variational algorithms.
! Bibs
* [[Bayesian Learning via stochastic Gradient Langevin Dynamics|http://people.ee.duke.edu/~lcarin/398_icmlpaper.pdf]]
* [[A Variational Analysis of Stochastic Gradient Algorithms|https://arxiv.org/abs/1602.02666]]
** Langevin dynamics is the discrete-time approximation of a continous-time stochastic differential equation.
* [[Approximation analysis of stochastic gradient Langevin dynamics by using Fokker-Planck equation and Ito process|http://www.jmlr.org/proceedings/papers/v32/satoa14.html]]
** detailed convergence analysis
** [[video|http://techtalks.tv/talks/approximation-analysis-of-stochastic-gradient-langevin-dynamics-by-using-fokker-planck-equation-and-ito-process/61010/]]
! Introduction
The Langevin method is a simpification of Hamiltonian dynamics where only a single leapfrog step is used.
This algorithm samples from a Bayesian posterior by adding artificial noise to the stochastic gradient which, at long times, dominates the SGD noise. Though elegant, one disadvantage of SGLD is that the learning rate must be decreased to achieve the correct sampling regime, and the algorithm can suffer from slow mixing times.
SG Fisher scoring speeds up mixing times in SGLD by preconditioning a gradient with the inverse sampling noise covariance.
! Computation
The SGLD technique generates a set of samples $\{\hat\omega_t\}$ from the model's posterior over the random variable $\omega$ by adding stochastic gradient steps to @@color:#859900;the previously generated samples@@:
$$
\delta\omega = \frac\epsilon2(\nabla\log p(\omega)+\frac NM\sum_{i\in S}\nabla\log p(y_i|x_i,\omega))+\eta
$$
where $\eta\sim\mathcal N(0, \epsilon)$ and $S$ is a randomly sampled set of $M$ indices from $\{1, \dots, N\}$. $\epsilon$ is decreased in magnitude following the Robbins-Monro equations. @@color:#859900;The approximate posterior structure need not be specified.@@
! Remarks
SGLD often collapses to a single mode.
! Bibs * [[A Complete Recipe for Stochastic Gradient MCMC|https://arxiv.org/abs/1506.04696]] ! Models * [[Stochastic Gradient Langevin Dynamics]] * SG Hamilton * SG thermostats * SG Fisher scoring SGD with a constant learning rate simulates a Markov chain with a stationary distribution. SG has been used in the service of scalable Bayesian MCMC methods, where the goal is to generate samples from a conditional distribution of latent variables given a data set. SG MCMCs employ stochastic gradients of $\log p(\theta, \mathbf x)$ to improve convergence and computation of existing sampling algorithms.
! Quant's View
Summary of [[YLK Samo et al. 2016|https://arxiv.org/abs/1605.02654]]
!! Problem
The goal is to find a portfolio selection to outperform the market index with probability 1. Such a strategy is called a ''relative arbitrage''. We evaluate those strategies based on certain metrics. The chosen performance metric may depart from the excess return, for instance by adjusting the risk taken.
!! Assumptions
Relative arbitrage can be achieved under some certain assumtions of the market:
* Fernholz's ''master equation'': free from stochastic integration
* Stock capitalisation models
The dynamics of the $n$ positive stock capitalisation processes $X_i(\cdot)$, $i=1, \dots, n$ are described by the following system of SDEs:
$$
dX_i(t) = X_i(t)(b_i(t)dt+\sum_{\nu=1}^d\sigma_{i\nu}(t)dW_\nu(t)).
$$
for $t\ge0$ and $W_\nu$ are independent standard Brownian motions with $d\ge n$, and $X_i(0)>0$. The ''rates of return'' $b_i(\cdot)$, and ''volatilities'' $\sigma$ are some unspecified $\mathbb F$-progressively measurable processes (filtration for stochastic processes means cannot see into the future) and are assumed to satisfy the integrability condition
$$
\sum_{i=1}^n\int_0^T(|b_i(t)|+\sum_{\nu=1}^d\sigma_{i\nu}(t)^2)dt<\infty, \qquad \mathbb P-a.s.,
$$
for all $T\in(0, \infty)$, and the ''non-degeneracy'' condition
$$
\exists\epsilon>0:\xi^T\sigma(t)\sigma^T(t)\xi\ge\epsilon\|\xi\|^2,
$$
!! Difficulties
* Inverse problem: the search space is too large to prove a strategy is optimal.
* Several market imperfections are ignored, e.g. bankruptcy
* Driven only by @@color:#859900;market capitalisations@@
! DL Practitioner's View
!! Sequence Prediction
We can construct an end-to-end sequence model to predict the price, variance
! Continuous Time ! Discrete Time ! Both [[HMM|Hidden Markov Model]]
For RNNs, $\lambda$ can be adjusted to be much larger.
For large values of $\lambda$ any Tikhonov-damped 2nd-order optimization approach (e.g. HF) wil behave similarly to a 1st-order approach and crucial low-curvature directions whose associated curvature is significantly smaller than $\lambda$ will be effectively masked-out.
One hypothesis is that for certain small changes in $\theta$ there can be large and highly non-linear changes in the hidden state sequence $h$ (given that $W_{hh}$ is applied iteratively to potentially hundreds of temporal 'layers') and these will not be accurately approximated by $M_{\theta_n}$.
A damping function $R$ that can penalize directions in parameter space that despite not large in magnitude, can nontheless lead to large changes in the hidden-state sequence. The structural damping function:
$$
R_{\theta_n}(\theta) = \frac 1 2\|\delta_n\|^2+\mu S_{\theta_n}(\theta)
$$
where $\mu>0$ is a weighting constant and $S_{\theta_n}(\theta)$ is a function which quantifies change in the hidden units as a function of the change in parameters. With the inclusion of $\mu S_{\theta_n}(\theta)$, we hope that the quadratic model will tend to be more accurate at its optimum even for smaller values of $\lambda$.
Since $S$ is not generally quadratic in $\theta$, CG cannot be applied in $R$. We have to approximate $S$.
! MIT 1999 paper
A general framework for learning to solve two-factor tasks using bilinear models, which provide sufficiently expressive representations of factor interactions but can nontheless be fit to data using efficient algorithms based on the singular value decomposition and expectation-maximization.
!! Bilinear models
!!! Symmetric model
$$
y_k^{sc} = \sum_{i=1}^I\sum_{j=1}^Jw_{ijk}a_i^sb_j^c.
$$
$a_i^s$ and $b_j^c$ are style and content parameters, $y_k^{sc}$ is a $K$-dimensional observation vector in style $s$ and content class $c$.
!!! Asymmetric model
$$
a_{jk}^s = \sum_iw_{ijk}^sa_i^s,
$$
giving
$$
y_k^{sc} = \sum_{j}a_{jk}^sb_j^c.
$$
Style and content can be viewed as 2 hidden layers in a neural net and they are interconnected by weight $W_k$.
!! Model fitting
Both model can be solved by matrix factorization techniques.
In the experiment, asymmetric model is fitted with 4-dimensional style parameter selected by cross validation. And before a test of recognition, a separable mixture model with $S\times C$ gaussian components is trained with EM.
The content is predicted while the style matrix of test case is learned in EM. However, the model is tricky to solve. "Overfitting" behavior is often observed, where as EM iterates, test case is assigned to incorrect class and the likelihood grows. With 15 speakers, best precision can get is 74.6% not significantly better than 1-NN, 63.9%.
@article{sudderth2008describing,
title={Describing visual scenes using transformed objects and parts},
author={Sudderth, Erik B and Torralba, Antonio and Freeman, William T and Willsky, Alan S},
journal={International Journal of Computer Vision},
volume={77},
number={1-3},
pages={291--330},
year={2008},
publisher={Springer}
}
* [[Geometric Insights into Support Vector Machine Behavior using the KKT Conditions|http://arxiv.org/abs/1704.00767]] The naive Bayes classifier is equivalent to first standardizing the data then computing the mean difference. In the small $C$ regime soft margin SVM behaves like the MD classifier or a cropped version of the MD. In the large $C$ regime SVM is related to the maximal data piling direction. The dimensionality of the space and the class sizes have strong impacts on SVM's behavior.
[[link|https://arxiv.org/abs/1604.06646]]
! Backgroud
We call the image patches containing single character proposals. The pipelines generating char proposal are suboptimal. They combines genral purpose features such as HoG, EdgeBoxes and Aggregate Channel Features. The proposals are then classified with e.g. CNN as specific letters/words.
!! [[Object Detection With CNNs]]
As in R-CNN, [[Jaderberg et al.'s text spotting method|Reading Text in the Wild with CNN]] also uses a similar pipeline for detection.
The performance of the detection pipeline becomes the new bottleneck of text spotting: recognition accuracy for correctly cropped words is 98% whereas the end-to-end text spotting F-score is only 69% mainly due to incorrect and missed word region proposals. Also, they are slow and inelegant.
! Synthetic datasets
Synthetic datasets with character-level region labels are very suitable for training detectors. Since we don't care natural image that much
! Fully-Convolutional Regression Network
!! Region proposals
Let $x$ denote an image, the most common approach for CNN-based detection is to propose a number of image regions $R$ that may contain the target object, and use a CNN $c = \phi(crop_R(x)) \in \{0, 1\}$ to classify. This approach, popularized by R-CNN, is considerably slow since it evaluates the CNN thousands of times per image.
An alternative faster way is to construct a fixed field of predictors $(c, \mathbf p) = \phi_{uv}(x)$, each of which specialises in predicting the presence $c\in\mathbb R$ and pose $\mathbf p = (x-u, y-v, w, h)$ of an object around a specific image location $(u, v)$. Here the pose parameters $(x,y)$ and $(w, h)$ denote respectively the location and size of a bounding box tightly enclosing the object. This can be implemented by Implicit Shape Models (ISM) and Hough voting. There a predictor $\phi_{uv}$ looks at a local image patch, centered at $(u,v)$, and tries to predict whether there is an object around $(u, v)$, and where the object is located relative to it.
To create a net which performs prediction densely, at every image location. Makes prediction about a class label (text/not text) and the parameters of a bounding box enclosing the word cnetered at that location (idea borrowed from You Only Look Once (YOLO) of Redmon et al.).
The differences:
# ISM and Hough voting aggregate individual predictions across the image by voting while YOLO uses individual predictions directly. This can accelerate detections.
# Hough voting predictors $\phi_{uv}(x)$ are local and translation invariant, whereas YOLO pools evidence from the whole image and uses different functions at different locations.
!! FCRN
''Single-scale features.'' Structure similar to VGG is used, only smaller. 9 CONVs each followed by a ReLU, and occasionally by a POOL. Max pooling is performed over 2x2 windows with a stride of 2 samples.
With 4 downsampling layers, the stride of these dense features is 16px, each containing 512 channels $\phi_{uv}^f(x)$.
''Bounding box prediction.'' The dense text predictors $\phi_{uv}(x) = \phi_{uv}^r\circ\phi^f(x)$. Where $\phi_{uv}^r$ is a (C-7-5x5) linear filter, regressing the object presence confidence c, and up to six object pose parameters $\mathbf p = (x-u, y-v, w, h, \cos\theta, \sin\theta)$ where $\theta$ is the bounding box rotation.
To constrain one word per cell, a denser predictor may be needed.
''Multi-scale detection.'' To detect larger text, input image is scaled down by factors $\{1, 1/2, 1/4, 1/8\}$. The largest scoring proposal over all overlapped detections is selected.
''Training loss.'' squared loss for each output. If a cell does not contain a ground-truth, loss ignores all parameters but $c$.
''Comparing to YOLO.'' YOLO imposes strong spatial constraints on bounding box predictions since each grid cell only predicts two boxes and can only have one class. This spatial constraint limits the number of nearby objects that our model can predict. Our model struggles with small objects that appear in groups, such as flocks of birds.
YOLO has its 90% parameters in the last two fully-connected layers. YOLO retains image size. This makes YOLO harder to train and less efficient.
! Experiment
Trained with 800,000 images from SynthText in the Wild dataset. Net is optimized with SGD with batch-normalization after each convolutional layer except the last one. Batch size is 16, momentum 0.9, weight-decay $5^{-4}$. Initial learning rate $10^{-4}$ and reduced to $10^{-5}$ when the training loss plateaus.
As only a small number of grid-cells contain text, non-text probability error term is weighed down by multiplying 0.01 at the begining, and gradually increased to 1. Or all result collapse to zero due to class imbalance.
The result is best on IC3 with high-recall profile. Not as good as [[Jaderberg 2015|Reading Text in the Wild with CNN]] on SVT but is 45 times faster.
! DNI * [[Decoupled Neural Interfaces using Synthetic Gradients|https://arxiv.org/abs/1608.05343]] ** We can create a model of error gradients using local information ** The result is Layer 1 can now update before the execution of Layer 2. ** Analogous to return prediction bootstrapping in RL (TD learing/Actor-Critic): 'Learn a guess from a guess' * [[Understanding Synthetic Gradients and Decoupled Neural Interfaces|https://arxiv.org/abs/1703.00522]] ** Synthetic gradients don't change the critical points ** Convergence proof: for very simple networks ** Difference of networks: ! Multi Network Two RNNs tick at different clock speeds. Don't have to train synchronized.
! Windows add permanent static route ``` route add -p 10.0.0.0 mask 255.0.0.0 192.168.0.1 ```
! Introduction
* PCA tries to perserve large pairwise distance, which is not a good idea for visualization. For instance, 2 distant points along a swiss roll manifold can be near in Euclidean space.
* Isomap tries to estimate the distance over the manifold.
* Locally linear embedding is similar with t-sne in the sense that they both try to preserve short distances within local structure.
T-sne is a very popular method to generate image feature 2-d visualization.
! Basic
The input of t-SNE consists of a collection of pairwise (Euclidean) distance $\delta_{ij}$ between the $n$ input objects, that are converted into conditional probabilities as $p_{j|i}=\frac{\exp(-\delta_{ij}^2/2\sigma_i)}{\sum_{k\ne i}\exp(-\delta_{ij}^2/2\sigma_i)}$, and which joint to obtain a joint probability matrix entries $p_{ij}=\frac{p_{j|i}+p_{i|j}}{2n}$ (for symmetry). @@color:#859900;$\sigma$ varies per object and are set such that the conditional has fixed perplexity.@@
The embedding similarity $Q$ is modeled by a normalized Student-t kernel with 1 DOF, which is very more heavy-tailed, making dissimilar points can be far enough in the embedding.
Locations are determined by minimizing $KL(P\|Q)$. Due to the asymmetry of the Kullback-Leibler divergence, the objective function focuses on modeling high values of $p_{ij}$ (similar objects) by high values of $q_{ij}$ (nearby points in the embedding space). This is typically solved by SGD:
$$
\frac{\partial C}{\partial\mathbf y_i} = 4\sum_{j\ne i}(p_{ij}-q_{ij})(1+\|\mathbf y_i - \mathbf y_j\|^2)^{-1}(\mathbf y_i - \mathbf y_j)
$$
! Tree-Based Algorithms
We can approximate the distribution with the $\lfloor 3u\rfloor$ neighbors.
!! Barnes-Hut approximation
Computing the attractive forces are easier. We can approximate repulsive forces by the intuition that many of the pairwise interactions are very similar. Barnes-Hut approximation is first used by astrophysicists to compute the interaction between stars. If a ceil in quadtree:
# is small enough
# sufficiently far away from point $\mathbf y_i$
! By Conferences * [[ICML 2015]] * [[KDD 2016]] ! TODO List # [[KDD 2016]] # Shen: A sufficiently advanced Lisp # [[SAHD]] !! Others * [[Towards bridging the gap between deep learning and biology|Deep learning in biology]]
! Players Game changers past 10yrs * Joe Maddon * Andrew Friedman * Alex Cobb: rising to be starting pitcher
[[Google Research Blog|https://research.googleblog.com/2017/09/build-your-own-machine-learning.html]] Interesting demos * [[Beholder|https://github.com/chrisranderson/beholder]]: A TensorBoard plugin for visualizing arbitrary tensors in a video as your network trains.
! Practices [[CS224D 2016|Stanford NLP Course]] has a decent tf tutorial. ! Features * [[TensorBoard]] * TensorFlow Serving * [[TensorFlow Fold|Deep Learning with Dynamic Computation Graphs]]: Parallel dynamic batching ! [[Projects|TensorFlow Projects]] ! [[TensorFlow Workarounds]] ! Tools * [[tfprof|https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/tfprof]]: A Profiling Tool for TensorFlow Models
!! Session object
<<<
Session object encapsulates the environment in which Tensor objects are evaluated.
<<< TensorFlow Docs
```python
c = a * b
with tf.Session() as sess:
print(sess.run(c))
print(c.eval()) # c.eval() is just syntatic sugar for sess.run(c) in the currently active session
```
* `c` is symbolic value until we actually evaluate it.
* `tf.InteractiveSession()` is useful for debugging
* `sess.run(c)` is an example of a TensorFlow ''Fetch''. First data are ''Fed'' into the computation graph and 'Fetch' take the result out.
!! TensorFlow computation graph
<<<
TensorFlow programs are usually structured into a construction phase, that assembles a graph, and an execution phase that uses a session to execute ops in the graph.
All computations add nodes to global default graph.
<<< TensorFlow Docs
Functions exist in global graph memory.
!! TensorFlow variables
<<<
When you train a model you use variables to hold and update parameters. Variables are in-memory buffers containing tensors.
<<< TensorFlow Docs
```python
W = tf.Variable(tf.zeros((2,2)), name="weights")
tf.initialize_all_variabes().eval() # explicit initialization.
W.eval()
```
Variable tensors always have to be intitialized.
```python
state = tf.Variable(0, name="counter")
new_value = tf.add(state, tf.constant(1)) # make a new value
update = tf.assign(state, new_value) # assign the value to var
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for _ in range(3):
sess.run(update)
print(sess.run(state))
```
The name of variables is like an index allowing other programs to communicate with the graph.
!! Inputting data
We can import tensors from numpy.
```python
a = np.zeros((3,3))
ta = tf.convert_to_tensor(a)
```
The above method does not scale. `tf.placeholder` vars are dummy nodes where we can put data in. And `feed_dict` is a python dictionary mapping from `tf.placeholder` vars to data.
```python
In [96]: input1 = tf.placeholder(tf.float32)
In [97]: input2 = tf.placeholder(tf.float32)
In [98]: output = tf.mul(input1, input2)
In [99]: with tf.Session() as sess:
....: print(sess.run([output], feed_dict={input1:[7.], input2:[2.]}))
....:
[array([ 14.], dtype=float32)]
```
!! Variable scope
* `tf.variable_scope()` provides name-spacing
* `tf.get_variable()` creates/accesses variables from within a variable scope.
```python
with tf.variable_scope("foo"):
with tf.variable_scope("bar"):
v = tf.get_variable("v", [1])
assert v.name == "foo/bar/v:0"
```
`reuse_variables()` is useful to implement weight sharing in RNNs:
```python
with tf.variable_scope("foo"):
v = tf.get_variable("v", [1])
tf.get_variable_scope().reuse_variables() # setting reuse=True in the scope
v1 = tf.get_variable("v", [1])
assert v1 == v
```
!! Gradient Computation
When people define a graph, a gradient operation is attached to the node. Backprop computes gradients for all variables in graph.
! Tutorials [[MNIST multiclass|https://www.tensorflow.org/versions/r0.8/tutorials/mnist/beginners/index.html]] ! Applications * [[cnn-text-classification]]
! Version 0.80r is not compatible with skimage * workaround: import skimage before tf. * clean solution: not supported yet. ! GPU1 not visible * workaround: run as `CUDA_VISIBLE_DEVICES=gpuid python myscript.py` * clean solution: maybe graph level specification works but not perceived. ! Imbalanced class * workarounds: ** increase batch size ** oversampling * should also try: ** advanced sampling methods (e.g. [[SMOTE|http://arxiv.org/abs/1106.1813]]) ** modify loss fun ```python z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x)) = x - x * z + log(1 + exp(-x)) cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(_pred) + (1-y)*tf.log(1-_pred), reduction_indices=1)) ```
* [[cnn-text-classification]]
[[blog|http://www.inference.vc/my-notes-on-the-numerics-of-gans/]]
Simultaneous gradient descent is a generalization, rather than a special case, of gradient descent.
Iff a vector field $v$ is conservative, is there a scalar potential $phi$ whose gradient equals $v$. A vector field defined by an AE is not necessarily conservative.
The solution proposed by Mescheder et al is to construct a conervative vector field from the original as follows: $-\nabla L(x):=-\frac{\partial}{\partial x}\|v(x)\|^2_2$. This new vector field $\-\nabla L$ has the same fix points as $v$.
<<< ''Theorem'' [name]<br> Descriptions <<<
<<< ''Theorem'' [name]<br> Descriptions <<<
* Fixed-parameter algorithms ** [[Open questions for scheduling|https://arxiv.org/abs/1709.01670]]
To predict stock values and other economic variables Google Trends: interest over time. [[ARMA]] * Many other generative models exist * Learning guarantees are asymptotic * Model needs to be correctly specified * Non-stationarity needs to be modeled explicitly. [[Generalization Bounds for Non-stationary Mixing Processes]]
$$
Ax=b \qquad A\succ0\qquad\kappa=K(A)=\frac{\lambda_{\text{max}}(A)}{\lambda_{\text{min}}(A)}
$$
Taylor expansion
$$
A^{-1}=\frac{1}{\lambda_{\text{max}}}(\frac{A}{\lambda_{\text{max}}})^{-1} = \frac{1}{\lambda_{\text{max}}}(I-(I-\frac{A}{\lambda_{\text{max}}}))^{-1}=\sum_{k=0}^{\infty}(I-\frac{A}{\lambda_{\text{max}}})^k\frac{1}{\lambda_{\text{max}}}
$$
<<<
''Theorem'' [name]<br>
$$
\|p_n(A)b-A^{-1}b\|\le\epsilon\|b\|\qquad\ni\qquad n\ge\Omega(\kappa\log\frac1\epsilon)
$$
<<<
By acceleration, we mean to find a tighter bound such as $\Omega(\sqrt\kappa\log\frac1\epsilon)$
```
@phdthesis{sutskever2013training,
title={Training recurrent neural networks},
author={Sutskever, Ilya},
year={2013},
school={University of Toronto}
}
```
A modern theory of why RNNs fail to learn long-term dependencies because simple gradient descent fails to optimize them correctly. One attempt to mitigate this problem is Hessian Free (HF) optimization
Hessian Free
(Stocahstic) Gradient Descent
Momentum methods (Hinton, Nesterov)
Initialization is important in DNNs. Is pretraining important or not?
! Background
!! RNN Algorithms
!!! Echo-State Networks
The Echo-State Network (ESN; Jaeger and Haas, 2004) is a standard RNN that is trained with the ESN
training method, which learns neither the input-to-hidden nor the hidden-to-hidden connections, but sets them to ''draws from a well-chosen distribution'', and only uses the training data to learn the hidden-to-output connections.
Unlike random projections or convolutional neural networks with random weights, RNNs are highly sensitive to the scale of the random recurrent weight matrix, which is a consequence of the ''exponential relationship between the scale and the evolution of the hidden states''. When the recurrent connections are sparse and are scaled so that their spectral radius is slightly less than 1, the hidden state sequence remembers its inputs for a limited but nontrivial #timesteps while applying many random transformations to it.
Despite its impressive performance on the synthetic problems from Martens and Sutskever (2011),
the ESN has a number of limitations. Its capacity is limited because ''its recurrent connections are not learned'', so it cannot solve data-intensive problems where high-performing models must have millions of parameters. And the size of high-performing ESNs grows very quickly with the information that the hidden state needs to carry. This explanation is consistent with the performance characteristics of random convolutional networks, which excel only when the number of labelled cases is very small, so systems that adapt all the parameters of the neural network lose because of overfitting.
!!! Mapping Long Sequences to Short Sequences
Connectionist Sequence Classification
!!! Truncated Backpropagation Through Time
Truncated BPTT processes the sequence one timestep at a time, and every $k_1$ timesteps, it runs BPTT for $k_2$ timesteps, so a parameter update can be cheap if $k_2$ is small. Consequently, its hidden states have been exposed to many timesteps and so may contain useful information about the far past, which would be opportunistically exploited.
! Training RNNs with Hessian-Free Optimization
RNNs trained by HF can significantly outperform similarly sized LSTMs.
!! [[Hessian-Free Optimization]]
! Language Modelling with RNNs
!! Tensor RNN
$$
h_t = \tanh(W_{hv}v_t+W_{hh}^{(v_t)}h_{t-1}+b_h)
$$
each character specifies a different hidden-to-hidden weight matrix. $W_{hh}^{(v_t)}$ can be defined with a tensor
$$
W_{hh}^{(v_t)} = \sum_{m=1}^Mv_t^{(m)}W_{hh}^{(m)}
$$
!! Multiplicative RNN
The storage of $W_{hh}^{(m)}$ can be prohibitive. $W_{hh}^{(v)}$ can be factored with
$$
W_{hh}^{(v_t)} = W_{hf}\text{diag}(W_{fv}v_t)W_{fh}
$$
! Things to Do
Compare SGD and second order methods for RNN, CW-RNN and LSTM.
Second order algorithms to try:
* HF
* BP of diagonal Hessian
Plot the Eigenvalue order of the Hessian.
* On [[GoogLeNet]] ** Moving its POOLs outside of Inception module, do it in traditional way ** What about adding another layer of Inception with no POOL? * Use handcrafted Gabor filters (or some wavelet filters) instead of first layer CONVs, which takes much space in optimization
! First Impression Like it very much, customizable and beautiful! ! About TiddlyWiki !! It is a Quine And very interestingly, it is a practical Quine. Wikipedia [[defines a Quine|http://en.wikipedia.org/wiki/Quine_(computing)]] as //a computer program which takes no input and produces a copy of its own source code as its only output//. TiddlyWiki is an unusual example of a practical quine: it is this ability to produce a copy of its own source code that lies at the heart of TiddlyWiki's ability to independently save changes to itself. ! Plugins * [[MathJax Plugin]] * [[Highlight]] * [[CodeMirror]]
! Problem
!! Time series forecasting
''Training data'': finite sample realization of some stochastic process
$$
(X_1, Y_1), \dots, (X_T, Y_T)\in \mathcal Z = \mathcal X\times\mathcal Y.
$$
''Loss function'': $L:H\times\mathcal Z\rightarrow[0, 1]$, where $H$ is a hypothesis set of functions mapping from $\mathcal X$ to $\mathcal Y$. The loss function is bounded by 1.
''Problem'': find $h\in H$ with small path-dependent expected loss
$$
\mathcal L(h, \mathbf Z_1^T) = \underset{Z_{T+1}}{\mathbb E}[L(h, Z_{T+1})|\mathbf Z_1^T].
$$
!! Standard Assumptions
* Stationarity
* Mixing: the dependency between 2 events decays with their gap.
** beta, alpha, etc. mixing
! Models
[[ARMA]]
! Papers
* [[Generalization Bounds for Non-stationary Mixing Processes]]
! Talks
NIPS 2016 Tutorial: [[Theory and Algorithms for Forecasting Non-Stationary Time Series]]
* [[link|https://arxiv.org/abs/1605.06336]] * [[blog|http://www.inference.vc/temporal-contrastive-learning-for-latent-variable-models/]] The activations in the last hidden layer will learn to represent something fundamental, the log-odds-ratios in a probabilistic generative model. The model relies on a not very practical assumption about the time series data. It's not hard to see that TCL is analogous to a temporal version of the [[jigsaw puzzle method|http://www.inference.vc/notes-on-unsupervised-learning-of-visual-representations-by-solving-jigsaw-puzzles/]]. This is yet another example of using logistic regression as a proxy to estimating log-probability-ratios directly from data. This insight provides new ways in which unsupervised or semi-supervised tasks can be reduced to supervised learning problems. As classification is now considered significantly easier than density estimation, direct probability ratio estimation may provide the easiest path forward for representation learning.
! Implementations
[[TIMIT HF]]
! Recent Results
Best TIMIT result in the literature: [[Transducer LSTM]]
TIMIT Results with End-To-End Training
|Training Method |Dev PER |Test PER |
|CTC |19.05+-0.11|21.57+-0.25|
|CTC (Noise) |16.34+-0.07|18.63+-0.16|
|Transducer |15.97+-0.28|18.07+-0.24|
Both networks consisted of five bidirectional hidden levels, each containing 2 LSTM layers of 250 cells, along with a size 62 softmax output layer (one unit for each phoneme, plus an extra blank unit).
* CTC: Connectionist Temporal Classification (6.8M weights). The CTC were first trained to convergence with no noise, then retrained with weight noise (std 0.075).
* Transducer: Sequence Transduction (7.4M weights) with an additional phoneme prediction network with a hidden layer of 250 LSTM cells, and an output network with a single hidden layer of 250 tanh units. Single best transducer run is 17.7.
All networks were trained using SGD, with learning rate $10^{-4}$, momentum 0.9 and random initial weights drawn uniformly from $[-0.1,0.1]$.
TIMIT Results with [[Hybrid Training|Hybrid LSTM]]i
|Network |Dev PER |Test PER |Dev FER |Test FER |Dev CE |Test CE |
|DBRNN |19.91+-0.22|21.92+-0.35|30.82+-0.31|31.91+-0.47|1.07+-0.010|1.12+-0.014|
|DBLSTM |17.44+-0.16|19.34+-0.15|28.43+-0.14|29.55+-0.31|0.93+-0.011|0.98+-0.019|
|DBLSTM (Noise) |16.11+-0.15|17.99+-0.13|26.64+-0.08|27.88+-0.16|0.88+-0.008|0.93+-0.004|
! Kaldi
!! Preprocessing
# Data preparation (`data_prep.sh`)
#* converting sphere files
#* and lexicon in text to fst format
#* generating utterance to speaker maps
#* transcriptions to symbol table ints
# Following steps
#* `prepare_graphs.sh`
#* symbol tables for words and phones
#* binary format FSTs: data/{G.fst, L.fst, L_disambig.fst}
#* files indicating silences
# Raw MFCC features
#* differ script (scp) and archive (ark) files (Table concept: specifiers)
# [[HMM Alignments]] Monophone training
#* `$feats` treated as an rspecifier
#* creates `$dir/topo` which specifies phone topologies
#* Initialization
#* creates decoding graphs
# Triphone training
!! Karel's setting
it relies on CUDA
!!! Original
Karel works on fMLLR and gmms.
# RBM pretraining [[geoff's tutorial|http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf]]
# frame cross-entropy
# sequence-training optimizing sMBR
!!! LSTM
Use fbank features.
# For dev & training set
## copy data
## make fbank
## compute cmvn
# split the training set with `subset_data_dir_tr_cv.sh`
```
# Train
$cuda_cmd $dir/log/train_nnet.log \
steps/nnet/train.sh --network-type lstm --learn-rate 0.0001 \
--cmvn-opts "--norm-means=true --norm-vars=true" --feat-type plain --splice 0 \
--train-opts "--momentum 0.9 --halving-factor 0.5" \
--train-tool "nnet-train-lstm-streams --num-stream=4 --targets-delay=5" \
--proto-opts "--num-cells 512 --num-recurrent 200 --num-layers 2 --clip-gradient 50.0" \
${train}_tr90 ${train}_cv10 data/lang $ali $ali $dir || exit 1;
```
!!! Multitask
```
$cuda_cmd $dir/log/train_nnet.log \
steps/nnet/train.sh \
--cmvn-opts "--norm-means=true --norm-vars=true" \
--delta-opts "--delta-order=2" --splice 5 \
--labels "scp:$dir/pasted_post.scp" --num-tgt $output_dim \
--proto-opts "--block-softmax-dims='$ali1_dim:$ali2_dim'" \
--learn-rate 0.008 \
${train_tr90_wsj} ${train}_cv10 lang-dummy ali-dummy ali-dummy $dir || exit 1;
```
| |lstm |multitask |
|network-type |lstm | |
|learn-rate |0.0001 |0.008 |
|cmvn-opts |norm-means/vars| |
|feat-type |plain | |
|delta-otps | |order =2 |
|splice |0 |5 |
|train-opts |momentum 0.9 halving-factor 0.5| |
|train-tool |nnet-train-lstm-streams num-stream=4 target-delay=5| |
|labels | |pasted_post.scp|
|num-tgt | |$output_dim|
|proto-opts |num-cells 512 num-recurrent 200 num-layers 2 clip-gradient 50.0|block-softmax-dims|
! Demo Comments
Initialization is important
RNNs can be used in phoneme classification but the final speech recognition system has to incorparate HMMs.
[[cheatsheet|https://gist.github.com/MohamedAlaa/2961058]] |List sessions|`tmux ls`| |Kill session|`tmux kill-session -t ID`|
! Stream of Consciousness # Here is a very interesting visual attention idea ## conventional projections scale features and lose details ## in frequency domain we can compress information without lossing resolution of certain part of the image. ## mult is implemented with FFT, so probably frequency domain alright? ## scattering representation, dilated convolution, draw inspirations... ### dilated convolution preserve resolution, which is contrary to what I am trying to do ## differentiable attention location # The ensemble nature of resnet may encourage refined SGD to modulate the impacts of different paths. # Experiment on Cross-convolutional-layer pooling, similar idea as conv-attention # Currently the biggest challenge of VQA is numbering problems. Probably this is related to detecting a cluster of similar objects (e.g. character detection). # NLP ## Compute word vectors from definition? ## A writing style transfer network # Optimization ## Batchsize affects saddle points? Smaller learning rate easier to escape? ## Backpropagation, good or evil? ! Experiments # [[Deep generative model tryouts]] # Try visual attentions: Juana is doing it. # Object detection ## Look for decent RFCN/Faster-RCNN/SSD implemenations, better in same framework. Google should include them in tensorflow/models later. ## [[CTPN|Connectionist Text Proposal Network]] implementation out, add character segmentation somehow. ! Side projects # [[Daily News for Stock Market Prediction]] ## Data not very valuable # Kaggle fish ## Wait for mxnet problem to be solved at [[Kaggle|https://www.kaggle.com/drn01z3/the-nature-conservancy-fisheries-monitoring/mxnet-xgboost-simple-solution/comments]] ! Reading List # Videos in [[Most Cited Deep Learning Papers|https://github.com/terryum/awesome-deep-learning-papers#recurrent-neural-network-models]] # [[Averaging and Wavelets|Understanding Deep ConvNets (Scattering Repr)]]: Continue UBC deep learning topic lecture 3 on wavelets. # NIPS videos # [[Foundations of Machine Learning Boot Camp|https://simons.berkeley.edu/workshops/machinelearning2017-boot-camp]] # [[DALI 2017 GAN Workshop]], also, the data efficient reinforcement learning workshop
* [[Editor Shortcuts]] * [[Arch Utils]] * [[Org-mode]] * [[WikiText]] * [[Spacemacs]] * [[Awesome WM]] * [[bug.n]] * [[tmux]] * [[LaTeX]]
* [[arxiv|https://arxiv.org/abs/1706.02379]] * [[ICML|http://vip.ece.cornell.edu/papers/17ICML_WS_qnets.pdf]]
Transducers promise to separate the element transformation from the actual traversal of the input data. This allows the very same transducer pipeline to be used in `reduce` (resp. the transducer specific variant `transduce`) and at the same time apply the exact same transformation logic to asynchronous channels as provided by `core.async`.
Just as well we could create a traditional lazy sequence from the transducer. The promise is to provide the laziness we know from sequences, but save the cons cells created in each step of the transformation pipeline. This is more efficient and reduces the load on the garbage collector.
Do transducer deliver on that promise?
Following is an example of a transducer:
```clojure
(defn neighbourhood
[n]
(list (dec n) n (inc n)))
(def example-t
(comp
(mapcat neighbourhood)
(map str))
```
```
@inproceedings{graves2013speech,
title={Speech recognition with deep recurrent neural networks},
author={Graves, Alan and Mohamed, Abdel-rahman and Hinton, Geoffrey},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on},
pages={6645--6649},
year={2013},
organization={IEEE}
}
```
! Experiments
|Training Method |Dev PER |Test PER |
|CTC |19.05+-0.11|21.57+-0.25|
|CTC (Noise) |16.34+-0.07|18.63+-0.16|
|Transducer |15.97+-0.28|18.07+-0.24|
Both networks consisted of five bidirectional hidden levels, each containing 2 LSTM layers of 250 cells, along with a size 62 softmax output layer (one unit for each phoneme, plus an extra blank unit).
* CTC: Connectionist Temporal Classification (6.8M weights). The CTC were first trained to convergence with no noise, then retrained with weight noise (std 0.075).
* Transducer: Sequence Transduction (7.4M weights) with an additional phoneme prediction network with a hidden layer of 250 LSTM cells, and an output network with a single hidden layer of 250 tanh units. Single best transducer run is 17.7.
All networks were trained using SGD, with learning rate $10^{-4}$, momentum 0.9 and random initial weights drawn uniformly from $[-0.1,0.1]$.
!! abstract
Allows for components to share perturbed copies of atoms.
! Details
convert 128-dimensional SIFT descriptor to a vector quantized discrete value.
The goal is to allow the same global cluster to describe objects at many different locations. To accomplish this, we augment topic models with transformation variables, thereby ''shifting'' global clusters from a "canonical" coordinate frame to the object positions underlying a particular image.
Let $\tau(\theta;\rho)$ denote a family of transformations of the parameter vector $\theta$, indexed by $\rho$
! Hierarchical Models
!! Fixed-Order Object Model
Fixed number of shared parts. LDA like model
* Unimodal Gaussian ''transformation priors'' $\rho_i$. Analogously to in-between-consonants (scale to uniform length).
* Mixture component $z_j$
* Multinomial appearance descriptors $W$
* Object transform displacement Gaussian $v$
<<<
Marginalizing the unobserved assignments $z_{ij}$ of features to parts, we find that the graph defines object appearance via a finite mixture model:
$$
p(w_{ji},v_{ji}|\rho_j, o_j=l)=\sum_{k=1}^K\pi_{lk}\eta_k(w_{ji})N(v_{ji}; \tau(\mu_k,\Lambda_k;\rho_j))
$$
Parts are thus latent variables which capture dependencies in feature location and appearance, while ''reference transformations'' allow a common set of parts to model unaligned images. Removing these transformations, we recover a variant of the author-topic model, where objects correspond to authors, features to words, and parts to the latent topics underlying a given text corpus.
<<<
How to marginalize reference transformation $\rho$?
!! Nonparametric Object Model
[[HDP|Dirichlet Process Mixtures]] to learn the number of shared parts (topics) underlying a set of object categories. The more samples, the more classes.
Let $H_w$ denote a Dirichlet prior on feature appearance distributions, $H_v$ a normal-inverse-Wishart prior on feature position distributions, and $H_w\times H_v$ the corresponding product measure. A global probability measure $G_0\sim DP(\gamma, H_w\times H_v)$ is first used to define an infinite set of shared parts:
$$
G_0(\theta)=\sum_{k=1}^\infty \beta_k\delta(\theta,\theta_k) \qquad \beta\sim GEM(\gamma) \qquad (\eta_k, \mu_k, \Lambda_k) = \theta_k\sim H_w\times H_v
$$
<<<
This generalizes the parametric model by defining an infinite set of potential global parts, and using the Dirichlet process's stick-breaking prior to automatically choose an ''appropriate model order''. It also extends the original HDP by associating a different reference transformation with each ''training image''.
<<<
!! Fixed-Order Scene Model
Learn the contextual relationship. (by transformations?)
! Sampling
Algorithm 1: Consider each training image $j$ in turn and sequential resampling of assignments $t_j$ of features to tables (category-specific copies of global parts) in the $j$th training image
$$
t_{ji}\sim\sum_{t=1}^{T_l}N_{lt}f_{k_{lt}}(w_{ji}, v_{ji})\delta(t_{ji},t)+\frac{\alpha}{\gamma+\sum_kM_k}\left[\sum_{k=1}^KM_kf_l(w_{ji},v_{ji}+\gamma f_{\bar k}(w_{ji}, v_{ji})\right]\delta(t_{ji},\bar t)
$$
and the image's coordinate frame $\rho_j$ with an auxiliary variable method.
<<<
Rao-Blackwellization
<<<
Algorithm 2: examine each object category $l$, and sequential resample all samples assigments $\mathbf k_l$ of local tables (category-specific parts) to global parts for the $l$th object category, as well as the HDP concentration parameters.
[[link|https://joanbruna.github.io/stat212b/]] ! Lec1 We can take deep learing as <<< A class of parametrized non-linear representations encoding appropriate domain knowledge (invariance and stationarity) that can be (massively) optimized efficiently using stochastic gradient descent <<< CNN is able to capture high level image properties more efficiently than previous models. How can we explain these advantage? * model architecture? non-linearity/convolution/depth/invariance * optimization? normalization/no local minima/stochastic optimization/equivalent local solutions [[Deep Learning Theory]] ! Lec2 [[Representation Learning]] ! Lec3 Groups, Invariants and Filters [[Understanding Deep ConvNets (Scattering Repr)]] ! Lec4 Scattering Convolutional Networks Why are wavelets a good
[[paper|http://web.stanford.edu/~cdesa/papers/isca_buckwild_submission.pdf]] Proof of low precision doesn't harm training. Things that matter: * rounding strategy * precision * sparsity of input basic facts: * BUCKWILD! is based on HOGWILD!. * The conversion in the AXPY operation decreases the number of bits used to represent the number and introduces round-off error. ! DMGC model Numbers used by a parallel SGD can be separated into: * data, quantized once and saved in DRAM. dataset precision, index precision for sparse data * model, always stored in the last-level of cache. Quantize after AXPY, standard/unbiased rounding. * gradient, * communicate
[[link|https://arxiv.org/abs/1601.04920]]
! [[Image Representation Learning]]
! Averaging and Wavelets
[[Fourier transform|Fourier Transform vs Wavelet Transform]] are unstable in high-frequency. We apply a local translation invariance:
$$
\|\Phi(x)-\|\Phi(\varphi_vx)\|\le C2^{-J}\|v\|
$$
we can do a local averaging within the translation orbit with $\phi(v)$:
$$
\Phi(x)=2^{-dJ}\int_v\phi(2^{-J}v)\varphi_vxdv
$$
In coordinates, it becomes
$$
\Phi(x)(u)=\int\phi_J(v)v(u-v)dv=x\ast\phi_J(u)
$$
with
$$
\phi_J(v)=2^{-Jd}\phi(2^{-J}v)
$$
But the low-pass information is insufficient. We want to capture high-frequency with a measure involving a non-linearity. We want them to preserve stability to deformations and inter-class variability.
Have to understand why wavelets are a good idea. [[Think about 2|TODOs]]
! Definitions
; Separation
: If we can write $f(x)=f_0(\Phi(x))$ where $\Phi(x)$.
This is always used to reduce the dimension of the data. We further impose the separation is Lipschitz:
$$
\exists\epsilon>0 \forall(x, x')\in\Omega^2, \epsilon\|f(x) - f(x')\| \leq \|\Phi(x) - \Phi(x')\|.
$$ which implies $f_0$ is Lipschitz continuous.
; Linearization
: On the other hand, we can linearize the variations of $f$ by projecting the data into a much larger space of dim $d'$. $\Phi(x)=\{\phi_k(x)\}_{k\leq d'}$. We say that $\Phi$ separates $f$ linearly if $f(x)$ is well approximated by a one-dimensional projection:
$$
\tilde f(x) = \langle\Phi(x), w\rangle=\sum_{k=1}^{d'}w_k\phi_k(x).
$$
; Local symmetries
: We suppose there is a metric $|g|_G$ which measures the distance between $g\in G$ and the identity. A function $f$ is locally invariant to the action of G if
$$
\forall x\in \Omega, \exists C_x>0, \forall g\in G \ni |g|_G<C_x, f(g.x)=f(x)
$$
; Translations
: The translation group $G=\mathbb R^n$ is an example of Lie group. The action of $g\in G=\mathbb R^n$ over $x\in\Omega$ is $g.x(u)=x(u-g)$.
; Diffeomorphisms
: A small diffeomorphism acting on $x(u)$ can be written as a translation of $u$ by a $g(u)$: $g.x(u) = x(u-g(u))$ with $g\in\mathbf C^{\mathbf 1}(\mathbb R^n)$.
; Averaging
: A linear operator can compute local invariants to the action of the translation group $G$, by averaging $x$ along the orbit $\{g.x\}_{g\in G}$. This is done with a convolution by an averaging kernel $\phi_J(u)=2^{-nJ}\phi(2^{-J}u)$ of size $2^J$, with $\int\phi(u)du=1$:
$$
\Phi_Jx(u)=x\star\phi_J(u)
$$
It is Lipschitz continuous to diffeomorphisms but eliminates the variation above the frequency $c2^{-J}$.
; Wavelet transform
: We define $K$ wavelets $\psi_k(u)$ for $u\in\mathbb R^n$. They are regular functions with a fast decay and zero average. These $K$ wavelets are dilated by $2^j$: $\psi_{j,k}(u)=2^{-jn}\psi_k(2^{-j}u$.
! Remarks
The set of convolution filters, which are wavelets, is overcomplete in order to be able to fully recover the original input signals. On the on hand, similar to ReLU, each individual activation within the scattering network only preserves partial information of the input. On the other hand, different to ReLU, scattering network activatio perserves the energy information, i.e. keeping only the modulus of the convolution responses and erasing the phase information; ReLU retains the phase information but eliminates the modulus information when the phase of a response is negative.
See [[Concatenated Rectified Linear Units]] for an alternative ReLU.
The main motivation behind the algorithm is to prevent the co-adaptation of feature detectors, or overfitting, by forcing neurons to be robust and rely on population behavior, rather than on the activity of other specific units. It is also noted that for a single logistic unit dropout performs a kind of "geometric averaging" (normalized weighted geometric mean) over the ensemble of possible subnetworks (proof in 3.1), and conjectured that something similar may occur also in multilayer networks.
! Definition
A one-parameter unitary group $\{\varphi_t\in Aut(\Omega)\}_{t\in\mathbb R}$ satisfies:
# $\forall t, s, \varphi_{s+t}=\varphi_t\varphi_s$,
# $\lim_{s\rightarrow t}\|\varphi_s-\varphi_t\| = 0$,
# $\forall t\in\mathbb R, x\in\Omega, \|\varphi_tx\|=\|x\|$.
In particular, these are Abelian groups
* Rotation and translations are $1$-parameter unitary groups
* Dilations can be made unitary: $\varphi_sx(u)=s^{1/2}x(su)$.
! Global Invariants
!! Stone's theorem
''Theorem:'' Suppose $\Omega$ is a Hilbert space. There is a one-to-one correspondence betweeen self-adjoint operators on $\Omega$ and one-parameter unitary groups of $Aut(\Omega)$. Given $\{\varphi_t\}_{t\in\mathbb R}$, there exists $A$ self-adjoint such that $\forall t$, $\varphi_t=e^{itA}$ ([[Matrix exponential]]). Conversely, if $A$ is self-adjoint, the family $\{e^{itA}\}_t$ is a one-parameter unitary group.
[[Proof|http://www2.maths.lth.se/media/thesis/2010/MATX01.pdf]]
!! Stone theorem, Fourier and Global Invariants
@@color:#859900;Translations are simultaneously diagonalized by Fourier atoms.@@
The Stone theorem formalizes the fact that a collection of “nice” commuting operators simultaneously diagonalizes (in a complex basis):
$$
A=V^*\text{diag}(\lambda_1, \dots, \lambda_n)V
$$
Unitary condition implies that eigenvalues are unitary complex numbers.
On larger Abelian (commuting) groups, we can factorize them into one-parameter groups (e.g. translations in R2)
$$
G=G_1\times G_2\times \dots G_l
$$
Thus $\Phi(x)=|Vx|$ is global invariant:
$$
\forall x, t, \Phi(\varphi_t(x))=\Phi(x).
$$
! Related works ''GANs'' * generator and discriminator nets * unstable to train this paper developed Deep Convolutional GAN A stable Deep Convolutional GAN should: * Replace any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator). * Use batchnorm in both the generator and the discriminator. * Remove fully connected hidden layers for deeper architectures. * Use ReLU activation in generator for all layers except for the output, which uses Tanh. * Use LeakyReLU activation in the discriminator for all layers. ! Implementations * [[tensorflow|https://github.com/carpedm20/DCGAN-tensorflow]]
This paper propses a biologically plausible way of modeling memory in RNNs. The authors claim short-term memory is not modeled well by hidden layer in classic RNN, so they add more layers to the recurrent unit.
TL;DR: Autoassociative memory accumulates the outerproduct of hidden state to form the memory.
The recurrent update:
$$
\mathbf h_{t+1} = \tanh(W\mathbf x+A_t\mathbf h_t+\mathbf b)
$$
<<<
Plastic mechanisms are more accurately modeled by a memory with $O(H^2)$ capacity than the $O(H)$ of standard recurrent artificial recurrent neural nets and LSTMs.
<<<
I think this structure is similar to a RNN with deep hidden layer and skip connections under the hood. The Hebbian-like rule for updating the fast weights $A(t)$ is like embedding a Hopfield network.
$$
A(t) = \lambda A(t-1)+\eta h(t)h(t)^\top
$$
We don't have to compute the fast weight matrix:
$$
A(t)h_s(t+1) = \eta\sum_{\tau=1}{\tau=t}\lambda^{t-\tau}h(\tau)[h(\tau)^\top h_s(t+1)]
$$
The $h(\tau)^\top h_s(t+1)$ part can be interpreted as attention. This auto associate rule from [[Hopfield Net]] is not memory efficient. It can only store $\log(N)$ number of the patterns. The fact that is is learned through backprop makes it more efficient in some way.
The decay rate is different from the original idea.
! Remarks
Although driven by associative memory, the rationale of Fast Weight was explained by attention mechanism, where $A_t\mathbf h_t$ is the attentive sum of $\mathbf h_t$ in history. The reason why Fast Weights could better store inputs than RNNs is explained by correspondence to Hopfield net and its biological intuition.
[[Learning to update Auto-associative Memory in Recurrent Neural Networks for Improving Sequence Memorization|https://scirate.com/arxiv/1709.06493]] change the update rule, specifically, the two hyperparameters $\lambda$ and $\eta$, into learnable weight matrices:
$$
A_t = W_A\odot A_{t-1} + W_h\odot \mathbf h_t \mathbf h_t^\top
$$
Using DNN to construct a global non-linear mapping relationship between the spectral envelopes of two speakers.
! Model
The DNN is trained by cascading 2 RBMs, which model the distributions of spectral envelopes of source and target speakers, using a Bernoulli BAM.
The model is proposed to convert the [[spectral envelopes|Spectral envelope]] directly instread of melcepstra.
! Learning
!! Training stage
Construct a generative model $\lambda^{(v)}$ to describe the distribution of the joint spectral space. The features of the joint space are ''concatenations'' of the time-aligned source and target spectral features, $v=[x, y]$.
!! Conversion stage
Maximum output probability parameter generation
$$
\tilde y^{(s)} = arg \underset{y^{(s)}}{\max}P(y|x,\lambda^{(v)})
$$
s.t. $y=My^{(s)}$. The constraint makes sure the observation sequences are fixed ''linear transformations'' of the static sequences (since the delta and delta-delta are computed by difference).
To eliminate variables in a factorized probability equation, repeat: # choose a variable to eliminate # push in its sum as far as possible # compute the sum, resulting in a new factor The time and space complexities of Variable Elimination are dominated by the size of the largest intermediate factor. Choosing the elimination ordering that minimizes this complexity is an NP-hard problem. Graphical models are powerful tools for visualizing the independence properties of complex probability models.
Some references
* [[Neural Variational Inference and Learning in Belief Networks|https://arxiv.org/abs/1402.0030]]
* [[Tutorial on Variational Autoencoders|https://arxiv.org/abs/1606.05908]]
* [[Auto-Encoding Variational Bayes|https://arxiv.org/abs/1312.6114]]
* [[Stochastic Backpropagation and Approximate Inference in Deep Generative Models|https://arxiv.org/abs/1401.4082]]
* [[Semi-Supervised Learning with Deep Generative Models|https://arxiv.org/abs/1406.5298]]
* [[Rényi Divergence Variational Inference|https://arxiv.org/abs/1602.02311]]
** This paper is definitely worth a read, and has a lot more to it than just deriving the bound. It proposes a practical algorithm VR-max, which uses $\alpha\rightarrow\infty$ and a smaller number of Monte Carlo samples. This method perfroms similarly to IWAE but requires fewer backpropagation iterations.
Nice models
* [[PixelVAE|https://arxiv.org/abs/1611.05013]]: not much perceptual gain but a good point to start.
Inference networks
* Amortized inference [Stuhlmuller et al., NIPS 2013]
* Inference networks, recognition networks [Kingma and Welling, 2014]
* Informed sampler [Jampani et al., 2014]
* Memory-based approach [Kulkarni et al., 2015]
! Background
''Directed graphical models'' rely on an ''ancestral sampling procedure'', which is appealing both for its conceptual and computational simplicity. They lack, however, the conditional independence structure of undirected models, making exact and approximate posterior inference on latent variables cumbersome [[Saul et al. 1996|https://arxiv.org/abs/cs/9603102]]. Recent advances in ''stochastic variational inference'' and ''amortized inference'', allowed efficient approximate inference and learning of deep directed graphical models by maximizing a variational lower bound on the log-likelihood. In particular, VAE simultaneously learns a generative network, that maps gaussian latent variables $z$ to samples $x$, and semantically meaningful features by exploiting the reparametrization trick.
We optimize the ELBO:
$$
\max_{w, \theta}\mathbb E_{z\sim q(z|x,w)}[\log p(x|z, \theta)]-D_{KL}(q(z|x, w)\|p(z))
$$
! Introduction
[[Reddit discussion|https://www.reddit.com/r/MachineLearning/comments/4r3pjy/variational_autoencoders_vae_vs_generative/]]
VAEs are a probabilistic graphical model whose ''explicit goal'' is latent modeling, and accounting for or marginalizing out certain variables (as in the semi-supervised work above) as part of the modeling process. They ''can'' make good generations (cf [[inverse autoregressive flow|https://arxiv.org/abs/1606.04934]], [[discriminative regularization|https://arxiv.org/abs/1602.03220]]) though they are ideal in settings where the latent is important (see [[variational fair autoencoder|https://arxiv.org/abs/1511.00830]]). The VAE naturally collapses most dimensions in the latent representations, and you generally get very interpretable dimensions out, the the training dynamics are generally a bit weird.
The ability to set [[complex priors|https://arxiv.org/abs/1605.06197]] in the latent is also nice especially in cases where you know something should make sense or you have a desired latent distribution. You can also do distributed latents (and priors) over time, as in the [[VRNN paper|https://arxiv.org/abs/1506.02216]] or fixed latents over a sequence as in VRAE, STORN, and Generating Sentences from a Continuous Latent Space. These all learn interesting and powerful latent representations for sequences, and can be combined with many existing models for sequence modeling.
! Model
Let $\bf x$ be a vector of $D$ observable variables, $\mathbf z\in \mathbb R^M$ a vector of stochastic latent units and let $p(\mathbf x, \mathbf z)$ be a parametric model of the joint distribution. Estimating the marginal likelihood $p(\mathbf x)$ could be intractable when the model is parametrized by a neural network. We can instead optimize the variational lower bound:
$$
\ln p(\mathbf x)\ge\mathbf E_{q(\mathbf z|\mathbf x)}[\ln p(\mathbf x|\mathbf z)]-\operatorname{KL}(q(\mathbf z|\mathbf x)\|p(\mathbf z)),
$$
where $p(\mathbf x|\mathbf z)$ is called a decoder and $p(\mathbf z)=\cal N(\mathbf z|\mathbf 0, \mathbf I)$ is the prior.
In practice, $q(\mathbf z|\mathbf x)$ is reparametrized:
$$
q(\mathbf z|\mathbf x) = \cal N(z|\mu(x), \operatorname{diag}(\sigma^2(x)))
$$
! Theory
The basic idea is [[Variational Inference]]. We wish to optimize $\theta$ such that we can sample $z$ from $P(z)$ and with high probability, $f(z;\theta)$ will be like the $X$'s in the dataset.
VAE assumes the latent factor $z$ that decides the properties of the image can be drawn from a simple distribution, i.e., $\mathcal N(0, I)$. Take this as a normalized random variable. The straightforward MC estimation $P(X)\approx\frac 1 n\sum_iP(X|z_i)$ is not feasible, because very similar samples are hard to genrate.
VAE attempts to sample values of $z$ from a new distribution $Q(z|X)$ that are likely to produce $X$. And compute $E_{z\sim Q}P(X|z)$. The relationship between $E_{z\sim Q}P(X|z)$ from $P(X)$ is one of the cornerstones of variational Bayesian methods.
Within SGD scheme, we only have to compute the gradient of
$$
\log P(X|z)-\mathcal D[Q(z|X)\|P(z)]
$$
We assume $Q(z|X)$ is a Gaussian of $X$, $\mathcal N(\mu(X), \Sigma(X))$.
To derive the variational autoencoder (VAE) from here you only need two more ingredients. Following borrowed from [[here|http://www.inference.vc/variational-renyi-lower-bound/]].
!! Amortised Inference
The bound above assumes that we observe a single sample, but usually we want to apply it on an entire dataset of i.i.d. samples. In this case, we need to introduce a separate auxiliary $q_n$ for each datapoint $x_n$, each with paramters $\psi_n$ that we need to optimise. This is feasible if the number of datapoints is small. In VAE we instead make the $q_n$s share parameters and make them depend on the datapoint $x_n$, basically $q_n(z)=q(z|x_n,\psi)$. This conditional auxiliary distribution is called the recognition model or inference network and can be used directly to perform approximate inference of latent variables given previously unseen data.
!! Monte Carlo and reparametrisation
Often, the variational lower bound can just be computed in closed form and optimized via gradient descent or via iterative methods. However, $\mathbb E_q$ involves an integral that is not always trivial to compute, and this is the case when we use a deep generative model as in VAE. Instead, we can sample from $q$ and approximate the lower-bound via Monte Carlo. This estimate will be unbiased, but have high variance if the number of samples is small. VAE uses a small reparametrisation trick to backpropagate through the sampling process and calculate derivatives with respect to $\theta$. Since Backprop does not flow through stochastic units, we perform a ''reparameterization trick'', sampling $z$ from $z = \mu(X) + \Sigma^{1/2}(X)\epsilon$, where $\epsilon\sim\mathcal(0, I)$.
! Remarks
[[From Variational auto-encoders do not train complex generative models|http://dustintran.com/blog/variational-auto-encoders-do-not-train-complex-generative-models]]
The neural network used in the encoder (variational distribution) does not lead to any richer approximating distribution. It is a way to amortize inference such that the number of parameters does not grow with the size of the data (an incredible feat, but not one for expressivity!) ([[Stuhlmüller et al., 2013|https://papers.nips.cc/paper/4966-learning-stochastic-inverses]]). This is better explained from the perspective of variational inference than auto-encoders. For example, suppose we have a model with latent variables that are Gaussian a priori (as in VAEs). We may choose a fully factorized Gaussian as the variational distribution, where each latent variable is rendered independent. The inference network takes data as input and outputs the local variational parameters relevant to each data point. The optimal inference network outputs the set of Gaussian parameters which maximizes the variational objective. This means a variational auto-encoder with a perfect inference network can only do as well as a fully factorized Gaussian with no inference network.
[[Much recent work has tried to improve this simple approximation to improve our approximate inference.|Variational Generative Models]]
! Bib
* [[Auto-Encoding Variational Bayes|https://arxiv.org/abs/1312.6114/]]
!! Applications
* [[Variational Deep Embedding: A Generative Approach to Clustering|https://arxiv.org/abs/1611.05148]]
** Hidden units conditioned on GMM as clustering assignment
* [[Deep Variational Inference Without Pixel-Wise Reconstruction|https://arxiv.org/abs/1611.05209]]
** Improve real NVP with VAE.
* [[A Deep Generative Framework for Paraphrase Generation|https://arxiv.org/abs/1709.05074]]
** A VAE-LSTM architecture should be able to generate more general text: refer to [[RVAE|https://github.com/kefirski/pytorch_RVAE]]
** How does it capture higher level features
[[A Theoretically Grounded Application of Dropout in Recurrent Neural Networks|https://arxiv.org/abs/1512.05287]]
! Bib * [[Variational Autoencoder]] * NICE [Dinh et al., 2015] * Hamiltonian variational inference [Salimans et al., 2015] * Stochastic Backpropagation and Approximate Inference in Deep Generative Models * Markov Chain Monte Carlo and Variational Inference: Bridging the Gap * Copula variational inference * The Variational Gaussian Process * Importance Weighted Autoencoders (IWAE) [Burda et al., 2016] * Hierarchical Variational Models * Auxiliary Deep Generative Models [Maaløe et al., 2016] * Variational inference for Monte Carlo objectives * [[Improving Variational Inference with Normalizing Flows]] [Rezende and Mohamed, 2016] ** Improving Variational Inference with Inverse Autoregressive Flow [Kingma et al., NIPS 2016] ** [[Improving Variational Auto-Encoders using Householder Flow|http://arxiv.org/abs/1611.09630]] [Tomczak and Welling, 2017] * [[Adversarial Variational Bayes]] (AVB) [Mescheder et al., 2017] ** Hierarchical Implicit Models [Tran et al., 2017] ** Variational Inference Using Implicit Distributions [Huszar, 2017] ** Adversarial Message Passing for Graphical Models [Karaletsos, 2016] ! Theory * [[Variational Inference for Generative Models]] ! Applications * [[End-to-end Optimized Image Compression]]
$$
\ln q_j^*(\mathbf Z_j) = \mathbb E_{i\ne j}[\ln p(\mathbf X, \mathbf Z)]+C
$$
Following borrowed from [[here|http://www.inference.vc/variational-renyi-lower-bound/]].
! Why do we need variational lower bounds?
One way to define a probabilistic generative model for some variable $x$ is via latent variables: We introduce hidden variables $z$ and define the joint distribution between $x$ and $z$. In such a model, typically:
* $p(z)$ is very easy 🐣,
* $p(x|z)$ is easy 🐹,
* $p(x,z)$ is easy 🐨,
* $p(x)$ is super-hard 🦂,
* $p(z|x)$ is mega-hard 🐉
to evaluate.
Unfortunately, in machine learning the things we need to calculate are exactly the bad guys, 🦂 and 🐉:
* inference is evaluating $p(z|x)$
* learning (via maximum likelihood) involves $p(x)$ or at least its gradients
Variational lower bounds give us ways to approximately perform both inference and maximum likelihood parameter learning.
! Standard variational (VI) lower bound
$$
p(x|\theta) = \int p(x|z, \theta)p(z)dz
$$
Maximizing this term is intractable, so we do @@color:#859900;per-instance@@ variational approximation.
$$
\begin{align}
\log p(x|\theta) &= \log\int p(x|z, \theta)p(z)dz\\
&= \log\int p(x|z, \theta)\frac{q(z)}{q(z)}p(z)dz\\
&= \log\int p(x|z, \theta)\frac{p(z)}{q(z)}q(z)dz\\
&= \log\mathbb E_{z\sim q(z)}[p(x|z, \theta)\frac{p(z)}{q(z)}]\\
&\ge \mathbb E_{z\sim q(z)}[\log p(x|z, \theta)\frac{p(z)}{q(z)}]\\
&= \mathbb E_{z\sim q(z)}[\log p(x|z, \theta)]-D_{KL}(q(z)\|p(z))
\end{align}
$$
To nice auxiliary distribution $q(x,\psi)$ 🦄, that is both easy to evaluate analytically and easy to sample from, and define the lower bound as follows:
$$
L(x,\theta,\psi)=\log p(x;\theta)−\operatorname{KL}[q(z;\psi)\|p(z|x;\theta)],
$$
where $\theta$ are the parameters of the generative model. Hey, but doesn't that formula have the two things we can't evaluate? The good thing is that the lower bound can be rearranged to a form where we don't need either of those:
$$
L(x,\theta,\psi)=\mathbb E_q\log p(x,y;\theta)q(x;\psi)
$$
So basically:
$$
\log(🦂)−\operatorname{KL}[🦄\|🐉]=\mathbb E_🦄\log\frac{🐨}{🦄}
$$
so we only have to deal with koalas and unicorns. Sweet.
For the drawbacks of VI and ''Rényi Lower Bound'', refer to [[Rényi Divergence Variational Inference|https://arxiv.org/abs/1602.02311]] and [[this blog|http://www.inference.vc/variational-renyi-lower-bound/]].
! Relationship with EM
$\mathbb E_🐉\log🐨$ is the ''expected complete log-likelihood'', which is optimized by EM. Variational inference does not estimate fixed model parameters, it is often used in a Bayesian setting where classical parameters are treated as latent variables. Variational inference applies to models where we cannot compute the exact conditional of the latent variables.
! Coordinate ascent mean-field variational inference
CAVI fixes other variational factors and compute the optimal $q_j(z_j)$ by its ''complete conditional''. This can be seen as a ''message passing'' algorithm, and is closely related to Gibbs sampling.
! Variations
* [[Variational Generative Models]]
! Computation
* Computing the expected log likelihood $\mathbb E_{z\sim q(z)}[\log p(x|z, \theta)]$
** MC estimator
** Adversarial Variational Bayes
Hopefully this would be the goto blog for variational inference if you are interested in Variational Autoencoder, GAN etc.
! References
* [[Variational Inference: Foundations and Modern Methods|https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Variational-Inference-Foundations-and-Modern-Methods]]
* Several posts from [[inFERENCe|http://www.inference.vc/]]
! Variational Inference
!! Introduction
We care about the posterior of hidden variable $z$ given observation $x$:
$$
p(z|x) = \frac{p(z, x)}{p(x)}
$$
This is always intractable because the denominator $p(x)$ is super hard to compute. Let use the notations from [[The Variational Rényi Lower Bound|http://www.inference.vc/variational-renyi-lower-bound/]] for the ease of reading, the evaluation of
* $p(z)$ is very easy 🐣,
* $p(x|z)$ is easy 🐹,
* $p(x,z)$ is easy 🐨,
* $p(x)$ is super-hard 🦂,
* $p(z|x)$ is mega-hard 🐉
The 2 major approximation methods are
* MCMC, which forms a Markov chain whose posterior is 🐉,
* Variational inference, which people use to call it what you do when you are waiting for your MCMC sampler to converge.
So it's fast, and it's not very accurate. Anyway, how does it work? VI posit a ''variational family'' of distributions over the latent variables, $q(z;\nu)$, 🦄, and minimize the KL-divergence between 🦄 and 🐉. KL itself is intractable, we turn to the ''evidence lower bound'':
$$
g(z, \nu) = \log(🦂)−\operatorname{KL}[🦄\|🐉]=\mathbb E_🦄\log 🐨 - \mathbb E_🦄\log 🦄
$$
The first term prefers 🦄 to place its mass on the MAP, @@color:#859900;and the second is the entropy of 🦄 and acts like a regulizer@@.
If we modify the divergence part, we arrive at other methods:
* Further factorizing 🦄 into $🦄_1(\theta)🦄_2(\mbox(tags))$, the alternating optimization scheme is similar to message passing
* This should remind you of belief propagation but this topic is beyond this post
* Rényi Divergence, then we get [[this paper|https://arxiv.org/abs/1602.02311]]
For the drawbacks of VI and ''Rényi Lower Bound'', refer to [[Rényi Divergence Variational Inference|https://arxiv.org/abs/1602.02311]] and [[this blog|http://www.inference.vc/variational-renyi-lower-bound/]].
VI was first introduced to AI world to solve neural networks and picked up by Jordan's lab and generalized to many probabilistic models. Modern VI research touches areas like Bayesian statistics, convex optimization, reinforcement learning and (probably you're interested) neural network.
! Stochastic Gradients of ELBO
When we have large dataset computing the gradient is expensive. We can update the parameters based on small batches of data.
To optimize ELBO, we have to first take the estimation and then take the gradient. The expectation is not always tractable, for specific models, we use conjugate priors to solve it. Unfortunately, most powerful models don't fall into this category.
In normal VI we are often limited to exponential family distributions. Here we introduce ways to enable VI for more generalized class of models.
!! REINFORCE gradients
The problem here is we try to compute the integration before gradient, when we switch these two (true for most cases), we get stochastic optimization scheme.
$$
\nabla_\nu\mathcal L = \nabla_\nu\int 🦄g(z, \nu)dz = \mathbb E_🦄[\nabla_\nu\log🦄(\log🐨-\log🦄)]
$$
This gradient is model independent. The problem with this gradient is that likelihood functions take large values on tail of the distribution, and the variance is too large.
!! Controling variance
We can subtract it with functions with expectation 0 to get likelihood functions with same expectation bu t smaller variance. This kind of function is called ''control variates''. For any 🦄, we can use the score function $\nabla_\nu\log 🦄$ as the controller.
Techniques from [[MCMC]] could help as well.
!! Pathwise estimator
Instead of sampling from 🦄, we sample $\epsilon$ from some standard distribution $s$ and then transform it to $z$ with some fixed function $t$. We also assume 🐨 and 🦄 are differentiable w.r.t. $z$.
$$
\nabla_\nu\mathcal L = \mathbb E_s[\nabla_\nu g(t(\epsilon, \nu), \nu)] = \mathbb E_s[\nabla_z[\log🐨-\log🦄]\nabla_\nu t(\epsilon, \nu)]
$$
This trick yield much better behaved variance. This is also known as the reparameterization gradient.
!! Amortized inference
We add a global variable $\beta$ to the model. The algorithm becomes slow because we have to run optimization in the stochastic inner loop for each data point.
We can learn a mapping $f_\theta$ from $x_i$ to $\phi_i$:
$$
\phi_i = f_\theta(x_i)
$$
This function is can be learned using neural network. VAE seems quite straight forward with pathwise estimator and amortized inference:
<<<
''Model'' [Variational Autoencoder]<br>
We reparametrize observation $x$ with hidden variables $z\sim\mathcal N(0, 1)$ from standard normal:
$$
p(x|z)$ = \mathcal N(\mu_\beta(z), \sigma_\beta^2(z)),
$$
and learn the parameters of 🦄 with amortized inference
$$
(q(z|x) = \mathcal N(f^\mu_\theta(x), f^\sigma_\theta(x)).
$$
Where all functions are deep networks.
<<<
! Variational Inference for Implicit Models
🦄 from ''Mean-field family'' is fully factorized, this cannot approximate complex 🐉. Fully factorized as VAE, at the end of the day, we usually output just a single Gaussian. The parametric assumptions we make in mean-field is too strong, and implicit models would be one way to relax these.
One step forward, we can use covariance matrix to control the dependencies between variables. Rank-1 approximation can give us better result than mean-field, but higher order is not acceptable because of the complexity. Another limitation of these methods is that the approximate posterior is always Gaussian.
[[DRAW: A Recurrent Neural Network For Image Generation|https://arxiv.org/abs/1502.04623]] imposes ordering and non-linear dependency on all preceding variables
$$
q_{AR}(z;\nu) = \prod_kq_k(z_k|z_{<k};\nu_k).
$$
This posterior is called autoregressive distribution. DRAW greatly improved the image generation quality.
A design principle for richer posteriors is as follow:
# introduce new variables
# adapt bound to compute entropy
# maintain computational efficiency
!! Change-of-variables
We transform the random variables with invertible functions, the final distribution is tractible with the rule of change of variables, multiplying the original distribution with the term of determinate of Jacobian:
$$
q(z') = q(z)|\det\frac{\partial f}{\partial z}|^{-1}
$$
By applying as many functions as we like, we can get models as complex as we need.
* Sampling of these models is easy
* And we can compute the entropy
This is called a normalising flow.
We want the Jacobian to be triangular so that the computation is fast. There are several choices of transformation functions:
* Planar flow: $z_k = z_{k-1} + uh(\omega^\top z_{k-1}+b)$ is able to learn an identity or an one-layer nonlinear transformation.
* Real NVP: $y_{1:d} = z_{k-1, 1:d}, y_{d+1:D}=t(z_{k-1, 1:d})+z_{d+1:D}\odot\exp(s(z_{k-1, 1:d}))$
* Inverse AR flow: $z_k = \frac{z_{k-1}-1-\mu_k(z_{<k, x})}{\sigma_k(z_{<k}, x)}$
We can use latent variables to enrich the posterior approximation. In previous setting, we only allow continuous hidden variable distributions. Now we can use stochastic latent variable $\omega$ and both continuous and discrete distributions.
We introduce ''auxiliary variable'' $\omega$ that depends on $z$ and $x$. We now use ''auxiliary variational bound'', a lower bound on minus entropy term to make the estimation of ELBO tractable:
$$
\log p(x)\ge\mathcal L - \mathbb E_{q(z|x)}\operatorname{KL}[q(\omega|z, x)\|r(\omega|z, x)]
$$
The way of choosing auxiliary prior $r(\omega|z, x)$ and auxiliary posterior $q(\omega|x, z)$ includes
* Hamilton flow: $r(\omega) = \mathcal N(\omega|0, M)$ as independent Gaussian.
* Input-dependent Gaussian: $r(\omega|x, z)$
* Auto-regressive: $r(\omega|x, z) = \prod_ir(\omega_t|f_\theta(\omega_{<t}, x))
* $q(\omega|x, z)$ can be a mixture model, normalising flow, Gaussian process
Another way of doing this is auxiliary variables, where we use entropy bounds and Monte Carlo sampling.
! Variational Inference for GANs
The idea of applying variational inference to adversarial training has long been establishded. [[Adversarial Autoencoder|http://www.inference.vc/adversarial-autoencoders/]] use amortized inference to
For estimating the density ratio $\frac{q(z|x}{p(z)}$, we use logistic regression, introducing a discriminator $D$. We amortise the discriminator, learn a logistic regression classifier $D(z, x)$.
The generator $G$ in GAN has loss approximating the ELBO:
$$
\mathcal L(G;D) = \frac 1 N\sum_{n=1}^N\mathbb E_q\log D(z, x_n)+\mathbb E_p\log(1-D(z, x_n) = \frac 1 N\sum_{n=1}^N\mathbb E_\epsilon\left[\log\frac{D(G(\epsilon, x_n), x_n)}{1-D(G(\epsilon, x_n), x_n)}-\log p(x_n|z)\right]
$$
We can minimise the same loss w.r.t. parameters of $p(x|z)$ to obtain a VAE-like learning algorithm.
*
* [[Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks|https://arxiv.org/abs/1701.04722]]
* [[ALI|https://arxiv.org/abs/1606.00704]]
* [[BiGAN|https://arxiv.org/abs/1605.09782]]
! Variational distribution
For each observation $x_n$ we have a corresponding latent variable $z_n$ comprising a $1$-of-$K$ binary vector.
!! Priors
The prior of $\pi$ is Dirichlet distribution
$$
p(\pi)=\text{Dir}(\pi\mid\alpha_0)=C(\alpha_0)\prod_{k=1}^K\pi_k^{\alpha_0-1}
$$
The prior of the mean and precision of each Gaussian component is an independent Gaussian-Wishart prior
$$
p(\mu,\Lambda)=p(\mu\mid\Lambda)p(\Lambda)=\prod_{k=1}^K\mathcal N(\mu_k\mid m_0, (\beta_0\Lambda_k)^{-1})\mathcal W(\Lambda_k\mid W_0,\nu_0)
$$
!! Conditional Distributions
$$
p(X\mid Z, \mu,\Lambda) = \prod_{n=1}^N\prod_{k=1}^K\mathcal N\left(x_n\mid\mu_k,\Lambda_k^{-1}\right)^{z_{nk}}
$$
is the conditional distribution of the observed data vector.
$$
p(Z\mid\pi)=\prod_{n=1}^N\prod_{k=1}^K\pi_k^{z_{nk}}
$$
is the conditional distribution of $Z$ given the mixing coefficient $\pi$.
The joint distribution of all of the random variables,
$$
p(X,Z,\pi,\mu,\Lambda= p(X\mid Z, \mu,\Lambda)p(Z\mid\pi)p(\mu\mid\Lambda)p(\Lambda)
$$
We consider a variational distribution which factorizes between the latent variables and the parameters so that
$$
q(Z,\pi, \mu, \Lambda)=q(Z)q(\pi, \mu, \Lambda)
$$
[[general result|10.1 Variational Inference]]
! References * Model-based VC [11-13] * Maximum-likelihood parameter generation (MLPG) is robus against variance modifications [22]
Lets break down the VGGNet in more detail. The whole VGGNet is composed of CONV layers that perform 3x3 convolutions with stride 1 and pad 1, and of POOL layers that perform 2x2 max pooling with stride 2 (and no padding). We can write out the size of the representation at each step of the processing and keep track of both the representation size and the total number of weights: ``` INPUT: [224x224x3] memory: 224*224*3=150K weights: 0 CONV3-64: [224x224x64] memory: 224*224*64=3.2M weights: (3*3*3)*64 = 1,728 CONV3-64: [224x224x64] memory: 224*224*64=3.2M weights: (3*3*64)*64 = 36,864 POOL2: [112x112x64] memory: 112*112*64=800K weights: 0 CONV3-128: [112x112x128] memory: 112*112*128=1.6M weights: (3*3*64)*128 = 73,728 CONV3-128: [112x112x128] memory: 112*112*128=1.6M weights: (3*3*128)*128 = 147,456 POOL2: [56x56x128] memory: 56*56*128=400K weights: 0 CONV3-256: [56x56x256] memory: 56*56*256=800K weights: (3*3*128)*256 = 294,912 CONV3-256: [56x56x256] memory: 56*56*256=800K weights: (3*3*256)*256 = 589,824 CONV3-256: [56x56x256] memory: 56*56*256=800K weights: (3*3*256)*256 = 589,824 POOL2: [28x28x256] memory: 28*28*256=200K weights: 0 CONV3-512: [28x28x512] memory: 28*28*512=400K weights: (3*3*256)*512 = 1,179,648 CONV3-512: [28x28x512] memory: 28*28*512=400K weights: (3*3*512)*512 = 2,359,296 CONV3-512: [28x28x512] memory: 28*28*512=400K weights: (3*3*512)*512 = 2,359,296 POOL2: [14x14x512] memory: 14*14*512=100K weights: 0 CONV3-512: [14x14x512] memory: 14*14*512=100K weights: (3*3*512)*512 = 2,359,296 CONV3-512: [14x14x512] memory: 14*14*512=100K weights: (3*3*512)*512 = 2,359,296 CONV3-512: [14x14x512] memory: 14*14*512=100K weights: (3*3*512)*512 = 2,359,296 POOL2: [7x7x512] memory: 7*7*512=25K weights: 0 FC: [1x1x4096] memory: 4096 weights: 7*7*512*4096 = 102,760,448 FC: [1x1x4096] memory: 4096 weights: 4096*4096 = 16,777,216 FC: [1x1x1000] memory: 1000 weights: 4096*1000 = 4,096,000 TOTAL memory: 24M * 4 bytes ~= 93MB / image (only forward! ~*2 for bwd) TOTAL params: 138M parameters ``` As is common with Convolutional Networks, notice that most of the memory is used in the early CONV layers, and that most of the parameters are in the last FC layers. In this particular case, the first FC layer contains 100M weights, out of a total of 140M.
# Jeff Donahue, Lisa Anne Hendricks, Sergio Guadarrama, Marcus Rohrbach, Subhashini Venugopalan, Kate Saenko, Trevor Darrell, Long-term Recurrent Convolutional Networks for Visual Recognition and Description, CVPR, 2015. # Subhashini Venugopalan, Huijuan Xu, Jeff Donahue, Marcus Rohrbach, Raymond Mooney, Kate Saenko, Translating Videos to Natural Language Using Deep Recurrent Neural Networks, arXiv:1412.4729. # Yingwei Pan, Tao Mei, Ting Yao, Houqiang Li, Yong Rui, Joint Modeling Embedding and Translation to Bridge Video and Language, arXiv:1505.01861. # Subhashini Venugopalan, Marcus Rohrbach, Jeff Donahue, Raymond Mooney, Trevor Darrell, Kate Saenko, Sequence to Sequence--Video to Text, arXiv:1505.00487. # Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, Aaron Courville, Describing Videos by Exploiting Temporal Structure, arXiv:1502.08029 # Anna Rohrbach, Marcus Rohrbach, Bernt Schiele, The Long-Short Story of Movie Description, arXiv:1506.01698 # Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler, Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books, arXiv:1506.06724 # Kyunghyun Cho, Aaron Courville, Yoshua Bengio, Describing Multimedia Content using Attention-based Encoder-Decoder Networks, arXiv:1507.01053
! Dataset * [[DAVIS Challenge 2017|http://davischallenge.org/code.html]] ** [[Video Object Segmentation with Re-identification|https://arxiv.org/abs/1708.00197]]: winner entry
* Descriptors * [[Visual Attention]] * [[Meta-Learning]] ! Multiscale approaches In LABGAN, generate a pyramid of images, and use the components of Laplacian filters to recover the whole image. This method is used for video prediction. ! Applications * [[OpenPose|https://github.com/CMU-Perceptual-Computing-Lab/openpose]] ** a fast inference implementation: [[tf-openpose|https://github.com/ildoonet/tf-openpose]]
!! Bib
For supervised tasks
* [[Recurrent Models of Visual Attention|https://arxiv.org/abs/1406.6247]]
** Use RNN to generate glimpse location
** [[Torch blog|http://torch.ch/blog/2015/09/21/rmva.html]]
* Learning to combine foveal glimpses with a third-order boltzmann machine
* What and where: A Bayesian inference theory of attention
* [[Show, Attend and Tell: Neural Image Caption Generation with Visual Attention|https://arxiv.org/abs/1502.03044]]
** Hard/soft attention. focus on single element, hard to learn.
* Neural variational inference and learning in belief networks
* Learning wake-sleep recurrent attention models
For unsupervised tasks
* [[DRAW: A Recurrent Neural Network For Image Generation|https://arxiv.org/abs/1502.04623]]
** [[Magenta review|https://github.com/tensorflow/magenta/blob/master/magenta/reviews/draw.md]]: the foveation attention model is differentiable and so can be trained end to end with backpropagation. Instead of simply cropping a patch from the input image, DRAW extracts the pixels in the patch by Gaussian interpolation.
!! Attention types
* Reading attentions are widely used in discriminative tasks, usually transforms an image into a representation in a canonical coordinate space, with the parameters controlling the attention learned by gradient descent.
* Writing (generative) attentions decide which part to generate
* Randomized
* Error-based
!! Spatially-transformed attention
refs:
* Spatial transformer networks
* Efficient inference in occlusion-aware generative models of images
** develop occlusion-aware generative models that make use of spatial transformers in this way.
Rather than selecting a patch of an image (taking glimpses) as other methods do, a more powerful approach is to use a mechanism that provides invariance to shape and size of objects (general affine transformations). ''Spatial transformers'' process an input image $x$ using parameters $\lambda$ to generate an output:
$$
\operatorname{ST}(x, \lambda) = [\kappa_h(\lambda)\otimes\kappa_w(\lambda)]\ast x,
$$
where $\kappa_h$ and $\kappa_w$ are $1$-dimensional kernels, $\otimes$ indicates the tensor outer-product of the two kernels and $\ast$ indicates a convolution. When used for reading attention, spatial transformers allow the model to observe the input image @@color:#859900;in a canonical form, providing the desired invariance@@. When used for writing attention, it allows the generative model to independently handle position, scale and rotation of parts of the generated image, as well as their content.
! Bibs * Learning to Compose Neural Networks for Question Answering ** Use modular neural network architectures. ! Dataset * [[COCO VQA Challenge|http://www.visualqa.org/]] ! Tutorial [[Visual Turing test tutorial|https://github.com/mateuszmalinowski/visual_turing_test-tutorial]]
Suppose that a ConvNet classifies an image as a dog. How can we be certain that it's actually picking up on the dog in the image as opposed to some contextual cues from the background or some other miscellaneous object? One way of investigating which part of the image some classification prediction is coming from is by plotting the probability of the class of interest (e.g. dog class) as a function of the position of an occluder object. That is, we iterate over regions of the image, set a patch of the image to be all zero, and look at the probability of the class. We can visualize the probability as a 2-dimensional heat map. This approach has been used in Matthew Zeiler's [[Visualizing and Understanding Convolutional Networks|http://arxiv.org/abs/1311.2901]]: [img height=400 [http://cs231n.github.io/assets/cnnvis/occlude.jpeg]] ! Implementations * [[Jason Yosinski|http://yosinski.com/]] prodived [[a great visualization tool|http://devblogs.nvidia.com/parallelforall/harnessing-caffe-framework-deep-visualization/]] based on caffe, implementing the method mentioned above (with slight difference). * [[Network Dissection|http://netdissect.csail.mit.edu/]] * [[Plug and Play Generative Networks|https://github.com/Evolving-AI-Lab/ppgn]]: generate features
! Introduction
Many state-of-art researches focus on spectral conversion techniques. 2 categories
* rule based methods: e.g. move the position of formants. most of the details are retained. ''The information (e.g. formants) for building the converion function is usually difficult to be extracted accurately.''
* statistical methods: construct a more precise mapping function using complex statistical models.
! 3 major issues
Sinusoidal model can support modifications to both the prosody and the spectral characteristics.
Acoustic features which enable humans to identify speakers must be extracted and coded. These features should be independent of the message and the environment so that whatever and wherever the source speaker speaks, his/her voice characteristics can be successfully transformed to sound like the target speaker.
The type of conversion function and method of training and applying the conversion function must be decided.
! Basic methods: parallel training
* [[Codebook mapping method for VC]]
** Standard - vector quantization: generate the 1-1 correspondence between source and target spectral codebook. But the linear transformation from a limited set of vectors to form the target centroids may leads to discontinuous in the synthesised voice and degrade of speech quality.
** Weighted - Line spectral frequencies (LSFs) are used to weigh the target codebooks. This is proposed to leviate the discontinous problem.
* Formant mapping is prone to formant tracking errors.
* Linear multivariate regression for VC
$$
\hat y_t = A_mx_t+B_m,
$$
* ANN can handle nonlinear transformation functions.
! HMM/GMM
[[GMM for VC]]
!! HMM sequence-to-frame mapping
Let $p(x|s)$ denote a state-observation probability of frame vector $x$ given state $s$, and $p(s'|s)$ represent a state-transition probability from state $s$ to $s'$. Given state $s$ and source vector $x$, we assume that target vector $y$ has the following linear-Gaussian distribution,
$$
p(y|s, x) = N(y|B_sx+b_s,\Sigma_s),
$$
The mapping can be learned with least mean square or
!! GP based model
This is a variation of GMM. Solve the mapping function as a regression problem and apply Gaussian process to solve it. Assign a GP for each dimension of the target spectra.
! Segmentation Based
[[Maximum likelihood transformations for VC]]
! Model-based VC
!! Expression conversion model
The advantage of model based VC method is it does not require parallel corpora for training. It describe the relationship between expressions of source and target speaker with linear transforms.
$$
\log(f0_c) = \mu_t+\frac{\sigma_t}{\sigma_s}(\log(f0_s)-\mu_s)
$$
[[VC with factorised HMM-TTS models]]
! Speaker adaptation techniques
Generate "average voice" model from multiple speakers' recordings.
* Feature-space MLLR
! Deep Neural Network
[[USTC DNN VC]]
[[Proposal|VC Research Proposal]]
Build the speaker acoustic model with dynamic information.
* HMM
* Conditional RBM
* Gaussian process dynamic model
! Previous works
Related works of [[DNN ASR]]
[[DNN Adaptation]]
[[Multitask Learning for Phone label and State]]
! Model
An ASR-like model to extract speaker's acoustic feature from the text label (and prosody). Then learn the transformation between target and source features.
I kind of think a flexible voice conversion system can work as a semi-TTS. So the training data need to be as large unless personal acoustic features can be precisely modeled. The difference is personal prosody (and maybe [[glottal flow|Glottis]]) features does not need to be learnt.
We can see this problem as learning to sing.
# Extract target and source acoustic features (enough to reconstruct the language, or unless the target sentence).
# Learn the exact matching (the matching matrix is sparse, but linear is not a good idea)
# We learn the score and lyrics (prosody and text) from the target voice. Using the other part of the same model.
# Send the converted features and the script back to the TTS-like feature extraction model and get the voice back at the other end.
!! Extracting vocal tract
A time series indicating the speech text and prosody characteristics of the sentence, multiplying the speaker's acoustic features for these variables makes a series of spectra.
!!! Sparse coding
!!! Deep feature generation
!! Learning the transformation
maximum output probability parameter generation
$$
\tilde y^{(s)} = arg \underset{y^{(s)}}{\max}P(y|x,\lambda^{(v)})
$$
[[link|https://arxiv.org/abs/1711.00937]] pretty nice result of combining VAE with pixelRNN. Further work should be done in speaker style conversion. The quantization method used is so simple. Should consider other approximation methods. [[Continuous approximations to arithmetic functions]]
Fourier transforms suffer from [[resolution problem|Fourier Transform vs Wavelet Transform]]. Wavelet is here to rescue.
In mathematics, a ''wavelet series'' is a representation of a square-integrable function by a certain orthonormal series generated by a wavelet.
!! Formal definition
A function $\psi\in L^2(\mathbb{R})$ is called an ''orthonormal wavelet'' if it can be used to define a Hilbert basis, that is a complete orthonormal system, for the Hilbert space $L^2(\mathbb{R})$ of square integrable functions.
The Hilbert basis is constructed as the family of functions $\{\psi_{jk}: j, k \in \mathbb Z\}$ by means of dyadic translations and dilations of $\psi$,
$$
\psi_{jk}(x) = 2^\frac{j}{2} \psi(2^jx - k)
$$
for integers $j$, $k \in \mathbb{Z}$.
This family is an orthonormal system if it is orthonormal under the standard inner product $\langle f, g\rangle = \int_{-\infty}^\infty f(x)\overline{g(x)}dx$ on $L^2(\mathbb{R})$.
$$
\langle\psi_{jk},\psi_{lm}\rangle = \delta_{jl}\delta_{km}
$$
where $\delta_{jl}$, is the Kronecker delta.
Completeness is satisfied if every function $h \in L^2(\mathbb{R})$ may be expanded in the basis as
$$
h(x) = \sum_{j, k=-\infty}^\infty c_{jk} \psi_{jk}(x)
$$
with convergence of the series understood to be convergence in norm. Such a representation of a function $f$ is known as a ''wavelet series''. This implies that an orthonormal wavelet is self-dual.
!! Wavelet transform
A wavelet transform is defined by dilating a mother wavelet $\psi\in\mathbf L^2(\mathbb R^d)$ with scale factors $\{a^j\}_{j\in Z}$ for $a>1$. In image processing applications one usually sets $a=2$, whereas audio applications need smaller dilations factors, typically $a\leq2^{1/8}$. Wavelets are not only dilation
The integral wavelet transform is the integral transform defined as
$$
\left[W_\psi f\right](a, b) = \frac{1}{\sqrt{|a|}} \int_{-\infty}^\infty \overline{\psi\left(\frac{x-b}{a}\right)}f(x)dx,
$$
The ''wavelet coefficients'' $c_{jk}$ are then given by
$$
c_{jk} = \left[W_\psi f\right]\left(2^{-j}, k2^{-j}\right)
$$
We can construct wavelet functions which are //orthonormal//. Examples of prototype wavelet functions are [[Haar basis function|Haar Wavelet]], [[Daubechies basis function|Daubechies Wavelet]], [[Shannon basis funtion|Shannon Wavelet]], [[Mexican Hat funtion|Mexican Hat Wavelet]] etc. These basis functions can be implemented using filters. Longer filters lead to smoother functions.
!!! Basic idea
The fundamental idea of wavelet transforms is that the transformation should allow only changes in time extension, but not shape. This is effected by choosing suitable basis functions that allow for this. Changes in the time extension are expected to conform to the corresponding analysis frequency of the basis function. Based on the uncertainty principle of signal processing.
!!! Littlewood-Paley wavelet transform
A Littlewood-Paley wavelet transform is a redundant representation which computes the following filter banks, without subsampling:
$$
\forall u\in\mathbb R^d, \forall \lambda a^{\mathbb Z}\times G, W_{\lambda}x(u)=x\star\psi_\lambda(u)=\int x(v)\psi_\lambda(u-v)dv.
$$
If $x$ is real and the wavelet is chosen such that $\hat\psi$ is also real, then $W_{-\lambda}x=W_{\lambda}x^*$, which implies that in that case one can assimilate a rotation $r$ with its negative version $-r$ into an equivalence class of positive rotations $G^+=G/\{\pm1\}$.
!!! Filter Banks
Wavelet can be thought of as a [[band-pass filter|Band-pass Filter]] from signal processing point of view. In specific cases, wavelets can be considered as an octave band pass filter. So, we can use [[filter banks|Filter Bank]] to implement wavelet transform. Thus, wavelet transform can be interpreted as [[constant-Q filtering|Constant-Q Filtering]] with a set of octave band filters, followed by sampling at respective [[Nyquist frequencies|Nyquist Frequency]]. Also, by adding higher octave bands, we add resolution to the signal.
[[paper|https://arxiv.org/abs/1609.03499]] * [[A clean tf implementation|https://github.com/ibab/tensorflow-wavenet]] * [[Fast implementation by caching|https://github.com/tomlepaine/fast-wavenet]] * [[ByteNet based on it|https://github.com/paarthneekhara/byteNet-tensorflow]] ** [[ByteNet paper|https://arxiv.org/abs/1610.10099]] WaveNet like papers have been used in audio generation, NMT and ASR.
Repeatedly running the model forward and backing it up again seems like a silly thing to do. Indeed online methods for training recurrent neural networks are an active area of research.
[[500+ Colours|http://cloford.com/resources/colours/500col.htm]]
[[link|https://arxiv.org/abs/1602.07868]]
openai uses this on ResNet VAEs for IAF. We express the weight vectors in terms of the new parameters using
$$
\mathbf w = \frac{g}{\|\mathbf v\|}\mathbf v
$$
where $\mathbf v$ is a $k$-dimensional vector, $g$ is a scalar, and $\|\mathbf v\|$ denotes the Euclidean norm of $\mathbf v$. We can differentiate this to obtain the gradient of a loss function $L$ with
$$
\nabla_gL = \frac{\mathbf v^\top}{\|\mathbf v\|}\nabla_{\mathbf w}L
$$
and the gradient w.r.t. $\mathbf v$ is
$$
\begin{array}
&&\\
\nabla_\mathbf{v}L&=&\frac{\partial\mathbf w}{\partial\mathbf v}\nabla_\mathbf{w}L\\
&=&\frac{g}{\|\mathbf{v}\|}I\nabla_\mathbf{w}L-\frac{\partial\mathbf w}{\partial\|\mathbf v\|}\frac{\partial\|\mathbf v\|}{\partial\mathbf v}\nabla_\mathbf{w}L\\
&=&\frac{g}{\|\mathbf{v}\|}I\nabla_\mathbf{w}L-\left(\frac{g}{\|\mathbf v\|^2}\mathbf v\right)\frac{\partial\|\mathbf v\|}{\partial\mathbf v}\nabla_\mathbf{w}L\\
&=&\frac{g}{\|\mathbf{v}\|}\nabla_\mathbf{w}L-\frac{g\mathbf{v}\mathbf{v}^\top}{\|\mathbf{v}\|^3}\nabla_\mathbf{w}L\\
&=&\frac{g}{\|\mathbf{v}\|}\nabla_\mathbf{w}L-\frac{g\nabla_gL}{\|\mathbf{v}\|^2}\mathbf{v}\\
\end{array}
$$
Welcome to my personal wiki. I think it would be helpful if I share my notes. I kept my notes in a very non-linear way. So be sure to dig really deep until you find what you are looking for. Here are some nice places to start: * [[Deep Learning]]: This is the root of deep learning related materials I am following. * [[Deep Learning Bib]]: I write some of my paper summaries here. * [[Readings]]: Some good old resources. * [[Speech]]: If you are interested in my works during graduate study. * [[Programming]]: Here are some more hackery stuff, focusing majorly on [[clojure]]. ''Notice'': please don't change anything.
* [[Wasserstein GAN|https://arxiv.org/abs/1701.07875]]
* [[Towards Principled Methods for Training Generative Adversarial Networks|https://arxiv.org/abs/1701.04862]]
* [[Improved Training of Wasserstein GANs|https://arxiv.org/abs/1704.00028]]
** A simple way to enforce the Lipschitz constraint on the class of functions, which can be modeled by the neural network, is weight clipping. It was proposed that training can be improved by instead augmenting the loss by a regularization term that penalizes the deviation of the gradient of the critic (as a function of the network's input) from one.
* [[On the regularization of Wasserstein GANs|https://arxiv.org/abs/1709.08894]]
** A theoretical proof of using a weaker regularization term enforcing the Lipschitz constraint is preferable
! Implementations
* [[tf|https://github.com/igul222/improved_wgan_training]]
! Idea
Directly learning $P_\theta$ is bad because it'll be very unlikely that all of $P_r$ lies within $P_\theta$'s support. We can add random noise to $P_\theta$.
! Details
WGAN uses a critic instead of a discriminator. The critic is trained to provide an approximation of the Wasserstein distance between the real data distribution $P_r$ and the generator distribution $P_g$. The approximation is based on the Kantorovich-Rubinstein duality (see [[this blog|https://vincentherrmann.github.io/blog/wasserstein/]] for more):
$$
W(P_r, P_g)\propto \underset{f}{\max}\mathbb E_{x\sim P_r}[f(x)]-\mathbb E_{x\sim P_g}[f(x)]
$$
[[Optimal Transport GAN and VAE Explaination|https://arxiv.org/abs/1706.01807]]
Jose Abreu: top fisherman
A whitening tranformation is used for decorrelation. It transforms a set of variables into a new set with covariance equals $I$.
Typically whitening $X$ means multiplying by $M^{-1/2}$ where $M = E[XX^\top]$. Whitening transformation is not unique. Whitened data will stay whitened after rotations, which means $W = RM^{-1/2}$ with orthogonal matrix $R$ will also be a whitening transformation.
Whitening can be done with PCA, let $M$ have eigenvectors in columns of $E$ and eigenvalues on the diagonal of $D$, so that $C = EDE^\top$. A "normal" PCA transformation if given by $W_{PCA} = D^{-1/2}E^\top$. $M^{-1/2}$ can also be obtained with PCA by rotating with $E$, we call this ZCA whitening
$$
W_{ZCA} = ED^{-1/2}E^\top = M^{-1/2}.
$$
ZCA tries to transform the data as little as possible. Since correlations in natural data (e.g. images) are mostly very local, so decorrelation filters can also be local.
Just links between Tiddlers.
! Refs * [[examples|https://meta.wikimedia.org/wiki/Help:Wikitext_examples]] ! Snippets !! WikiLinks * `[[Tiddler Name]]` * `[[Link Name|Link Address]]` !! Images First import the image. Then include it with `[img[img_name.ext]]`
* BoW * Distributed word embeddings * Continuous ''vector'' representation of words ** co-occurrence stats to obtain vectors for long phrases ** LSA, fail to preserve linear regularities among words ** LDA, computationally expensive on large datasets ** [[Word2Vec]] ** [[GloVe|Global Vectors for Word Representation]] * [[Hashed Character $n$-gram|https://arxiv.org/abs/1511.06018]] ! Refs * [[Supervised Learning of Universal Sentence Representations from Natural Language Inference Data|https://arxiv.org/abs/1705.02364]] ** embedding sentences on Stanford Natural Language Inference dataset ** BiLSTM with attention, max-pooling over hidden representation * [[One-shot and few-shot learning of word embeddings|https://arxiv.org/abs/1710.10280]]
! Ideas to try
* character level word embedding learning
** learn word embedding model end2end to avoid storing large stuff
** probably better word root stuff?
* definition based word embedding learning
! refs
* [[Distributed Representations of Words and Phrases and their Compositionality|https://arxiv.org/abs/1310.4546]]
* [[Enriching Word Vectors with Subword Information|https://arxiv.org/abs/1607.04606]]
* [[Bag of Tricks for Efficient Text Classification|https://arxiv.org/abs/1607.01759]]
** [[fastText supervised tutorial|https://github.com/facebookresearch/fastText/blob/master/tutorials/supervised-learning.md]]
** Language detector in less than 1MB
!! Similarity to Count-Based Models
* [[Neural Word Embedding as Implicit Matrix Factorization|https://papers.nips.cc/paper/5477-neural-word-embedding-as-implicit-matrix-factorization]]: Word2Vec == PMI matrix factorization of count based models
* Count based and most neural models have equivalent performace when properly hyperoptimized.
Word similarity is hard to represent. High-dim vectors are not robust. Usually around 25-1000 dim.
Represent words as vectors in some space. Learn high-quality word vectors from hugh datasets with billions of words, and with millions of words in the vocabulary.
king:queen=man:woman
''The distributional hypothesis'': words with similar context have similar meaning.
We can represent words as cooccurence matrix. And reduce dim (by e.g. svd). This method suffer from frequency inbalance, computation inefficiency, etc.
!! Idea
By predicting words to the left and right, cooccurence will also be captured.
!! The model
Predicting surrounding words. The object of Skip-gram model is to maximize the average log probability:
$$
J(\Omega) = \frac 1 T\sum_{t=1}^T\sum_{-c\le j\le c, j\ne0}\log p(w_{t+j}|w_t)
$$
Where the input $w_t$ is the center word of a 1-of-V (or one-hot) encoding. The basic Skip-gram formulation defines $p(w_{t+j}|w_t)$ as softmax, whose computation is expensive when vocabulary is large (10^^6^^).
!!! Hierarchical softmax
$$
p(w|w_I) = \prod_{j=1}^{L(w)-1}\sigma([n(w, j+1)=ch(n(w,j))]\cdot v_{n(w,j)}'\top v_{w_I})
$$
where $v$ and $v'$ are the same as $v_{in}$, $v_{out}$, $\sigma(x)=1/(1+\exp(-x))$, $[x]$ is 1 if $x$ is true otherwise -1.
So the nagative sampling approximates it as:
$$
\log p(w_O|w_I)\approx\log\sigma(\langle v^{w_I}_{in}, v^{w_O}_{out}\rangle)+\sum_{k=1}^K\mathbb E_{wk\sim P_n(w)}[\log \sigma(-\langle v^{w_I}_{in}, v^{w_O}_{out}\rangle)],
$$
where $\sigma(x)=\frac 1 {1+\exp(-x)}$ is the sigmoid (logistic) function, and the expectations are computed by drawing random words.
@inproceedings{ze2013statistical,
title={Statistical parametric speech synthesis using deep neural networks},
author={Ze, Heiga and Senior, Andrew and Schuster, Mike},
booktitle={Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on},
pages={7962--7966},
year={2013},
organization={IEEE}
}
[[Recent Advances in Zero-shot Recognition|https://arxiv.org/abs/1710.04837]] ! Semantic Representations in Zero-shot Recognition * Semantic Attributes: has wings, spotted... ** User-defined Attributes ** Relative Attributes ** Data-driven Attributes ** Video Attributes * Semanatic Representation beyond Attributes ** Concept ontology: e.g. WordNet's semantic distance ** Semantic word vectors ! Models * Embedding Models ** Bayesian Models *** Direct Attribute Prediction *** Indirect Attribute Prediction ** Semantic Embeddings ** Embeddings into Common Spaces ** Deep Embedding *** [[Learning a Deep Embedding Model for Zero-shot Learning|https://arxiv.org/abs/1611.05088]] * Recognition Models in the Embedding Space * Problems in Zero-shot Recognition ** Projection Domain Shift problem *** transductive learning ** Hubness problem
[[link|http://arxiv.org/pdf/1606.01305v1.pdf]] In RNN, this has a similar structure as dropout, only different by passing the activations from the previous time-step. Idea should work with ConvNet, result not very pretty.
高一时写的小说。当时想模仿古龙的文笔写一个不严肃的故事。没有换行符，只能在编辑模式下阅读。 古龙是优秀勤奋热情的小说家 黄风中的刀声 瀚海结金城, 清潮授长生 九月月带刀, 风萧萧无情 (一) 九月九,柳叶城. 柳叶城是再平凡不过的小城, 平凡得没什么人知道. 除非你到了那里, 否则你不会知道它. 因为去了那里的人, 没有回来过. /******/ 萧无情却偏偏在那里, 走在城中唯一的一条街上, 紧握着他的刀. 张老实站在他的面摊边上老老实实地冲他打着招呼. 张老实之所以叫张老实, 是因为他是小城里出了名的老实人. 萧无情并不买账, 写在他脸上的永远只有轻蔑和仇恨. 虽然只到柳叶城第二天, 他已知道城里很多事情, 其中每一件都很有把握. 张老实不老实! 绝无例外! 萧无情看着漂着青菜和油花的粗汤面, 目光无法移开. 他忽然发现饥饿比仇恨更强烈. 日头正高, 街上的行人仿佛都消失了. 张老实在他的面摊前, 笑着. "萧大爷吃面?"虽只一日, 大家都已知道这个萧无情. 萧无情把刀握得更紧了, 而他的脚迈进了门 /******/ 一碗热气腾腾的面摆在萧无情面前. 萧无情右手用筷子挑起一根清面, 左手依然握着刀. 世上一共出现过一百四十六种下毒的方法, 萧无情至少知道其中的一百四十四种, 所以他还活着, 也会活下去. 萧无情盯着面上的热气, 忽然明白了. 一碗盖着油的汤面不该有热气, 除非它盖的不是油!(忍不住加句后话....还是忍住了) 张老实站在他面前, 等着他将这口面吃下. 张老实果然不老实! 但萧无情可还是萧无情, 萧无情不光知道如何下毒, 也知道如何解别人下的毒. 油状之毒, 来自七步鹰胃中的黄汤, 沙漠中曾有秀水宫中人用熬鹰的手法将其制成七步孟婆汤, 因为毒性全然不同于百草之毒, 根本察无所察. 其克星正是与其同生在腐肉上的生物. 万物生克, 天下之毒七步之内必有解药, 萧无情很明白这个道理. 那种生物就是苍蝇. 没错, 不然与七步鹰共食一尸, 怎能不死. 最平凡的事物都有意想不到的力量. 正如这柳叶城的暗藏杀机, 如这城中张老实的深藏不露, 如张老实店中密布的苍蝇. 张老实店中的苍蝇实在很多, 多得让人有些不舒服. 但不舒服总比死了要好. 萧无情放下筷子, 抓起一张桌布, 在空中一挥, 十几只黄豆大小的苍蝇竟被吸在了桌布上. 萧无情从容地伸出空闲的右手, 将一只只苍蝇捻碎, 撒在面上. 张老实面色忽然煞白, 仿佛胸口被人打了一拳, 想走, 又不敢走. 萧无情一手捻着苍蝇, 另一只手握着刀, 眼睛看也不看张老实. 他已猜到张老实的表情. 过了好久, 面汤上的热气终于消失了. 萧无情眯起了双眼, 似乎很期待将要品尝的美味. 因为这面中还有习惯性的胜利和残虐的快感. 他喜欢践踏自己对手自尊心的感觉. 萧无情用一只手吃着面, 吃得很仔细. 仿佛这是他一生的最后一顿饭. 他就像一只野兽, 珍惜眼前的每一丁点面. 他用一只手吃面, 吃得很慢. (二) 夕阳西斜, 沙漠上瞬间凉了下来. 虽然阴云渐浓, 柳叶城却恢复了生机, 各样的人都出来消磨时光. 一个劲装男子走在路上, 走得很别扭. 一个跛子走路本来就别扭. 萧无情是个跛子, 这个跛子是萧无情. 他左脚先迈出, 右脚再慢慢跟上. 他的左手一直握着刀, 漆黑的刀! 用剑的人, 该把剑放在惯用手的另一侧. 正如当年迎风一刀斩描述的东洋刽子手拔刀术, 这样才能在剑出鞘的一刻使出夺命的一击. 刀却相反, 很少有人懂得这个道理. 有的人懂了, 却在同时死在了萧无情的刀下. 对一个刀客来说, 这样的道理还是不要懂的好. 萧无情一张紧绷的脸忽然涨红, 双目如要瞪出. 熟悉萧无情的人知道, 他从未有过这样的表情. 路人还未发现这名高贵刀客的异样, 萧无情已经不见了, 比风还快! 萧无情在飞奔, 人们如何都不肯相信跛子竟有如此敏捷的身法. 萧无情不能停下. 因为还没有活人被大便憋死! 因为解毒而加了苍蝇, 因为苍蝇白吃了面. 萧无情当然可以不吃那碗面. 但他不介意给张老实一个教训. /******/ 天皇皇, 地荒荒. 入了万草堂, 薰天香, 人断肠. (三) 这就是万草堂, 柳叶城最气派的地方. 男人女人分别有一间半封顶的房间, 每间房只有两个位置. 气派的地方当然容不下太多人. 这里是城中最宝贵的位置. 之所以宝贵, 是因为这里可以使人舒畅. 最让人舒畅的事当然是薰香. 城中人每天都来这里, 所以这里经常特别热闹. 还好现下是柳叶城各家吃晚饭的时间. 好在晚饭时很少有人薰香. /******/ 万草堂男宾上房有一个空位置, 萧无情刚坐下, 屋外一声惊雷, 竟下起了暴雨! 哗--哗-- 萧无情身边的黑衣人, 似乎已待了好久, 道:"好快!" 在做一些原始的事, 比如薰香时, 人总会坦诚起来. 萧无情开口说了自打来柳叶城的第一句话: "我还未脱下裤子." 黑衣人竟疯狂了, 他也是江湖上有身份的人. 这种人最容易疯狂. 因为他看到了一个远比他幸福的人. 一个如暴风雨般薰香的人. 萧无情的刀还在手里. 黑衣人的刀却在心上! 薰香虽然使人舒畅, 薰得久了却会受不了. 而黑衣人已经薰了三个时辰. 漫长的三个时辰, 会有很多人走进万草堂. 所以黑衣人已经杀了很多人--薰得比他快的人. 而没有一个人比萧无情更快! 比他快的只有他自己的刀! 萧无情的刀, 是魔刀, 因仇恨而生, 带给人血与痛. 江湖上很少有人不知道. 黑衣人当然知道, 他还知道了萧无情胜人一筹的不只有他的刀, 他更羡慕的是萧无情风驰电掣的薰香速度. 黑衣人知道自己杀不了眼前这个人, 他本也没打算杀他. 这种优越与对他的羞辱, 是性命无法偿还的. 疯狂的人有疯狂的想法. /******/ 萧无情:"朋友," 黑衣人:"没有朋友!朋友是用来出卖的!" 萧无情:"你有草纸吗?" 黑衣人冷笑道:"我用不着纸, 你也用不着纸." "..." 萧无情抬起头, 看着身边这张可憎的脸. 眯起了双眼, 刀握得更紧了. 这是他胜利的表情. 黑衣人在这野兽的注视下显然无法保持镇静. 然而疯狂壮了他的胆. 黑衣人:"我知道你在想什么." 他当然不知道, 这是江湖人的坏习惯, 你越想知道什么, 就越要说你已经知道了. 萧无情面色没有丝毫变化. 冷峻得像一块石头, 会吃人的石头. 黑衣人的敌意明显被恐惧淹没了. 如果黑衣人知道张老实今天的事, 他可以预知自己的下场. 他已经像张老实一样, 后悔目睹这魔鬼刀客复仇的过程. 听说张老实后来吐了四天四宿, 他肯定不会好到哪里去. 萧无情已然发现这个外强中干的黑衣人根本不是张老实那样值得尊敬的对手, 他只是一个冲动的可悲的小丑. 忽然想起安慰自己刀下亡魂的话, 头疼的话, 砍掉就不疼了. /******/ 一阵寒光! 萧无情的刀出鞘了! 只有眼力最好的人才能看出他这一刀挥向哪里. 无情刀客终于出刀了, 而谁又能想到他这一刀竟挥向他自己! 头被砍下, 自然不会再疼, 其他地方, 其他毛病也一样. 萧无情身后一股黄色的疾风闪过. 随后是一声十分诡异的肃杀的啸声, 龙吟虎咆一般. 好快的刀! 萧无情从容地站起来, 用鞋底蹭了蹭刀上的血. 黑衣人竟流下了泪水. 因为在那出刀的一刻, 他薰出了香.